music recommendation system using machine learning research paper

Music Recommendation System Using Multiple Machine Learning Models

9 Pages Posted: 21 Nov 2023

Mudit Kumar Tyagi

Maharaja Agrasen Institute of Technology - Maharaja Agrasen Institute of Technology, Students

Muhammad Ali

Garvit kaim, tripti lamba.

Maharaja Agrasen Institute of Technology

Gunjan Chugh

Date Written: November 20, 2023

Music recommendation systems play a crucial role in helping users discover new music based on their preferences and interests. This paper presents a novel approach to music recommendation using three distinct models: topic-based, feature-based, and text-based. The topic-based model recommends music based on the thematic content of songs, while the feature-based model suggests similar tracks based on musical features. The text-based model leverages natural language processing techniques to recommend music based on user-entered text. Each model operates independently, providing diverse and comprehensive music recommendations. The proposed approach offers a user-friendly web interface and utilizes advanced algorithms such as bag-of-words, cosine similarity, and k-nearest neighbors for efficient and accurate recommendations. Experimental results demonstrate the effectiveness and versatility of the approach, offering users a personalized and engaging music discovery experience

Keywords: Music Recommendations, Topic-based recommendations, Feature-based recommendations, Text-based recommendations, K-Nearest Neighbors, Bag-of-words, Tensorflow, Nodejs, Expressjs, Child-Processes, web application Spotify-Web-Api, CountVectorizer, Cosine Similarity

Suggested Citation: Suggested Citation

Mudit Kumar Tyagi (Contact Author)

Maharaja agrasen institute of technology - maharaja agrasen institute of technology, students ( email ).

New Delhi India

Maharaja Agrasen Institute of Technology ( email )

New Delhi, 110086 India

Do you have a job opening that you would like to promote on SSRN?

Paper statistics, related ejournals, music psychology ejournal.

Subscribe to this free journal for more curated articles on this topic

International Conference on Innovative Computing & Communication (ICICC) 2024

Decision-making in computational design & technology ejournal.

Subscribe to this fee journal for more curated articles on this topic

Art & Science eJournal

Music Recommendation System Using Machine Learning

Chat with Paper

Music Similarity Measures: What's the use?

The million song dataset challenge, 6 – fine particles, thin films and exchange anisotropy (effects of finite dimensions and interfaces on the basic properties of ferromagnets), music recommendation from song sets., beyond timbral statistics: improving music classification using percussive patterns and bass lines, related papers (5), the effect of grade, experience, and listening condition on the melodic error detection of fifth- and sixth-grade woodwind students, leveraging multiviews of trust and similarity to enhance clustering-based recommender systems, an obfuscated attack detection approach for collaborative recommender systems, a concurrent recommender system based on social network., contextual sentiment based recommender system to provide recommendation in the electronic products domain, trending questions (1).

The paper does not provide information on how music recommendation systems affect consumers.

Search form

Enhanced music recommendation systems: a comparative study of content-based filtering and k-means clustering approaches.

OPEN ACCESS

In the dynamic landscape of digital music services, recommendation systems play a pivotal role, evolving in tandem with advances in artificial intelligence and machine learning. This research undertakes a comparative exploration of two distinct approaches to song recommendations: content-based filtering and K-means clustering. Drawing upon an extensive Spotify dataset encompassing diverse song attributes like genre, tempo, and key, the study meticulously evaluates the efficacy of personalized track recommendations. Content-based filtering tailors recommendations to users' established preferences by scrutinizing audio features such as Danceability, Energy, and Loudness. Conversely, the K-means clustering algorithm groups’ similar songs into clusters based on shared characteristics. The primary goal is to devise a music recommendation system that impeccably aligns with user preferences. The research evaluates the performance of the K-means clustering approach using the Silhouette index as a metric, revealing a recommendation accuracy exceeding 99%. Notably, data analysis underscores the superior performance of the content-based filtering technique. These findings hold substantial importance for refining personalized music recommendation systems, offering valuable insights into the effectiveness of different methodologies in catering to user-specific musical tastes. This study contributes to the ongoing evolution of digital music services, providing a foundation for future advancements in enhancing user experience through precise and tailored music recommendations.

K-means clustering, Content Based Filtering, music recommendation system, silhouette index

Human lives have always been inspired by music in some or other ways. The enormous growth of digital music services provides accessibility to huge library of songs than ever. Yet, many users may find it difficult to choose new music they would like due to many options for selections accessibility. A music recommendation system that suggests music as per the interest of the individual user may be useful in this situation. An intelligent system that employs data mining and machine learning techniques to propose songs or playlists based on a user's listening history, preferences, and behavior is called a music recommendation system. A music recommendation system seeks to enhance users' listening experiences by offering individualized and pertinent recommendations [1].

Systems for making music recommendations have many benefits. Users can find new songs and artists that they might not have otherwise known about, making for a more varied and pleasurable musical experience. It can boost user retention and engagement for music streaming services, which will ultimately spur revenue development. Developing an effective music recommendation system can be challenging due to the two basic factors i.e., Data Sparsity and Cold Start issues. To make recommendations, music recommendation algorithms look to user reviews and listening patterns. It might be challenging to precisely identify user preferences due to the data sparsity, that may be available for any given user. There isn't much information about a user's tastes or listening history when they first sign up for a music streaming service. Because of this cold start problems, it could be challenging to make reliable music recommendations until enough information is gathered. Real-world music streaming services like Spotify, Apple Music, and Prime Music frequently employ recommendation algorithms for music. They are also useful in other fields, such as entertainment, where they can suggest songs for films, TV shows, and advertisements, and in retail, where they can provide background music that fits the atmosphere of a space. Artificial intelligence and machine learning play a crucial role in music recommendation systems. They let the system to evaluate enormous volumes of data, spot trends, and take into account user behavior to generate precise and individualized recommendations. Music recommendation systems can deliver more precise and varied recommendations than conventional rule-based systems by utilizing these technologies.

In connection with this research, two separate music recommendation systems were built using K-Means clustering and content-based filtering methodology. K-Means clustering, an unsupervised machine learning technique, facilitated the grouping of similar tracks based on their shared acoustic characteristics. Content-based filtering, on the other hand, works by recommending songs based on the similarity of their characteristic profiles to the profiles of songs that the user has used in the past. The development of music recommendation systems has been characterized by systematic and careful development involving steps such as data collection, pre-processing, feature derivation, model training and evaluation. This study investigates two independent strategies, content-based filtering and K-Means clustering, to address shortcomings in existing recommendation systems, particularly in settings with sparse data and new user instances. The study's goal is to improve the accuracy and customer happiness of music suggestions. The key contributions include a detailed examination and comparison of the effectiveness of content-based filtering and K-Means clustering on the Spotify dataset. The study intends to highlight the strengths and limits of each strategy within the context of a wide range of musical preferences and genres. This comparison study provides useful insights for optimizing recommendation systems with the goal of improving the user experience in music choosing. The evaluation of these systems covers a wide range of parameters, with a focus on their adaptation to user-specific requirements and scalability in the face of growing music collections. This main objective of this study is to provide the basis for more sophisticated and user-centric methods to music selection and recommendation.

The rest of the paper is structured as follows: The existing methods and Machine Learning approaches are discussed in Section 2. The proposed methodology is detailed in Section 3. Sections 4 presents the results based on the Music Recommendations system using K-Means clustering and Content Based Filtering. Whereas Discussion provided in Section 5. Finally, Section 6 presents the conclusion.

The influence of music on children's and young people's intellectual, social, and personal development has come to light in this research [2]. It has been discovered that actively listening music can improve knowledge in other subjects, including arithmetic, language, and motor skills. This transfer of abilities is greatly aided by the brain's ability to self-organize in response to various musical activities. Some abilities may transfer naturally without conscious thought, while others required for conscious thought. This new knowledge has implications for using music as a tool to improve abilities in other fields. Engagement, drive, and enjoyment can all be raised by incorporating music into learning and therapy. People with cognitive impairments or learning difficulties may benefit the most from using music as a tool to improve skills. Designing interventions that focus on particular cognitive processes can be influenced by knowledge of how the brain organizes itself in response to musical activity. The ability to transfer skills between domains can result in more comprehensive learning and enhanced cognitive functioning. These results imply that music can help a wide spectrum of learners and has enormous promise as a tool for improving learning outcomes.

In recent years, the use of machine learning algorithms for music recommendation has grown in popularity. The limitations of music recommendation, such as the lack of user-item interactions and the subjectivity of musical taste, have been studied using a variety of strategies. In order to increase suggestion accuracy, some studies [3] have concentrated on creating hybrid recommendation systems that mix collaborative filtering and content-based filtering techniques. Others have looked towards modelling user-item interactions and capturing intricate patterns in music taste using deep learning approaches like neural networks and autoencoders. The use of reinforcement learning has also been researched as a means to improve user satisfaction and optimize the recommendation process. There are still issues that need to be resolved in the subject of music recommendation, despite the advancements made. Some of the problems that need more research are the lack of diversity in recommended things, the cold start issue, and the challenge of modelling user preferences over time. In addition, privacy and prejudice issues related to music recommendation algorithms need to be properly explored and handled. Overall, the application of machine learning algorithms for music selection is quite promising, but more study is required to fully realize its potential and assure its responsible application.

Several existing systems have been created in the field of music recommendation systems to forecast song preferences based on a user's playlist, taking into consideration their likes and dislikes. One such system [4] offers a graphical user interface for users to input attribute details and forecast music preference using machine learning methods, such as decision trees, random forests, and logistic regression. The best algorithm for song prediction has been chosen after these algorithms have been tested using metrics like MAE, MSE, RMSE, and R-squared error. However, it is crucial to take the musical genre into account in order to improve the quality of song selections. The convolutional recurrent neural network-based recommender system makes use of song attributes to suggest songs and detects plagiarism by creating similarity scores for suggested songs.

Gunawan and Suhartono [5] have demonstrated that content-based techniques that take into account a user's perceptual similarity to previously heard music might enhance music recommendation systems. This study uses convolutional recurrent neural networks (CRNNs) for feature extraction and similarity distance to search for similarities between features in order to compare the similarity of features on audio signals. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are combined to form CRNNs, have been found to be effective at predicting complex musical elements like chords and beats. CNNs that mix the frequency and time domains for music genre classification are not as accurate as CRNNs, according to earlier research.

Gunawan and Suhartono [5] also discovered that feature extraction with CNNs and audio representation with Mel-spectrograms is particularly efficient for automatic tagging. Therefore, this study uses Mel-spectrograms to describe audio, CRNNs to extract features, and recommends music based on how similar the features are. In conclusion, this study builds on the earlier work of Choi et al. by employing a content-based music recommendation system that makes use of CRNNs for feature extraction, similarity distance to search for similarities between features, and Mel-spectrograms for audio representation. This method might increase the precision and potency of music recommendation systems.

Systems for categorizing and recommending music are crucial parts of the contemporary music industry since they make it easier to find and explore new music. Manual feature extraction and rule-based systems were used in conventional methods of music classification and recommendation. Deep learning, however, has caused the discipline to move towards more automated methods. In particular, collaborative filtering methods have been utilized in recommendation systems while convolutional neural networks (CNNs) have been found to be useful in classifying music genres. Novel deep learning-based methods have been proposed in recent works to increase the precision of music genre classification and recommendation.

Table 1. Summary of research papers


Hallam [2]	Music’s influence on intellectual, social, and personal development	Improved knowledge in arithmetic, language, motor skills	Music can enhance cognitive skills in other subjects and aid brain self-organization. Music use can benefit cognitive impaired learners
Mendjel et al. [3]	SVM, KNN, Decision Tree	Accuracy	Highlight the importance of considering music genre information, song features, and lyrics-based mood prediction for improving the quality
Gunawan and Suhartono [5]	Convolutional recurrent neural networks (CRNN)	Mel-spectrograms	Efficient automatic tagging and music recommendation
Elbir and Aydin [6]	MusicRecNet model	Accuracy	Improved music genre classification and recommendation.
Karpati et al. [7]	Music Recommendation using User’s Sentiments	Remote subjective tests	The proposed sentiment intensity metric, eSM, improved the music recommendation system
Chen and Chen [8]	Decision trees, random forests, logistic regress	MAE, MSE, RMSE, R-squared error	Use of graphical user interface for users to input attribute details and forecast music preference using machine-learning methods.

Elbir and Aydin [6] presented the MusicRecNet model in this regard, which employs a CNN with dropout layers for music genre classification and recommendation. According to their findings, MusicRecNet performs better than earlier research in terms of musical similarity and recommendation, yet it is possible for genres like jazz and classical to be misclassified or improperly recommended [7]. To enhance the existing outcomes, the authors advise using large data processing strategies and more thorough deep neural network models. Chen and Chen [8] also presents low perceived impacts on the analysis of energy consumption, network and latency in accordance with the processing and memory perception of the recommendation system. Rosa et al. [9] proposes a music recommendation system based on user's sentiments extracted from social networks. The system uses a lexicon-based sentiment metric and a correction factor based on the user's profile to adjust the final sentiment intensity. The system was evaluated with remote users using the crowdsourcing method, reaching a rating of 91% of user satisfaction.

The current literature goes deeply into the subject of digital music recommendation, demonstrating several techniques with promise but also revealing unique limits. Notably, while content-based filtering is efficient at capturing user preferences, it falls short of managing new or unusual tunes, a critical gap in the ever-shifting environment of musical trends. K-Means clustering, on the other hand, shows potential in grouping music with similar characteristics but struggles to personalize suggestions to unique user preferences. This study is based on these previous efforts and aims to contribute to the area by overcoming the stated shortcomings. The goal is to give improved solutions for customization and precision in music recommendation systems by thoroughly investigating both content-based filtering and K-Means clustering using an extensive spotify dataset [10]. Two methods of content-based filtering and K-Means clustering techniques are used to provide song recommendations [11]. A comparative evaluation of the results of both methods was done and the factors that contributed to the different recommendations for each model were investigated [12]. The reasons behind the deviant recommendations were investigated. In addition, K-Means clustering performance was evaluated using the Silhouette Index. This index explained the special characteristics of the clusters formed by the K-Means Clustering technique and thus provided an overview of the performance of the aforementioned technique. The summary of research papers is provided in Table 1.

To gain a comprehensive comprehension of the music library, an accumulation of data encompassing metadata pertaining to songs, encompassing aspects like genre, artist, and album, was initiated. Subsequent to data collection, a preprocessing phase was undertaken, entailing the cleansing and systematic organization of data. This involved the elimination of duplicates, rectification of errors, and formatting the data to render it amenable for analytical pursuits [13]. Feature engineering constituted the subsequent phase, wherein pertinent attributes were distilled from the amassed data. This endeavor encompassed the determination of salient attributes characterizing a song, such as its genre, tempo, and key. Following the completion of the feature engineering stage, K-Means clustering and content-based filtering techniques were employed to discern patterns inherent in the identified attributes. The efficacy of the K-Means algorithm was gauged via the utilization of the Silhouette score. The comprehensive depiction of the Research Methodology can be observed in Figure 1, encompassing the entire workflow. Figure 2 (a) and Figure 2 (b) represent the working of the Content Based Filtering and K-Means Clustering techniques respectively. Content-based filtering is a method of recommending comparable items by analyzing the properties and characteristics of objects (in this case, music tracks). It focuses on the content of the things and attempts to detect similarities based on criteria such as audio characteristics, genre, mood, and so on. It is a personalized recommendation approach that suggests similar things based on the user's previous preferences [14]. K-means clustering, on the other hand, is an unsupervised machine learning approach used for data grouping and segmentation. In the context of music recommendation, k-means clustering divides music songs into clusters based on feature similarities but does not take user preferences into account. It seeks to group things with similar properties together, which can help with pattern discovery and data organization.

music recommendation system using machine learning research paper

Figure 1. Architecture diagram of the methodology

Table 2. Description of dataset attributes


track id	A unique identifier for each track within the Spotify ecosystem.
artists	The names of the artists who performed the track, separated by semicolons in case of multiple artists.
album name	The name of the album in which the track appears.
track name	The title of the track.
popularity	A measure of how popular a track is, with higher values indicating greater popularity.
duration ms	The length of the track in milliseconds.
explicit	A binary indicator of whether the track contains explicit lyrics.
danceability	A measure of how suitable a track is for dancing, based on factors such as tempo, rhythm stability, beat strength, and overall regularity.
energy	A measure of the intensity and activity of a track, with higher values indicating more energetic tracks.
key	An integer value that maps to a pitch using standard Pitch Class notation, indicating the key in which the track is played.
loudness	A measure of the overall loudness of a track in decibels (dB).
mode	A binary indicator of whether the track is played in a major or minor scale.
speechiness	A measure of the presence of spoken words in a track.
acousticness	A measure of how acoustic a track is, with higher values indicating greater acousticness.
instrumentalness	A measure of the likelihood that a track contains no vocals.
liveness	A measure of the presence of an audience in the recording, with higher values indicating a higher likelihood of a live performance.
valence	A measure of the positive or negative emotional tone conveyed by a track, with higher values indicating more positive emotions.
tempo	A measure of the overall estimated tempo of a track in beats per minute (BPM).
time signature	An estimated time signature, indicating the number of beats in each bar or measure.
track genre	The genre in which the track belongs.

(a) Recommendation Using Content Based Filtering

(b) Recommendation Using K-Means Clustering

Figure 2. Recommendation algorithm of the two methods

3.1 Data collection

The initial phase of this research focused on combining information related to a music recommendation system. A dataset called Spotify Tracks Dataset was obtained from Kaggle with special help from MaharshiPandya (owner). This dataset contains a diverse set of over 125 genres, with each track associated with its associated sound attributes. Conveniently stored in CSV format, the dataset allowed for seamless data download and analysis. This dataset contained a significant set of 114,000 items. This painstaking process of combining data produced an important archive of knowledge about the sound characteristics of different pieces of music. This repository is the basis for later steps, especially when training machine learning algorithms adapted to music recommendations. Careful acquisition of a high-quality dataset is a key aspect of any research project that supports the reliability of the results. Using Kaggle enabled the targeted acquisition of extensive and diverse material, which strengthens the prospects of success of the research [15].

3.2 Understanding the dataset

The Spotify Tracks Dataset, obtained from Kaggle, contains data on 114,000 tracks across 125 different genres. Each track is associated with several audio features that have been provided in a tabular format, which can be easily loaded into analytical tools [16]. The columns present in the dataset are represented in Table 2.

3.3 Data cleaning

Data cleaning constitutes a pivotal phase within the data analysis process. The initial stride in this purview involves the elimination of null or missing values present within the dataset. Such values have the potential to distort the analytical outcomes and introduce errors. Subsequently, extraneous columns devoid of substantive contribution to the analysis are pruned [17]. These columns might encompass data-absent entries or information of marginal relevance. In the course of this project, the 'Unnamed' column was eliminated. Moreover, the identification and elimination of duplicate tracks were undertaken based on the attributes 'album_name' and 'track_name'. Duplicates bear the potential to disrupt analysis and engender erroneous findings. Their eradication guarantees a singular analysis per track. In scenarios involving multiple artists for a given song, the artists' names were systematically segregated through a comma delimiter. This stratagem ensures the discrete analysis of each artist, thereby averting ambiguities and analytical inaccuracies. It is impossible to emphasize the significance of the data cleaning stage. Making certain the data is reliable and accurate is essential for making wise decisions. Inadequate data cleaning can result in erroneous findings and bad decision-making [18, 19]. As a result, data cleaning is an essential stage in the data analysis process to guarantee that the outcomes are precise and trustworthy.

WCSS $=\sum_{i=1}^k \sum_{x \in c_i}\left|x-\mu_i\right|^2$ (1)

3.4 Recommendation using K-Means clustering

A prominent unsupervised machine learning approach used to cluster comparable data points according to their attributes is called K-Means clustering. It uses a centroid-based clustering technique, in which the algorithm seeks to reduce the distance between each cluster's points and the centroid. The number of clusters is determined before running the algorithm, which makes this approach well suited for a system that focuses on grouping songs into clusters with similar audio characteristics. In Eq. (1), WCSS represents the within Cluster Sum of Squares, K is the number of clusters, C i is the i-th cluster, x represents data points Within cluster C i and µ i is the centroid of cluster C i. The sum of squared distances between each data point and its respective cluster centroid is calculated by the formula used in Eq. (1).

In k-means clustering, the Within Cluster Sum of Squares (WCSS) is used as a measure of how well the data points within each cluster are grouped around their respective cluster centroids. The main goal of k-means clustering is to find cluster centroids in such a way that the WCSS is minimized. Lower WCSS indicates that the data points within each cluster are closer to their centroid, implying better clustering [20, 21].

The optimal number of clusters is ascertained through the application of the elbow method, entailing the plotting of the within-cluster sum of squares (WCSS) vis-à-vis the number of clusters. The WCSS is the sum of the squared distance between each point and its cluster centroid. Figure 3 usually forms an elbow-like shape, and the optimal number of clusters is where the decrease in WCSS starts to level off. In this case, the optimal value of K was found to be 4. A critical aspect of the study was the K-Means clustering algorithm, which was carefully designed to assure optimal grouping of music recordings from the Spotify dataset. The Elbow Method was used to determine the optimal number of clusters. The explained variation is plotted versus the number of clusters in this approach, and the elbow of the curve is chosen as the best cluster count. This strategy assists in balancing the maximum explained variance and the model's complexity [22, 23].

Figure 3. Elbow method to find optimal clusters

$d=\sqrt{\left(x_2-x_1\right)^2+\left(y_2-y_1\right)^2+\left(z_2-z_1\right)^2}$ (2)

In terms of distance measurements, Euclidean distance was chosen as the preferred option. This selection is based on its efficacy in determining dissimilarity between data points in the feature space. The Eq. (2) used in this study computes the direct distance between any two locations in a three-dimensional space, taking into consideration their positions along the x, y, and z axes. It is used in this study for music recordings, where each axis (x, y, z) refers to various qualities or attributes such as pace, loudness, or danceability. A two-pronged technique was implemented to address the difficulty provided by outliers, which can drastically skew K-Means clustering findings. To begin, the dataset was preprocessed using z-score normalization to reduce the influence of extreme values. Second, a post-clustering study was performed to identify and assess the impact of probable outliers. Clusters with a high number of outliers were investigated further, and various handling procedures were examined when outliers had a discernable pattern. This extensive method to K-Means clustering was critical in effectively segmenting songs into meaningful clusters, laying the groundwork for the comparative analysis using content-based filtering [24].

Figure 4. Visualization of Clusters using PCA

The data is then fitted into a K-Means cluster model using the Pipeline module provided by scikit-learn. This pipeline begins by scaling the data using a standard scaler, ensuring that features are weighted uniformly. After that, the K-Means clustering algorithm is used with a predetermined number of clusters of 4. After clustering, the corresponding cluster identifiers are predicted for each story and then added as a new column to the original data frame. PCA method is used to reduce the dimensionality of the data to facilitate the visualization of clusters. The data points are then plotted in 2D space. Figure 4 is the representation of different clusters of songs obtained by applying the K-Means clustering technique and using PCA for visualization of the clusters. The PCA pipeline also includes the StandardScaler to ensure that the feature scaling is consistent across the entire process. The information is shown on a graph, using different colors to show different groups of data. This helps to easily identify different groups and their related songs. The K-Means clustering is an efficient and effective algorithm for grouping songs into similar clusters based on their audio features. The elbow method and PCA visualization techniques are useful tools for determining the optimal number of clusters and visualizing the results. The resulting clusters can be used for further analysis and recommendations, such as suggesting similar songs or playlists to users [25, 26].

3.5 Recommendation using Content Based Filtering

Content-Based Filtering is a technique used in recommender systems that recommends items to a user based on the features of the items that they have interacted with previously. In content-based filtering, the features of the items are analyzed and a similarity score is computed to find similar items. The music recommendation system uses content-based filtering to recommend songs to users based on the characteristics of songs they have interacted with in the past. The similarity score between two items is calculated using their feature vectors in content-based filtering. Each item is represented by a set of features, and the similarity score measures how related or similar two objects are based on how similar their feature values are to one another [27].

The similarity between the items is determined by the cosine of the angle formed by two non-zero vectors in an n-dimensional space, which is measured by the cosine similarity. Given two items, denoted as A and B, their feature vectors can be expressed as follows:

A= (a 1 , a 2 , ..., a n ), B= (b 1 , b 2 , ..., b n )

The Cosine Similarity C sim between A and B is calculated as

$C_{s i m}(A, B)=\frac{a_1 \cdot b_1+a_2 \mid b_2+\ldots+a_n \cdot b_n}{\sqrt{a_1^2+a_2^2+\ldots+a_n^2} \cdot \sqrt{b_1^2+b_2^2+\ldots+b_n^2}}$ (3)

The numerator of Eq. (3) computes the dot product of the two vectors, which measures the similarity of the direction of the vectors. The denominator represents the normalization factor, which scales the similarity score to the range [-1, 1]. A cosine similarity of 1 indicates that the items have the same feature values and are identical, while a score close to 0 indicates low similarity, and a score close to -1 indicates high dissimilarity between the items. In preparing the data for content-based filtering, a subset of columns is chosen from the preprocessed data frame. These columns include important characteristics of the recommendation system such as danceability, energy, valence, talkability, instrumentality, acoustics, and popularity. Subsequently, the subset of columns is normalized by row, facilitating uniformity in feature value ranges and enabling effective comparison among different songs' features [28, 29]. The resulting normalized data frame as depicted in Table 3 has the song ID as its index and the feature columns as its columns. The content-based filtering system is implemented using a function. The function takes two inputs: the name of a song and the number of recommendations to generate. It first identifies the song id corresponding to the input song name [30, 31]. It then computes the cosine distance between the normalized feature vector of the input song and the feature vectors of all other songs in the data frame. The cosine distance is a similarity metric that ranges from 0 (dissimilar) to 1 (identical) and measures the cosine of the angle between two feature vectors. The function then sorts the computed distances in ascending order and selects the top N songs with the lowest distance values. These top N songs are then merged with the original data frame to obtain additional information about the recommended songs such as the song name, artist, and album name [32, 33]. The function then returns a data frame containing the recommended songs with their corresponding information. The content-based filtering component of the music recommendation system uses the characteristics of songs previously heard by the user to recommend similar songs that are expected to match the user's preferences. By using a similarity metric to identify songs that are most similar to the user's preferred songs, the content-based filtering component provides a personalized and relevant set of song recommendations [32, 33].

Table 3. Sample of normalized data


0	0.009259	0.006314	0.009793	0.001959	0.000441	0.999887
1	0.007635	0.003018	0.004854	0.001387	0.016797	0.999812
2	0.007684	0.006298	0.002105	0.000977	0.003684	0.999941
3	0.003746	0.000839	0.002014	0.000511	0.012745	0.999909
4	0.007536	0.005402	0.002036	0.000641	0.005719	0.999938

4.1 Feature analysis

(a) Most popular artists

(b) Most lively artists

(d) Most lively songs

Figure 5. Comparative analysis of different features

Data Analysis (DA) is the process of examining, visualizing, and summarizing a dataset to better understand its characteristics, patterns, and relationships between variables. Finding intriguing ideas, spotting abnormalities, and creating hypotheses that can direct additional analysis are the main objectives of Exploratory Data Analysis [34, 35]. Because it lays the groundwork for further data analysis, modelling, and interpretation, Exploratory Data Analysis is crucial. The purpose of exploratory data analysis (EDA) is to assess compliance with data analysis requirements, identify data quality problems, detect outliers, and examine the distribution of data for possible specific trends. This project uses EDA to gain insight into the properties of a data set that includes various properties of parts. The main focus is on assessing the popularity of stories in different population groups and identifying potential emerging trends. The aim is to use the EDA results to identify the most popular songs, measure their relative popularity and gain valuable insights for possible research directions. The top 15 artists are represented on Figure 5 along with the number of times their music has been played. George Jones, who has 259 songs on the list, is the most popular musician, followed by my tiny airport, who has 171 tracks. The Beatles, BTS, and Glee Cast are also popular artists with more than 100 songs. Hank Williams, Håkan Hellström, Linkin Park, Scooter, CoCo melon, Ella Fitzgerald, Arctic Monkeys, OneRepublic, Dean Martin, and Charlie Brown Jr. are also in the list. The top 15 longest songs are presented along with their track names and duration in milliseconds (ms).

The songs' running times range from 3,876,276 to 5,237,295 milliseconds. "Unity (Voyage Mix) Pt. 1" has the longest song duration at 5,237,295 milliseconds. Figure 5 represents the graph of the longest music tracks. Based on their liveliness score, that represents the list of artist who are the most animated. An artist's liveliness rating is a gauge of how vivacious and dynamic their songs are. This shows important trends, presenting George Jones as the most dynamically attractive artist, followed by My Little Airport, The Beatles, BTS, Glee Cast, Hank Williams, Håkan Hellström, Linkin Park, Scooter, CoComelon, Ella Fitzgerald, Arctic Monkeys, OneRepublic, Dean Martin and Charlie Brown Jr. The significant overlap between the 15 most popular artists and the most active artists highlights a remarkable relationship between popularity and vitality characteristics in this context [36, 37].

Figure 6. Most popular genre

Figure 7. Correlation matrix of several features

The list of the liveliest Christmas songs suggests that the classic tunes continue to be popular even in modern times. "Rockin' Around the Christmas Tree," "Frosty the Snowman," and "Let It Snow! Let It Snow! Let It Snow!" are among the all-time favorites, while newer versions like "Little Saint Nick-1991 Remix" and "Mistletoe" also make an appearance. The enduring popularity of these festive tunes can be attributed to their catchy melodies, upbeat rhythms, and nostalgic feel that evokes happy memories of the holiday season. The graph in Figure 6, shows the distribution of most popular genres. The graph shows that the most popular genres are K-Pop and Pop-Film, followed by Metal and Chill. It is noteworthy that the Indian genre secures the eighth place in the hierarchy of popular genres. The correlation shows in Figure 7, the correlation coefficients between different variables. The chart shows an observable positive correlation linking popularity to characteristics such as loudness, Danceability, energy and tempo. This indicates that more popular songs are usually louder, more danceable, energetic, and faster in tempo. On the other hand, there is a negative correlation between popularity and Instrumentalness, liveness, and valence [38]. This indicates that less popular songs are usually more instrumental, less lively, and less positive in terms of emotional content.

Silhouette Index $=\frac{1}{N} \sum_{i=1}^N\left(\frac{b(i)-a(i)}{\max a(i), b(i)}\right)$ (4)

4.2 Performance evaluation of K-Means clustering using silhouette score

Figure 8. Silhouette score for different values of K

The Silhouette index was used to measure the efficiency of the K-Means clustering algorithm. This index quantifies the similarity of an object to its cluster compared to other clusters, called "silhouette points" Eq. (4) represents the formula for calculating the Silhouette Index values where, N is the total number of data points or samples, a (i) is the average distance of the data point i to all other data points within the same cluster and b (i) is the average distance of the data point i to all data points in the nearest neighboring cluster. The Silhouette Index is used to evaluate the quality of clustering results. A score of 1 indicates that the object is well-matched to its own cluster and poorly matched to surrounding clusters, whereas a value of -1 suggests the opposite. The score goes from -1 to 1. A score of 0 means that the object's similarities to its own cluster and its nearby clusters are equal. Typically, a Silhouette score above 0.5 is considered to be a good score. In this instance, a Silhouette score of 0.62 was achieved for the 3-cluster configuration of K-Means, while a score of 0.52 was obtained for the 4-cluster configuration of K-Means. While the score for 3 clusters is higher than that of 4 clusters, it is important to consider other factors such as the interpretability of the clusters and their usefulness for the task at hand. To conduct a more in-depth assessment of the K-Means algorithm's performance, the range of clusters was adjusted from 2 to 16, and the associated Silhouette scores were plotted in Figure 8. This helps to identify the optimal number of clusters for the given dataset. In this case, the graph shows that Silhouette's score peaked at about 3 clusters, followed by a gradual decline. This shows that the optimal number of clusters for the dataset is 3. The Silhouette score provides a useful measure of the quality of the clusters obtained by the K-Means algorithm. It helps to evaluate the performance of the algorithm and determine the optimal number of clusters for a given dataset.

4.3 Result comparison from K-Means and content based techniques

The result of the song recommendation received by both the techniques has been compared with the track "Burn It Down". Based on the data in Table 4, it can be concluded that content-based filtering focuses mainly on the study of song-specific features such as acoustics, danceability, energy and valence. The purpose of this analysis is to identify songs with comparable musical characteristics. When applied to the song "BURN IT DOWN" by Linkin Park, this method successfully recommended songs that share a similar music style, like "ZOMBIFIED" by Falling in Reverse, "Life is a Highway" by Rascal Flatts, and "Dragula" by Rob Zombie. The advantage of content-based filtering is that it leverages the specific attributes of a song, making its recommendations more tailored to the user's musical preferences. On the other hand, the K-means clustering approach groups songs together based on their audio features, aiming to identify patterns and similarities within the dataset. This method recommended songs like "The Last Stand" by Sabaton, "My Head & My Heart" by Ava Max, and "No One Like You" by Scorpions. While these songs might not share the same music style as "BURN IT DOWN," they are clustered together because they exhibit similar audio characteristics. K-means clustering provides a broader perspective, categorizing songs into clusters based on their feature similarity, which can be valuable for discovering hidden patterns in the data.

Table 4. Recommendation using content-based filtering and k-means clustering for the song "burn it down" by linkin park


ZOMBIFIED, Falling in Reverse	The Last Stand, Sabaton
Life is a Highway, Rascal Flatts	My Head & My Heart, Ava Max
Dragula, Rob Zombie	No One Like You, Scorpions
My Songs Know What You Did in The Dark, Fall Out Boy	Black Catcher, Vickeblanka
Run, BTS Fireside,	Arctic Monkeys
You’ve Got Another Thing Coming, Judas Priest	Kryptonite, 3 Doors Down

To validate the outcomes of the comparative study of content-based filtering and K-Means clustering, robust statistical significance testing was used. A paired t-test was used to see if the differences in performance parameters between the two techniques were statistically significant. The results showed that content-based filtering outperformed K-Means clustering (with p 0.05), indicating a significant difference in their efficacy for music suggestion. Further research was carried out to determine the causes for the improved performance of content-based filtering. A crucial component discovered was its ability to leverage unique song attributes more efficiently, matching suggestions closely with individual user tastes. In comparison to K-Means clustering, this method's sophisticated approach to assessing commonalities between tracks appears to resonate more with consumers' different interests. The efficiency with which K-Means clustering categorizes music into various groups based on their attributes contrasts with its limited ability to capture the delicate subtleties of user preferences. This drawback may be linked to the method's inherent concentration on broad similarities between songs, which may result in generalized suggestions that may not completely fit with specific user preferences. The value of customization in music recommendation systems is shown by the critical interpretation of these findings. When compared to the more universal technique of K-Means clustering, the flexibility of content-based filtering to the particular tastes of each user provides a more fulfilling user experience. These findings not only validate the recommended strategy, but also pave the way for future research targeted at improving customization and accuracy in music recommendation systems.

The development of efficient recommendation systems has been largely facilitated by artificial intelligence and machine learning techniques. Music recommendation systems have grown to be a significant component of the digital music ecosystem. This study considered two different approaches for recommendation tracking: content-based filtering and K-Means clustering. Applying content-based filtering, relevant song attributes were extracted from the Spotify Tracks dataset, including acoustics, danceability, energy, popularity, liveliness, and valence, among others. Similarly, these features were used to facilitate grouping of similar songs using the K-Means grouping technique. When evaluating the effectiveness of both strategies, it was found that the content-based filtering method was better in terms of recommendation accuracy. This may be due to the fact that the content-based approach relies on internal song features to make recommendations, which can contribute to more reliable results compared to grouping similar songs based only on characteristics such as danceability, energy and popularity. Future research may choose to combine the two methods by grouping comparable songs together using K-Means Clustering, and then making suggestions within those categories using content-based filtering. By identifying both the characteristics of each music and their similarities, this could result in more accurate recommendations and user experience. Future intelligent music recommendation systems are anticipated to be driven by developments in artificial intelligence and machine learning.

The study's higher performance of content-based filtering may be ascribed primarily to the special properties of the Spotify dataset and the rigorous feature engineering that was performed. The Spotify dataset, which was rich in various and nuanced song attributes, served as an excellent foundation for content-based filtering. This technique works best in settings with comprehensive and multiple qualities since it depends largely on the granularity and accuracy of these variables to provide suggestions. The technique placed a strong focus on feature engineering, which entailed carefully choosing, analyzing, and manipulating song elements to improve their value in the recommendation algorithm. For example, the addition of sophisticated audio elements such as tempo, key, and danceability, as well as genre and artist information, allowed for a more in-depth knowledge of each tune. This in-depth analysis of songs allowed content-based filtering to produce more nuanced and personalized suggestions that matched the interests of individual users.

Furthermore, in this dataset, where the diversity and breadth of musical elements were vast, the content-based filtering approach of comparing songs based on their intrinsic properties proved to be more successful. In contrast, K-Means clustering, while useful for grouping related music, lacks the accuracy to fully use these specific qualities. This disparity in the use of song attributes explains why content-based filtering emerged as the more successful recommendation strategy in the study. The research' conclusions emphasize the importance of precise feature engineering and the compatibility of recommendation systems to the nature of the dataset. This comprehension not only sheds light on the success of content-based filtering in this example, but it also gives useful insights for future study and development in music recommendation systems, particularly in contexts with rich and diverse datasets.

The paper contributes significantly to music recommendation systems by doing a thorough investigation of content-based filtering and K-Means clustering using the Spotify dataset. It emphasizes the superiority of content-based filtering in terms of suggestion accuracy and user happiness, which is validated by extensive statistical testing and meticulous feature engineering. The repercussions are far-reaching. The research presents advice for creating more successful, user-centric solutions for professionals in music streaming and recommendation. The advantage of content-based filtering in managing comprehensive datasets highlights its potential for increasing user engagement on platforms with rich musical material. The approaches used in this study are relevant beyond music streaming. They may be applied to areas like as movie streaming, e-commerce, and social media content curation, providing common principles for enhancing suggestion quality and relevance. A cornerstone of personalised music recommendation, content-based filtering, zealously tailors song suggestions to resonate with users' stylistic inclinations, meticulously emphasising the mirroring of musical properties between target and recommended songs. In sharp contrast, the data-driven technique of the K-Means Clustering approach orchestrates a comprehensive symphony of song groupings based on shared audio attributes. This methodology extends an invitation to identify new, unexplored patterns and insights hidden inside the musical information, perhaps stimulating chance discoveries. Adoption of either strategy is dependent on the end user's preference, who must choose between the attractiveness of bespoke, style-driven recommendations and the fascination of uncovering hidden relationships through data-driven clustering. Significantly, this study lays the groundwork for future research, calling the growth of existing algorithms and the birth of novel hybrid techniques, ultimately taking the arena of user-centric music recommendation to unparalleled heights.

Looking forward, the domain of music recommendation systems presents extensive opportunities for exploration and innovation. The subsequent points delineate particular areas for future research, extending from the discoveries of the present study: User Data Integration: Future research may look at combining supplemental user data, such as listening history, user ratings, and behavioral patterns, into recommendation systems. This might lead to a better understanding of user preferences, perhaps improving suggestion accuracy and customisation. Evaluating Distinct or creative Song qualities: Appraising distinct or creative song qualities is another area for future research. Examining the impact of less often used elements, such as lyrical content or cultural influences, might provide new insights into user preferences and suggestion efficacy. Hybrid Recommendation Systems: There is also opportunity to design hybrid systems that combine the advantages of content-based filtering with other recommendation techniques, such as collaborative filtering or deep learning models. This might overcome some of the limitations of using a single approach while also presenting a more resilient recommendation system. Cross-Domain Recommendations: Investigating the use of music recommendation approaches in other domains, such as movies or literature, might provide significant insights into the systems' transferability and adaptability.

While this study gives useful insights into music recommendation systems, it does have certain shortcomings that should be addressed in future research: Limitations of the dataset: While the Spotify dataset is broad, it may not fully represent worldwide music preferences. Future research should look at more diversified datasets that represent a broader spectrum of musical genres and cultural backgrounds. Algorithmic Bias: The study admits the possibility of bias in the recommendation systems used. To guarantee fair and varied suggestions, future research should focus on addressing and minimizing these biases. User contact and Feedback: In assessing recommendation systems, the study lacked direct user contact or feedback. Future study might improve accuracy by including user feedback loops to analyze suggestion quality and user satisfaction more thoroughly. These next work ideas and considerations attempt to improve on the current study's findings, correcting its shortcomings and broadening the field of research in music recommendation systems.

[1] Zhu, X., Shi, Y.Y., Kim, H.G., Eom, K.W. (2006). An integrated music recommendation system. IEEE Transactions on Consumer Electronics, 52(3): 917-925. https://doi.org/10.1109/TCE.2006.1706489 [2] Hallam, S. (2010). The power of music: Its impact on the intellectual, social and personal development of children and young people. International Journal of Music Education, 28(3): 269-289. https://doi.org/10.1177/0255761410370658 [3] Mendjel, M.S.M., Ghazi, S., Dib, A., Seridi, H. (2023). A new audio approach based on user preferences analysis to enhance music recommendations. Revue d'Intelligence Artificielle, 37(5): 1341-1349. https://doi.org/10.18280/ria.370527 [4] Verma, V., Marathe, N., Sanghavi, P., Nitnaware, P. (2021). Music recommendation system using machine learning. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 7(6): 80-88. https://doi.org/10.32628/CSEIT217615 [5] Gunawan, A.A., Suhartono, D. (2019). Music recommender system based on genre using convolutional recurrent neural networks. Procedia Computer Science, 157: 99-109. https://doi.org/10.1016/j.procs.2019.08.146 [6] Elbir, A., Aydin, N. (2020). Music genre classification and music recommendation by using deep learning. Electronics Letters, 56(12): 627-629. https://doi.org/10.1049/el.2019.4202 [7] Karpati, F.J., Giacosa, C., Foster, N.E., Penhune, V.B., Hyde, K.L. (2017). Dance and music share gray matter structural correlates. Brain Research, 1657: 62-73. https://doi.org/10.1016/j.brainres.2016.11.029 [8] Chen, H.C., Chen, A.L. (2001). A music recommendation system based on music data grouping and user interests. In Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 231-238. https://doi.org/10.1145/502585.502625 [9] Rosa, R.L., Rodriguez, D.Z., Bressan, G. (2015). Music recommendation system based on user's sentiments extracted from social networks. IEEE Transactions on Consumer Electronics, 61(3): 359-367. https://doi.org/10.1109/TCE.2015.7298296 [10] Ludewig, M., Kamehkhosh, I., Landia, N., Jannach, D. (2018). Effective nearest-neighbor music recommendations. In Proceedings of the ACM Recommender Systems Challenge 2018, pp. 1-6. https://doi.org/10.1145/3267471.3267474 [11] Chheda, R., Bohara, D., Shetty, R., Trivedi, S., Karani, R. (2023). Music recommendation based on affective image content analysis. Procedia Computer Science, 218: 383-392. https://doi.org/10.1016/j.procs.2023.01.021 [12] Liu, Z., Xu, W., Zhang, W., Jiang, Q. (2023). An emotion-based personalized music recommendation framework for emotion improvement. Information Processing & Management, 60(3): 103256. https://doi.org/10.1016/j.ipm.2022.103256 [13] Lee, W.P., Chen, C.T., Huang, J.Y., Liang, J.Y. (2017). A smartphone-based activity-aware system for music streaming recommendation. Knowledge-Based Systems, 131: 70-82. https://doi.org/10.1016/j.knosys.2017.06.002 [14] Álvarez, P., Zarazaga-Soria, F.J., Baldassarri, S. (2020). Mobile music recommendations for runners based on location and emotions: The DJ-Running system. Pervasive and Mobile Computing, 67: 101242. https://doi.org/10.1016/j.pmcj.2020.101242 [15] Sarin, E., Vashishtha, S., Kaur, S. (2022). SentiSpotMusic: A music recommendation system based on sentiment analysis. In 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST), Jamshedpur, India, pp. 373-378. https://doi.org/10.1109/ICRTCST54752.2022.9781862 [16] Hyung, Z., Lee, K., Lee, K. (2014). Music recommendation using text analysis on song requests to radio stations. Expert Systems with Applications, 41(5): 2608-2618. https://doi.org/10.1016/j.eswa.2013.10.035 [17] Wang, R., Ma, X., Jiang, C., Ye, Y., Zhang, Y. (2020). Heterogeneous information network-based music recommendation system in mobile networks. Computer Communications, 150: 429-437. https://doi.org/10.1016/j.comcom.2019.12.002 [18] Andjelkovic, I., Parra, D., O’Donovan, J. (2019). Moodplay: Interactive music recommendation based on artists’ mood similarity. International Journal of Human-Computer Studies, 121: 142-159. https://doi.org/10.1016/j.ijhcs.2018.04.004 [19] Chen, H.C., Chen, A.L. (2005). A music recommendation system based on music and user grouping. Journal of Intelligent Information Systems, 24: 113-132. https://doi.org/10.1007/s10844-005-0319-3 [20] Weng, H., Chen, J., Wang, D., Zhang, X., Yu, D. (2022). Graph-Based attentive sequential model with metadata for music recommendation. IEEE Access, 10: 108226-108240. https://doi.org/10.1109/ACCESS.2022.3213812 [21] Kim, H.G., Kim, G.Y., Kim, J.Y. (2019). Music recommendation system using human activity recognition from accelerometer data. IEEE Transactions on Consumer Electronics, 65(3): 349-358. https://doi.org/10.1109/TCE.2019.2924177 [22] Zangerle, E., Chen, C.M., Tsai, M.F., Yang, Y.H. (2018). Leveraging affective hashtags for ranking music recommendations. IEEE Transactions on Affective Computing, 12(1): 78-91. https://doi.org/10.1109/TAFFC.2018.2846596 [23] Hasib, K.M., Tanzim, A., Shin, J., Faruk, K.O., Al Mahmud, J., Mridha, M.F. (2022). Bmnet-5: A novel approach of neural network to classify the genre of bengali music based on audio features. IEEE Access, 10: 108545-108563. https://doi.org/10.1109/ACCESS.2022.3213818 [24] Wang, D., Zhang, X., Yu, D., Xu, G., Deng, S. (2020). Came: Content-and context-aware music embedding for recommendation. IEEE Transactions on Neural Networks and Learning Systems, 32(3): 1375-1388. https://doi.org/10.1109/TNNLS.2020.2984665 [25] Cai, X., Han, J., Li, W., Zhang, R., Pan, S., Yang, L. (2018). A three-layered mutually reinforced model for personalized citation recommendation. IEEE Transactions on Neural Networks and Learning Systems, 29(12): 6026-6037. https://doi.org/10.1109/TNNLS.2018.2817245 [26] Ricci, F., Rokach, L., Shapira, B. (2015). Recommender systems: introduction and challenges. Recommender Systems Handbook, 1-34. https://doi.org/10.1007/978-1-4899-7637-6_1 [27] Zhong, G., Wang, H., Jiao, W. (2018). MusicCNNs: A new benchmark on content-based music recommendation. In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, Springer International Publishing. Proceedings, Part I 25: 394-405. https://doi.org/10.1007/978-3-030-04167-0_36 [28] Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J. (2010). Temporal recommendation on graphs via long-and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 723-732. https://doi.org/10.1145/1835804.1835896 [29] He, Y., Wang, C., Jiang, C. (2018). Correlated matrix factorization for recommendation with implicit feedback. IEEE Transactions on Knowledge and Data Engineering, 31(3): 451-464. https://doi.org/10.1109/TKDE.2018.2840993 [30] Karthik, R.V., Ganapathy, S. (2021). A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce. Applied Soft Computing, 108: 107396. https://doi.org/10.1016/j.asoc.2021.107396 [31] Athira, U., Thampi, S.M. (2018). Linguistic feature based filtering mechanism for recommending posts in a social networking group. IEEE Access, 6: 4470-4484. https://doi.org/10.1109/ACCESS.2017.2789200 [32] Krumhansl, C.L., Schenck, D.L. (1997). Can dance reflect the structural and expressive qualities of music? A perceptual experiment on Balanchine's choreography of Mozart's Divertimento No. 15. Musicae Scientiae, 1(1): 63-85. https://doi.org/10.1177/102986499700100105 [33] Lee, M., Lee, K., Park, J. (2013). Music similarity-based approach to generating dance motion sequence. Multimedia Tools and Applications, 62: 895-912. https://doi.org/10.1007/s11042-012-1288-5 [34] Wang, D., Zhang, X., Wan, Y., Yu, D., Xu, G., Deng, S. (2021). Modeling sequential listening behaviors with attentive temporal point process for next and next new music recommendation. IEEE Transactions on Multimedia, 24: 4170-4182. https://doi.org/10.1109/TMM.2021.3114545 [35] Patil, S.A., Komati, T.R. (2022). Designing of a novel neural network model for classification of music genre. Ingénierie des Systèmes d’Information, 27(2): 327-333. https://doi.org/10.18280/isi.270217 [36] Yi, J., Zhu, Y., Xie, J., Chen, Z. (2021). Cross-modal variational auto-encoder for content-based micro-video background music recommendation. IEEE Transactions on Multimedia. https://doi.org/10.1109/TMM.2021.3128254 [37] Lin, Q., Niu, Y., Zhu, Y., Lu, H., Mushonga, K.Z., Niu, Z. (2018). Heterogeneous knowledge-based attentive neural networks for short-term music recommendations. IEEE Access, 6: 58990-59000. https://doi.org/10.1109/ACCESS.2018.2874959 [38] Ayata, D., Yaslan, Y., Kamasak, M.E. (2018). Emotion based music recommendation system using wearable physiological sensors. IEEE Transactions on Consumer Electronics, 64(2): 196-203. https://doi.org/10.1109/TCE.2018.2844736

Phone: + 1 825 436 9306

Email: [email protected]

Subscription

Language support

Please sign up to receive notifications on new issues and newsletters from IIETA

Select Journal/Journals:

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

We're Hiring!
Help Center

Music Recommendation System Using Machine Learning

2021, International Journal of Scientific Research in Computer Science, Engineering and Information Technology

In our project, we will be using a sample data set of songs to find correlations between users and songs so that a new song will be recommended to them based on their previous history. We will implement this project using libraries like NumPy, Pandas.We will also be using Cosine similarity along with CountVectorizer. Along with this,a front end with flask that will show us the recommended songs when a specific song is processed.

Related Papers

IRJET Journal

This research paper presents a study on developing a machine learning-based system to provide suggestions for music, utilizing a dataset from Asia's leading music streaming service. The purpose is the study to build a better music system for suggestions and provides personalized recommendations for listeners based on their previous listening behavior. The proposed approach employs both content-based as well as collaborative filtering approaches to produce suggestions. The content-based approach analyzes the properties associated with music, such as genre, tempo, and melody, to find similar songs. The collaborative filtering approach uses user behavior data to recognize other people that have similar hobbies and music preferences and recommends songs that they have listened to. The paper presents the planning and carrying out of the system for a song suggestion, including the data collection, preprocessing, and feature extraction steps. The system is evaluated using the dataset from the music streaming service and compared to a number of baseline algorithms. The conclusions show if the suggested system exceeds the baseline algorithms in relation to recommendation accuracy and diversity. This paper ends with a discussion of conceivable applications and limitations in terms of the planned music recommendation system, as well as future directions for investigating this field. In general, the research demonstrates the effectiveness of methods of learning from machines for building better suggestions for song systems which can improve the music experience with hearing for users.

This project's goal was to create a music recommendation system that offers consumers suitable tracks depending on their tastes. Different distance and similarity algorithms, such as correlation, Euclidean distance, correlation distance, and cosine similarity, were used in the project. A user-friendly online interface was connected to the recommendation model using the web framework Flask. The architecture of the music recommendation system is described in the report, with an emphasis on the incorporation of Flask. Using HTML, CSS, and JavaScript, it describes the setup procedure, model integration, and user interface design. The phases of testing and deployment are also covered, along with the difficulties encountered and their remedies. In order to evaluate the effectiveness of the recommendation system, evaluation measures were used. The study's findings compare how well various algorithms do at producing precise and pertinent song recommendations. In summary, the project created a music recommendation system that makes use of distance and similarity metrics and uses Flask for easy web interface integration. The results emphasise the system's advantages and disadvantages and offer ideas for future study, such as looking into other metrics and incorporating user input for advancements.

Indian Scientific Journal Of Research In Engineering And Management

SWAPNA SINGH

SHUBH AGARWAL_084

THE IJES Editor

This paper describes the creation of a music recommendation system that is based on audio signal data. The current state of the art method for music recommendation is to recommend songs based on their metadata or to use collaborative filtering. This system has been employed by, for example, Spotify, for generating "recommended" playlists based on user history. But it has problems like, the cold start problem and the popularity bias problem. The described method will help to tackle this issue by removing metadata from the equation and provides recommended playlists to the users depending only on audio signal data

Journal of Computer Science IJCSIS

Recommender system is able to identifying the n-number of users preferences and adaptively recommend music tracks according to user preferences. we are extracting unique feature tempo of each music using Marsyas Tool. Then we are applying BLX- α crossover to a extracted feature of each music track. User favorite and user profiles are included. This system have been emerging as a powerful technique of ecommerce. The majority of existing recommender systems uses an overall rating value on items for evaluating user’s preference opinions. Because users might express their opinions based on some specific features of the item, recommender systems could produce recommendations that meet user needs. In this paper we presented a Real time recommender system for music data. Multiuser Real time recommender system combines the two methodologies, the content based filtering technique and the interactive genetic algorithm by providing optimized solution every time and which is based on user’s preferences We can also share the favorite songs to other user hence it give better result and better user system. https://sites.google.com/site/ijcsis/

Ivan Koychev

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, June, 2017The paper presents a research that aims to investigate whether sound features can be used for recommending music. First it presents a study of existing tools for sound processing in order to see what features of the sound can be extracted with these tools. Second it presents experiments that use machine learning algorithms to identify the key features of the sound for the purpose of recommending music. Finally, manually classified data from 19 users were used for experiments. The achieved maximum average accuracy was measured to be 68.16%. This is an 18.17% increase in accuracy over the baseline. The conclusion is that it makes sense to analyze sound for the purpose of recommending music.Association for the Development of the Information Society, Institute of Mathematics and Informatics Bulgarian Academy of Sciences, Plovdiv University &...

International Journal of Information System Modeling and Design

Mamata Garanayak

The future of many modern technologies includes machine learning and deep learning methodologies. One of the prominent applications of these technologies is the recommender system. Due to the rapid growth of the songs in digital formats, the searching and managing of songs has become a great problem. In this study, the authors developed a recommender system using popularity and rhythm content of the song. The studies compared various techniques to improve the robustness and minimal error of the system. The authors will mostly focus on content-based, popularity-based, and collaborative-based filtering algorithms and also try to combine them using a hybrid approach. The authors utilized MAE for comparing the several procedures implemented here for the recommendation. Out of all procedures used, SVD performed well with MAE of 1.60 while KNN didn't perform that well as the authors had fewer features of song with mean absolute error of 2.212. User-relied and item-relied prototypes pe...

International Journal of Engineering Research and Technology (IJERT)

IJERT Journal

https://www.ijert.org/music-recommendation-system-using-content-and-collaborative-filtering-methods https://www.ijert.org/research/music-recommendation-system-using-content-and-collaborative-filtering-methods-IJERTV10IS020071.pdf Rapid development of mobile devices and internet has made possible for us to access different music resources freely. While the Music industry may favor certain types of music more than others, it is important to understand that there isn't a single human culture on earth that has existed without music. In this paper, we have designed, implemented and analyzed a song recommendation system. We have used Song Dataset provided to find correlations between users and songs and to learn from the previous listening history of users to provide recommendations for songs which users would prefer to listen most. The dataset contains over ten thousand songs and listeners are recommended the best available songs based on the mood, genre, artist and top charts of that year. With an interactive UI we show the listener the top songs that were played the most and top charts of the year. Listener also have the option to select his/her favorite artist and genres on which songs are recommended to them using the dataset.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Eka Karyawati

Raghav Gupta

Saber Modallal

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

International scientific research and publications M

Srinivas Kanakala

1st Workshop On Music …

Perfecto Herrera

Ankur Bhartiya

IJRISE Journal

Perfecto Herrera , Dmitry Bogdanov

Kazunori Komatani

IEEE Transactions on Audio, Speech, and Language Processing

Roja Dasthavejula

Information Processing & Management

Sung-Hyon Myaeng

Mitsunori Ogihara

International Journal on Recent and Innovation Trends in Computing and Communication

swathy vodithala

M. Gasser , Arthur Flexer

IEEE Transactions on Consumer Electronics

Yuanyuan Shi

Sabri Ghazi , Mehdi Mendjel

Expert Systems with Applications

Vivian F. López Batista

Research Square (Research Square)

dharmendra Roy

Music Recommendation System Using Machine Learning

When did we see a video on youtube let’s say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide personalized experience and increase customer engagement.

In this article, we will try to build a very basic recommender system that can recommend songs based on which songs you hear.

Importing Libraries & Dataset

Python libraries make it very easy for us to handle the data and perform typical and complex tasks with a single line of code.

Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
Numpy – Numpy arrays are very fast and can perform large computations in a very short time.
Matplotlib / Seaborn – This library is used to draw visualizations.
Sklearn – This module contains multiple libraries having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.

The dataset we are going to use contains data about songs released in the span of around 100 years. Along with some general information about songs some scientific measures of sound are also provided like loudness, acoustics, speechiness, and so on.

First five rows of the dataset

Data Cleaning

Data Cleaning is one of the important steps without which data will be of no use because the raw data contains a lot of noises that must be removed else the observations made from it will be inaccurate and if we are building a model upon it then it’s performance will be poor as well. Steps included in the data cleaning are outlier removal, null value imputation , and fixing the skewness of the data.

Basic information about the columns of dataset

Basic information about the columns of the dataset

Now. let’s check if there are null values in the columns of our data frame.

Number of null values in each column

The genre of music is a very important indicator of the type of music which is why we will remove such rows with null values. We could have imputed then as well but we have a huge dataset of around 6 lakh rows so, removing 50,000 won’t affect much (depending upon the case).

After removing rows containing null values

Now let’s remove some columns which we won’t be using to build our recommender system.

Exploratory Data Analysis

EDA is an approach to analyzing the data using visual techniques. It is used to discover trends, and patterns, or to check assumptions with the help of statistical summaries and graphical representations.

The dataset we have contains around 14 numerical columns but we cannot visualize such high-dimensional data. But to solve this problem t-SNE comes to the rescue. t-SNE is an algorithm that can convert high dimensional data to low dimensions and uses some non-linear method to do so which is not a concern of this article.

Scatter plot of the output of t-SNE

Here we can observe some clusters.

Formation of clusters in 2-D space

As we know multiple versions of the same song are released hence we need to remove the different versions of the same sone as we are building a content-based recommender system behind which the main worker is the cosine similarity function our system will recommend the versions of the same song if available and that is not what we want.

So, our concern was right so, let’s remove the duplicate rows based upon the song names.

Let’s visualize the number of songs released each year.

Countplot of the number of songs in subsequent years

Here we can see a boom in the music industry from the year 1900 to somewhere around 1990.

There is a total of 10 such columns with float values in them. Let’s draw their distribution plot to get insights into the distribution of the data.

Distribution plot of the continuous features

Some of the features have normal distribution while some data distribution is skewed as well.

As the dataset is too large computation cost/time will to too high so, we will show the implementation of the recommended system by using the most popular 10,000 songs.

Below is a helper function to get similarities for the input song with each song in the dataset.

To calculate the similarity between the two vectors we have used the concept of cosine similarity.

$sim\left ( X, Y \right ) = \frac{X \cdot Y}{\left\| X\right\|\left\| Y\right\|}$

Now, it’s time to see the recommender system at work. Let’s see which songs are recommender system will recommend if he/she listens to the famous song ‘Shape of you’.

Recommended songs if you hear ‘Shape of you’

Let’s try this on one more song.

Recommended songs if you hear ‘Love Someone’

Below shown is the case if the song name entered is incorrect.

If the input song name is not in the dataset

Although this model requires a lot of changes before it can be used in any real-world music app or website. But this is just an overview of how recommendation systems are built and used.

Please Login to comment...

Similar reads, improve your coding skills with practice.

What kind of Experience do you want to share?

5 pages•Date: March 31, 2024 and

Music recommendation system is like musical friend that understands listeners preferences and Suggests songs and playlists to the listeners. Music

recommendation based on past data suggests songs to the listeners according to the listeners choice. However, customers often face challenges in selecting the

most suitable song from such an extensive music collection. Various methods exist for developing song recommendation systems, including collaborative

filtering, content-based filtering, and hybrid method. Initially, the system collects large amount of user data, including listening history and ratings to create a

detailed profile. To construct a music recommendation system, we can use different machine learning algorithms, such as cosine similarity, K-nearest neighbor,

Weighted Product Method. Hybrid System with Singular Value Decomposition, Factorization Machine will be used.

: , ,

Main Navigation

Contact NeurIPS
Code of Ethics
Code of Conduct
Create Profile
Journal To Conference Track
Diversity & Inclusion
Proceedings
Future Meetings
Exhibitor Information
Privacy Policy

NeurIPS 2024, the Thirty-eighth Annual Conference on Neural Information Processing Systems, will be held at the Vancouver Convention Center

Monday Dec 9 through Sunday Dec 15. Monday is an industry expo.

Registration

Pricing » Registration 2024 Registration Cancellation Policy »

Conference Hotels NeurIPS has contracted Hotel guest rooms for the Conference at group pricing, requiring reservations only through this page. Please do not make room reservations through any other channel, as it only impedes us from putting on the best Conference for you. We thank you for your assistance in helping us protect the NeurIPS conference.

Announcements

See the Visa Information page for changes to the visa process for 2024.
The call for High School Projects has been released
The Call For Papers has been released
The accepted competitions have been released.

Latest NeurIPS Blog Entries [ All Entries ]

Aug 02, 2024
Jun 19, 2024
Jun 04, 2024
May 17, 2024
May 07, 2024
Apr 17, 2024
Apr 15, 2024
Mar 03, 2024
Dec 11, 2023
Dec 10, 2023

Important Dates

	Mar 15 '24 11:46 AM PDT *
	Apr 05 '24 (Anywhere on Earth)
	Apr 21 '24 (Anywhere on Earth)
Main Conference Paper Submission Deadline	May 22 '24 01:00 PM PDT *
	May 22 '24 01:00 PM PDT *
	Jun 14 '24 (Anywhere on Earth)
	Sep 05 '24 (Anywhere on Earth)
Main Conference Author Notification	Sep 25 '24 06:00 PM PDT *
Datasets and Benchmarks - Author Notification	Sep 26 '24 (Anywhere on Earth)
Workshop Accept/Reject Notification Date	Oct 09 '24 (Anywhere on Earth)
	Oct 23 '24 (Anywhere on Earth)
	Oct 30 '24 (Anywhere on Earth)
	Nov 15 '24 11:00 PM PST *
	Timezone:

If you have questions about supporting the conference, please contact us .

View NeurIPS 2024 exhibitors » Become an 2024 Exhibitor Exhibitor Info »

Organizing Committee

General chair, program chair, workshop chair, workshop chair assistant, tutorial chair, competition chair, data and benchmark chair, diversity, inclusion and accessibility chair, affinity chair, ethics review chair, communication chair, social chair, journal chair, creative ai chair, workflow manager, logistics and it, mission statement.

The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research advances in Artificial Intelligence and Machine Learning, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.

About the Conference

The conference was founded in 1987 and is now a multi-track interdisciplinary annual meeting that includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers. Along with the conference is a professional exposition focusing on machine learning in practice, a series of tutorials, and topical workshops that provide a less formal setting for the exchange of ideas.

More about the Neural Information Processing Systems foundation »

NeurIPS uses cookies to remember that you are logged in. By using our websites, you agree to the placement of cookies.

DOI: 10.1145/3672758.3672793
Corpus ID: 271755551

Online Courses Student Performance Prediction with Multi-model Stacking Ensemble Classifier

Tianci Zheng , Zhurong Zhou , +1 author Yi Chen
Published in Proceedings of the 3rd… 26 January 2024
Computer Science, Engineering
Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering

5 References

Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system, multi-split optimized bagging ensemble model selection for multi-class educational data mining, an ensemble prediction model for potential student recommendation using machine learning, a review on predictive modeling technique for student academic performance monitoring, related papers.

Showing 1 through 3 of 0 Related Papers

This paper is in the following e-collection/theme issue:

Published on 2.8.2024 in Vol 26 (2024)

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers

Authors of this article:

Amir Kamel Rahimi 1, 2 , BSc, MSc ;
Oliver Pienaar 3 , BSc (Hons), MSc ;
Moji Ghadimi 3 , PhD ;
Oliver J Canfell 1, 2, 4, 5 , PhD ;
Jason D Pole 1, 6, 7 , PhD ;
Sally Shrapnel 1, 3 , PhD ;
Anton H van der Vegt 1 , PhD ;
Clair Sullivan 1, 8 , MBBS(Hons), MD

1 Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia

2 Digital Health Cooperative Research Centre, Australian Government, Sydney, Australia

3 The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia

4 Business School, The University of Queensland, Brisbane, Australia

5 Department of Nutritional Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom

6 Dalla Lana School of Public Health, The University of Toronto, Toronto, ON, Canada

7 ICES, Toronto, ON, Canada

8 Metro North Hospital and Health Service, Department of Health, Queensland Government, Brisbane, Australia

Corresponding Author:

Amir Kamel Rahimi, BSc, MSc

Queensland Digital Health Centre

Faculty of Medicine

The University of Queensland

Health Sciences Building

Herston Campus

Brisbane, QLD4006

Phone: 61 0733465350

Email: [email protected]

Background: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set–based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows.

Objective: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.

Methods: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics.

Results: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework.

Conclusions: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

Introduction

The growing adoption of electronic medical records (EMRs) in many high-income countries has resulted in improvements in health care delivery through the implementation of clinical decision support systems at the point of care [ 1 ]. To meet the ever-accelerating demands for clinical care, various innovative models have been developed to harness the potential of EMR data [ 2 - 4 ]. These new care models aim to enable health care organizations to achieve the quadruple aim of care, which includes enhancing patient experience, advancing providers’ experience, improving the health of the population, and reducing health care costs [ 5 ].

Artificial intelligence (AI) holds the potential to improve health system outcomes by enhancing clinical decision support systems [ 6 , 7 ]. AI aims to augment human intelligence through complicated and iterative pattern recognition, generally on large data sets that exceed human abilities [ 8 ]. While a large body of academic literature has demonstrated the efficacy of AI models in various health domains, most of these models remain as proof of concept and have never been implemented in real-world workflows [ 9 ]. This demonstrates the relatively inconsequential endeavors of many AI studies that fail to produce any meaningful impact in the real world. Even with the substantial investments made by the health industry, the implementation of AI analytics in complex clinical practice is still at an early stage [ 10 ]. In a limited number of instances, AI has been successfully implemented, largely for nonclinical uses such as service planning or trained on limited static data sets such as chest x-rays or retinal photography [ 11 ]. The factors influencing the success or failure of AI implementations in health are poorly investigated [ 12 ]. Understanding these barriers and enablers increases the likelihood of successful implementation of AI for the digital transformation of the health system [ 13 , 14 ], ultimately aiding in achieving the quadruple aim of health care [ 5 ].

Toward the Digital Transformation of Health Care

A 3-horizon framework has been previously published to help health systems create an iterative pathway for successful digital health transformation ( Figure 1 [ 15 ]). Horizon 1 aims to optimize the routine collection of patient data during every interaction with the health system. In horizon 2, the data collected during routine care are leveraged in real or near real time to create analytics. Finally, in horizon 3, the insights from data and digital innovations are collated to develop new models of care. A health care system focused on continuous improvement is referred to as a learning health system (LHS) that uses routinely collected data to monitor and enhance health care outcomes consistently [ 16 ]. When health care organizations reach the third horizon, they can leverage data in near real time to create ongoing learning iterations and enhance patient care, leading to the establishment of an LHS [ 17 ].

Regarding the 3-horizon model, EMRs are the foundation of horizon 1 ( Figure 1 ). While many health organizations have successfully adopted EMRs into their existing workflows, the transition to horizons 2 and 3 has been challenging for many of these health care facilities [ 18 ]. A critical phase in this transition involves moving beyond the capture of EMR data for delivering analytics, including AI, aiming to improve clinical outcomes. There is little published evidence to assist health systems in making this transition [ 19 , 20 ].

Analysis of Prior Work

Before conducting our review, we performed a manual search on Google Scholar using our Medical Subject Heading (MeSH) terms along with the “review” keyword to identify previous review papers that aimed at reviewing studies on the implementation of clinical AI in health care settings. We also included review papers known to our research team. Between 2020 and 2022, we identified 4 reviews that were relevant to the implementation of AI in health care systems [ 21 - 24 ]. Overall, these papers reviewed 189 studies between 2010 and 2022. The characteristics of these reviews, outlined in Table 1 , were the year of publication, the targeted care settings, the source of data, the predictive algorithm, and whether the predictive algorithm was implemented.

Study	Year	Health care setting	Data source	Predictive algorithm	Implementation state
Lee et al [ ]	2020	Any	EMR	Any	Implemented
Wolff et al [ ]	2021	Any	Any	AI and ML	Implemented
Sharma et al [ ]	2022	Any	Any	AI and ML	Implemented
Chomutare et al [ ]	2022	Any	Any	AI and ML	Implemented or developed
Our study	2023	Hospitals	EMR	AI and ML	Implemented or guidelines

a EMR: electronic medical record.

b AI: artificial intelligence.

c ML: machine learning.

The prior works identified 20 enablers and 13 barriers to AI implementation in health care across 4 categories: people, process, information, and technology ( Multimedia Appendix 1 [ 21 - 24 ]). Overall, the findings derived from these review papers hold significant potential in providing valuable insights for health systems to navigate the path toward digital health transformation. One prevailing shortcoming of these studies is the absence of alignment with evidence-based digital health transformation principles to provide health care organizations with actionable recommendations to enable an LHS [ 17 ], therefore limiting their applicability for strategic planning within hospital organizations.

Research Significance and Objectives

Hospitals are intricate hubs within the health care ecosystem, playing a central role in providing comprehensive medical care and acting as crucial pillars supporting the foundations of health care systems worldwide. Understanding the factors influencing the success or failure of AI in hospitals provides valuable insights to optimize the integration of these emerging technologies into hospital facilities. While the previous reviews included all health care settings [ 21 - 24 ], our study only focused on hospital settings. Given the limited instances regarding the implementation of AI in hospital facilities, this study explored the real-world case studies that have practically reported their AI implementation solutions in hospital facilities, aiming to synthesize the evidence of enablers and barriers within their implementation process. In addition to the inclusion of these implementation case studies, we incorporated implementation guidelines as they can potentially assist in the overall understanding of AI implementation in hospitals. This study also focused on aligning the evidence of enablers and barriers within the 3-horizon framework [ 15 ], offering a way to establish an empirical infrastructure. As a result, this can enable health care organizations to learn, adapt, and accelerate progress toward an LHS [ 25 ].

This review investigated the following research questions (RQs): (1) What enablers and barriers are identified for the successful implementation of AI with EMR data in hospitals? (RQ 1) and (2) How can the identified enablers of and barriers to AI implementation lead to actions that drive the digital transformation of hospitals? (RQ 2).

In addressing these questions, our objectives were to (1) conduct a systematic review of the literature to identify the evidence of enablers of and barriers to the real-world implementation of AI in hospital settings and (2) map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.

Search Strategy

This study followed an extended version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to outline the review methodology with comprehensive details [ 26 ]. PubMed, Scopus, Web of Science, and IEEE Xplore were searched on April 13, 2022. We reviewed prior work to determine potential MeSH keywords relevant to our study [ 21 - 24 ]. A research librarian helped with the definition of the MeSH keywords in PubMed and the translation of that search strategy to all platforms searched. The search strategies were applied across the 4 databases ( Multimedia Appendix 2 ). The MeSH keywords used to search PubMed were as follows: product lifecycle management , artificial intelligence , machine learning , deep learning , natural language processing , neural networks , computer , deep learning , big data , hospital , inpatient , medical , clinic , deploy , integrate , monitor , post prediction , data drift , and regulatory . Using the Boolean operator OR , their synonyms were joined to form search phrases. Combining search phrases using the AND operator produced the final search string. We incorporated the term “data drift” to the title and abstract, and full-text search as it is a prominent concept for the continuous integration of AI. The term “regulatory” was also added to our search criteria because it is a relevant term for the implementation of AI in health care within the domain of software as a medical device. The reference lists of the included studies were examined to ensure that all relevant papers were included.

Eligibility Criteria

The inclusion criteria were articles published from January 1, 2010, to April 13, 2022, that included case studies and guidelines on the implementation of AI analytic tools in hospital settings using EMR data. Given the scarcity of real-world AI tools in hospital settings, especially the scarcity of published case studies of unsuccessful implementations of clinical AI tools, we specifically included case studies that successfully implemented AI within hospitals to understand lessons learned and provide use cases that other jurisdictions may learn from. On the basis of a review of frameworks for AI implementation in health care practice, we defined the term implementation as “an intentional effort designed to change or adapt or uptake interventions into routines” [ 19 ]. The term “barrier” was defined as “experiences that impeded, slowed, or made implementations difficult in some way” [ 20 ]. In contrast, the term enablers was defined as factors, experiences, or processes that facilitated the implementation process. Studies conducted in community or primary care settings were excluded as our main focus was hospital facilities. Studies that did not use AI models were also excluded. We also eliminated non–English-language and conference articles. Studies that focused on regulatory domains and challenges, opportunities, requirements, and recommendations were also excluded as they did not demonstrate real-world AI implementation. The selection of studies was based on the criteria specified in Textbox 1 .

Inclusion criteria

Population: adults (aged ≥18 y); inpatients
Intervention: successfully implemented artificial intelligence (AI) and machine learning (ML) tools using hospital electronic medical record data
Study design: case studies that implemented AI and ML in the real world; guidelines on the real-world implementation of AI and ML
Publication date: January 2010 to April 2022
Language: English

Exclusion criteria

Population: nonadults (aged <18 y); outpatients
Intervention: traditional statistical methods; rule-based systems; systems without AI and ML
Study design: studies without implementation of AI and ML; studies focused on AI and ML development, regulatory-related domains, challenges, opportunities, and recommendations; conference papers; primary care or community settings
Language: non-English

For the screening and data extraction procedures, the Covidence (Veritas Health Innovation) systematic review software was used [ 27 ]. A 2-stage screening process was performed with the involvement of 2 reviewers (AKR and OP). In the initial stage, the reviewers assessed the relevance of titles and abstracts based on the inclusion criteria. Subsequently, in the second stage, the full texts of the included articles were reviewed by AKR and OP independently. Consensus was reached through discussion between the reviewers whenever necessary.

Data Extraction and Synthesis

AKR and OP conducted the procedure of data extraction. The following study characteristics were extracted from all final included studies: country, clinical setting, study type (case study or guideline), and aim of study. With the adoption of EMR as a prerequisite for AI development, our focus was on extracting evidence of enablers and barriers solely within horizons 2 (implementation) and 3 (creating new models of care). In total, 2 reviewers (AKR and OP) independently extracted evidence regarding enablers and barriers (RQ 1), subsequently reaching consensus through weekly discussions and analysis. The extracted data were disseminated among our research team for review and to gather additional feedback.

To address the second RQ (RQ 2), we mapped the findings from previous reviews along with the found factors in this study across horizons 2 and 3 of the digital transformation framework [ 15 ]. Following the data extraction phase, 2 reviewers independently mapped the identified enablers and barriers to 4 categories (people, process, information, and technology). During the mapping of a given enabler or barrier, if it was related to the development of AI analytics, it was mapped to horizon 2 considering its relevance across the 4 domains (people, technology, information, and processes). When an enabler or barrier was associated with the postdevelopment phase focusing on establishing new care models, it was mapped to horizon 3. Consensus was reached between AKR and OP through a meeting to finalize the mapping phase.

Quality Assessment

For the included use case studies, we used the Mixed Methods Appraisal Tool (MMAT) [ 28 ] to conduct a quality assessment. The choice of the MMAT was suitable as the included use case studies exhibited a range of qualitative, quantitative, and mixed methods designs. For evaluating the methodology of guideline studies, we followed the ADAPTE framework [ 29 ]. With 9 modules for guideline development, this framework was designed to streamline and enhance the process of creating guidelines within the health domain. The quality assessment was conducted independently by 2 authors (AKR and OP), and any discrepancies were resolved through a meeting.

Study Selection

The search strategy retrieved 1247 papers from PubMed, Scopus, IEEE Xplore, and Web of Science for analysis, and 67 (5.37%) duplicates were identified and eliminated using the EndNote (Clarivate Analytics) citation manager. After screening titles and abstracts, 92.03% (1086/1180) of the studies were removed as the inclusion criteria were not satisfied. A total of 7.97% (94/1180) of the papers remained for full-text review following title and abstract screening. In total, 48% (45/94) of papers were excluded because AI models were not implemented in clinical care. A total of 19% (18/94) of the studies were excluded because they focused on regulatory domains. In total, 9% (8/94) of the studies were excluded due to being the wrong intervention (eg, studies that did not develop AI models). A total of 3% (3/94) of the studies were found to have a clinical population that did not align with our inclusion criteria (eg, hospitalized patients). One study was not in English and was excluded. In addition, 7 studies were discovered by scanning the reference lists of the included articles. In total, 26 studies were included in this review, comprising 9 (35%) guideline studies and 17 (65%) papers with successful implementation examples ( Table 2 ). Figure 2 presents the PRISMA flow diagram outlining the outcomes of this review.

Study, year	Country	Clinical setting	Study type	Aim of study	Enablers	Barriers
Wilson et al [ ], 2021	United Kingdom	General	Guideline	development and implementation		inexperience with AI
Svedberg et al [ ], 2022	Sweden	General	Guideline
Subbaswamy and Saria [ ], 2019	United States	General	Guideline
Pianykh et al [ ], 2020	United States	Radiology	Guideline
Leiner et al [ ], 2021	The Netherlands	Radiology	Guideline
Gruendner et al [ ], 2019	Germany	General	Guideline	models in health care settings	standard was used to exchange health data between different health care points in a consistent manner database structure was used as a standard method to organize health care data consistently across various data points. This also enabled the availability of data to researchers and end users.
Eche et al [ ], 2021	United States	Radiology	Guideline
Allen et al [ ], 2021	United States	Radiology	Guideline		allows AI to perform according to the implementation requirements
Verma et al [ ], 2021	Canada	General	Guideline
Wiggins et al [ ], 2021	United States	Radiology	Case study
Wang et al [ ], 2021	China	Radiology	Case study	scans automatically to promptly detect COVID-19 pneumonia in hospitals
Strohm et al [ ], 2020	The Netherlands	Radiology	Case study
Soltan et al [ ], 2022	United Kingdom	ED triage	Case study
Sohn et al [ ], 2020	United States	Radiology	Case study
Pierce et al [ ], 2021	United States	Radiology	Case study
Kanakaraj et al [ ], 2022	United States	Radiology	Case study		image management, HTTPS service, and REDCap database compliance)
Jauk et al [ ], 2020	Austria	General	Case study			data
Davis et al [ ], 2019	United States	General	Case study
Blezek et al [ ], 2021	United States	Radiology	Case study
Pantanowitz et al [ ], 2020	United States	Pathology	Case study
Fujimori et al [ ], 2022	Japan	ED	Case study
Joshi et al [ ], 2022	United States	General	Case study	tool with ML models and rule-based approach from the viewpoint of those leading the implementation
Pou-Prom et al [ ], 2022	Canada	General	Case study
Baxter et al [ ], 2020	United States	General	Case study
Sandhu et al [ ], 2020	United States	ED	Case study
Sendak et al [ ], 2020	United States	ED	Case study

a AI: artificial intelligence.

b CST: collaborative science team.

c HCP: health care provider.

d HL7: Health Level 7.

e ML: machine learning.

f FHIR: Fast Healthcare Interoperability Resources.

g OMOP-CDM: Observational Medical Outcomes Partnership Common Data Model.

h QA: quality assurance.

i SOLE: Standardized Operational Log of Events.

j CT: computerized tomography.

k ED: emergency department.

l PACS: picture archiving and communication system.

m REDCap: Research Electronic Data Capture.

n HIPAA: Health Insurance Portability and Accountability Act.

o EHR: electronic health record.

p CDS: clinical decision support.

Study Characteristics

Table 2 outlines the characteristics of the included studies in this review. The publication dates of the included studies ranged from 2019 to 2022 [ 20 , 30 - 54 ]. In total, 65% (17/26) of the studies were case studies on the implementation of AI in hospitals [ 20 , 39 - 54 ], whereas the remaining 35% (9/26) were implementation guidelines [ 30 - 38 ].

Of the 26 identified studies, 15 (58%) originated from the United States [ 20 , 32 , 33 , 36 , 37 , 39 , 43 - 45 , 47 - 49 , 52 - 54 ]; 2 (8%) originated from the United Kingdom [ 30 , 42 ]; 2 (8%) originated from the Netherlands [ 34 , 41 ]; and 1 (4%) originated from China [ 55 ], Australia [ 46 ], Japan [ 50 ], Canada [ 51 ], Austria [ 46 ], Germany [ 35 ], and Sweden [ 31 ] each.

Radiology was the clinical setting in 46% (12/26) of the studies [ 33 , 34 , 36 , 37 , 39 - 41 , 43 - 45 , 48 , 49 ]. A total of 38% (10/26) of the studies were conducted in general inpatient wards [ 20 , 30 - 32 , 35 , 38 , 46 , 47 , 51 , 52 ], and 15% (4/26) were conducted in emergency departments [ 42 , 50 , 53 , 54 ].

Regarding the 35% (9/26) of guideline studies, none fully adhered to the ADAPTE framework [ 29 ]. Although these included guideline studies had clear scopes and purposes aligned with this review, they all lacked details concerning the assessment of quality, external validation, and aftercare planning procedures. The details of this assessment for all the guideline studies can be found in Multimedia Appendix 3 [ 20 , 30 - 54 ].

With respect to the 65% (17/26) of case studies, they were classified into 3 groups: quantitative descriptive (12/17, 71%) [ 39 , 40 , 42 - 49 , 51 , 54 ], qualitative (4/17, 24%) [ 20 , 41 , 52 , 53 ], and mixed methods (1/17, 6%) [ 50 ]. Overall, 5 of the case studies met the MMAT criteria: all 4 (80%) qualitative studies and the one mixed methods study. The remaining 71% (12/17) of quantitative descriptive studies failed to fully adhere to the MMAT criteria. In all but 17% (2/12) of these quantitative descriptive studies, an appropriate data sampling strategy was not used to represent their target population [ 40 , 49 ]. The statistical analysis of the findings was assessed as appropriate in 58% (7/12) of the quantitative descriptive studies [ 42 , 43 , 46 , 47 , 49 , 51 , 54 ]. Overall, our assessment revealed that the quality of 81% (21/26) of the included studies was poor due to insufficient reporting of their methodologies ( Multimedia Appendix 3 ).

RQ Findings

Rq 1a findings: enablers of ai implementation in hospitals.

A total of 28 enablers extracted from both prior work and this study (n=8, 29% were new enablers identified in our study) are presented in Table 3 . Most of these newly identified enablers (7/8, 88%) related to the information and technology categories, highlighting the potential opportunities for hospitals regarding data readiness and required technologies for the successful implementation of AI. A total of 54% (15/28) of the enablers were shared findings between the previous reviews and this study.

Horizon and category		Source		Studies, n (%)
		Previous studies	This study

					12 (46)
	Enabler 1: multidisciplinary team	]	] ] ] ] ] ] ] ] ] ] ] ]
	Enabler 2: experienced data scientists	—	] ] ]
					22 (85)
	Enabler 3: co-design with clinicians	]	] ] ] ] ] ] ] ] ] ] ] ]
	Enabler 4: robust performance monitoring and evaluation	] ]	] ] ] ] ] ] ]
	Enabler 5: seamless integration	] ]	] ] ] ] ] ]
	Enabler 6: organizational resources	] ] ]	] ] ] ]
	Enabler 7: evidence of clinical and economic AI added value	] ] ]	] ] ]
	Enabler 8: addressing data shift	]	] ] ]
	Enabler 9: improved team communication	]	—
					9 (35)
	Enabler 10: data quality	]	] ] ] ] ]
	Enabler 11: data security	]	] ] ]
	Enabler 12: data visualization	—	] ]
					15 (58)
	Enabler 13: continuous learning capability	]	] ] ] ] ]
	Enabler 14: containerization	—	] ] ] ] ]
	Enabler 15: interoperability	]	] ] ] ]
	Enabler 16: shared infrastructure	—	] ] ] ]
	Enabler 17: customization capability	] ] ]	] ] ]
	Enabler 18: vendor-agnostic infrastructure	—	] ]
	Enabler 19: computational and storage resources	—	] ]
	Enabler 20: alert considerations	—	]
	Enabler 21: ease of integration	—	]

					8 (31)
	Enabler 22: skilled end users	] ] ]	] ] ] ] ] ] ] ]
	Enabler 23: hospital leadership	]	]
	Enabler 24: innovation champions	] ] ]	—
					9 (35)
	Enabler 25: staff training	]	] ] ] ] ] ] ] ] ]
	Enabler 26: provide incentives when using AI	] ]	—
	Enabler 27: limiting non-AI solutions	]	—
					1 (4)
	Enabler 28: usability	]	]

a Enablers identified in previous reviews and this review were mapped to 4 categories of the 3-horizon framework [ 15 ].

b Not specified.

Within the scope of the 3-horizon framework [ 15 ], most included studies in this paper (22/26, 85%) indicated that the process domain facilitated the development of AI analytics within horizon 2 [ 20 , 30 , 31 , 33 - 44 , 47 , 48 , 50 - 54 ]. Co-design with clinicians was the most commonly reported enabler in 46% (12/26) of the papers in horizon 2 [ 30 , 31 , 33 , 35 , 39 - 41 , 43 , 44 , 52 - 54 ]. The process domain was also highlighted as having a facilitative role in the creation of new care models with AI (horizon 3) in 35% (9/26) of the papers [ 30 , 34 , 44 , 46 , 48 , 50 , 51 , 53 , 54 ]. Training end users to adopt AI solutions and interpret the insights was reported in all these 9 studies as an enabling factor in horizon 3.

Technological factors were highlighted in 58% (15/26) of the studies as enablers within horizon 2 [ 20 , 32 - 35 , 39 , 40 , 43 - 45 , 48 - 51 , 54 ], with the most commonly reported factor being continuous learning capability of AI analytics [ 32 , 33 , 40 , 44 , 51 ] and containerization capability by providing separated development environments [ 34 , 35 , 40 , 43 , 44 ] and applying the interoperability techniques ensuring seamless integration of diverse formats of clinical data from different hardware and software sources [ 34 , 35 , 39 , 45 ].

Of all the included studies, 46% (12/26) [ 30 , 34 , 35 , 38 - 41 , 43 , 44 , 48 , 51 , 54 ] and 31% (8/26) [ 30 , 33 , 35 , 44 , 46 , 48 , 53 , 54 ] identified people-related enablers across horizons 2 and 3, respectively, with multidisciplinary teams in horizon 2 and trained end users in horizon 3 being the 2 most reported enablers.

Enabling factors related to the information domain were discussed in 35% (9/26) of the included studies in this review [ 32 , 35 , 37 , 39 , 40 , 45 , 49 - 51 ], with data quality being the most reported enabler of the successful implementation of AI in hospitals in >50% of these papers (5/9, 56%) [ 37 , 39 , 40 , 49 , 51 ]. The enablers of the AI adoption in hospitals were reported to include factors such as considerations of data security [ 35 , 45 , 51 ] and data visualization [ 32 , 50 ] in horizon 2 along with AI usability [ 38 ] solutions in horizon 3.

RQ 1B Findings: Barriers to AI Implementation in Hospitals

Overall, a total of 18 barriers to AI implementation in hospitals were extracted from both prior work and this study, with 5 (28%) found to be new in this study ( Table 4 ). Most of these newly identified barriers (4/5, 80%) were related to the information and technology categories. A total of 50% (9/18) of the identified barriers were found to be shared findings between the previous work and this study. In our analysis, some factors played dual roles, acting as both enablers and barriers. For instance, “Seamless integration” served as an enabler (enabler 5; Table 3 ), whereas “Disruptive integration” acted as a barrier (barrier 3; Table 4 ). We reported both enablers and barriers with such reversed meanings to highlight the real-world complexities due to which such factors can exhibit this duality.

Regarding the 3-horizon framework [ 15 ], 58% (15/26) of the included studies in this review showed that the process domain hindered the development of AI within horizon 2 [ 20 , 31 , 37 , 40 - 43 , 45 - 47 , 50 - 54 ]. The lack of sufficient performance assessment within horizon 2 was the most commonly reported barrier in 27% (7/26) of the papers [ 37 , 41 , 42 , 46 , 47 , 50 ]. The factors related to the process domain were also reported as barriers to the implementation of AI within horizon 3, with 8% (2/26) of the papers reporting alert fatigue as an obstacle to AI adoption for creating new models of care [ 20 , 53 ].

Information-related factors were highlighted in 31% (8/26) of the studies as barriers within horizon 2 [ 20 , 35 , 36 , 46 , 51 ], with the most commonly mentioned one being poor data quality [ 20 , 35 , 36 , 46 , 51 ]. The challenge with data shift was reported as part of the information domain within horizon 3 [ 32 ].

Technology-related challenges in horizon 2 were identified in 19% (5/26) of the studies, including issues such as the lack of customization capability and computational limitations of hardware [ 35 , 43 , 48 , 50 , 52 ].

Within horizon 3, a total of 19% (5/26) of the included papers highlighted the barriers related to the people domain [ 20 , 30 , 41 , 50 , 53 ], with lack of trust by clinicians and inexperienced end users in using AI within their routine workflows being 2 barriers reported in these studies.

Horizon and category			Source			Studies, n (%)
			Previous studies		This study

						15 (58)
	Barrier 1: insufficient performance assessment	]		] ] ] ] ] ]
	Barrier 2: lack of standardized guidelines for AI implementation	] ] ]		] ] ] ] ]
	Barrier 3: disruptive integration	] ]		] ] ] ]
	Barrier 4: inadequate continuous learning	]		] ] ]
	Barrier 5: complexity of maintenance	] ]		]
	Barrier 6: lack of clear consensus on alert definitions	—		]
	Barrier 7: insufficient data preprocessing	]		—
						8 (31)
	Barrier 8: poor data quality	] ] ]		] ] ] ] ]
	Barrier 9: data heterogeneity	—		] ]
	Barrier 10: data privacy	—		]
	Barrier 11: challenges with data availability	] ] ]		—
						5 (19)
	Barrier 12: lack of customization capability	—		] ] ]
	Barrier 13: computational limitations of hardware	—		] ]

						5 (19)
	Barrier 14: inexperienced end users with AI output	]		] ] ]
	Barrier 15: lack of clinician trust	] ]		] ] ]
						2 (8)
	Barrier 16: alert fatigue	] ]		] ]
	Barrier 17: difficulties with understanding AI outputs	]		—
						1 (4)
	Barrier 18: data shift	]		]

a Barriers identified in previous reviews and this review were mapped to 4 categories of the 3-horizon framework [ 15 ].

RQ 2 Findings: Mapping the Findings to the 3-Horizon Framework

The identified enablers and barriers to AI implementation in hospitals (RQ 1) were mapped to the 3-horizon framework [ 15 ] across 4 categories: people, process, information, and technology within horizons 2 and 3 ( Figure 3 [ 15 ]).

In horizon 2, we identified a total of 21 enablers, with most associated with technology (n=9, 43%) and processes (n=7, 33%). Moving to horizon 3, a total of 7 enablers were identified, spanning the categories of people (n=3, 43%), processes (n=3, 43%), and information (n=1, 14%). Regarding barriers, horizon 2 presented a total of 13 barriers, with >50% (n=7, 54%) falling into the process category. In horizon 3, we identified a total of 5 barriers primarily distributed among the people (n=2, 40%), process (n=2, 40%), and information (n=1, 20%) categories.

Principal Findings

The health care industry needs to adopt new models of care to respond to the ever-growing demand for health services. Over the last decade, the academic community has shown considerable interest in the application of AI to explore new innovative models of care. Despite the numerous papers published each year exploring the potential of AI in various health domains, only a few studies have been implemented into routine workflows. Investigating the factors that lead to the success or failure of AI in health care could potentially provide actionable insights for the effective implementation of AI in clinical workflows. In this review, we explored the current state of the literature focusing on the implementation of AI in hospitals. Our review of 26 studies revealed several enablers of and barriers to the implementation of AI in digital hospitals. Although our search for studies dated back to 2010, all 26 case studies and guidelines found in our study were published from 2019 onward. This is not surprising considering the significant progress made in AI implementation across many fields in recent years. Given such substantial advancements, implementation science needs to be further developed to accommodate these new AI innovations in health care [ 19 ]. This paper can serve as a road map for decision makers, presenting key actionable items to translate AI into hospital settings and leveraging it for potential new models of care.

While this paper extends the findings of previous reviews by examining the factors associated with AI implementation in health care [ 22 - 24 ], a significant aspect found in both previous reviews and our study underscores the significance of process-related factors for creating AI analytics. A large number of papers identified in this study (22/26, 85%) reported process factors as enablers of their AI implementation, aligning with the factors found in all previous reviews (enablers 3-9; Table 3 ). This commonality indicates the significant opportunity for hospitals to leverage their existing workflows as a strategic approach to enable AI adoption. In the context of developing innovative care models through AI analytics, obstacles associated with people (barriers 14 and 15; Table 4 ) were identified in 19% (5/26) of the included studies, consistent with findings in 2 previous reviews [ 22 , 24 ]. This highlights the influence of human factors in facilitating the integration of AI in practice.

Apart from the common findings between this and previous reviews, there are several novel aspects to this study. First, it centered specifically on hospitals, the largest and richest source of clinical data. Second, it incorporated AI implementation guidelines from the included studies, allowing for a broader understanding of AI implementation. Third, our review identified new enablers of AI implementation regarding technology and information that can facilitate AI implementation, including quality of data, shared infrastructure for continuous development, and capabilities regarding hardware resources. Fourth, this paper identified new barriers to AI implementation, with most of them being within the domains of process, information, and technology. These barriers included challenges such as data privacy, dealing with heterogeneous data, limitations with the customization of AI analytics, and ambiguity surrounding the design of alert definitions. Finally, the study findings were mapped to a 3-horizon framework encompassing 4 key categories: people, information, process, and technology. This framework offers a clear and practical road map for health care organizations planning to create new AI analytics.

It is important to note that, while our primary focus was on hospital facilities, the findings of this review may exhibit variations across other health care settings. For example, the incorporation of AI in outpatient care may demand different technological infrastructures to enable AI development. Future research can expand upon this study by investigating the evidence of enablers and barriers associated with AI implementation in wider health care settings, including primary care and outpatient care, as we expect that the outcomes of this study may differ in other health care settings. Moreover, the incorporation of studies related to regulatory aspects can be a crucial component for a more comprehensive understanding of the trajectory of AI adoption within health care systems.

Toward AI Implementation in Hospitals

Actionable recommendations.

In this section, we consolidate the findings of this study and prior work within the scope of a 3-horizon framework [ 15 ] and provide recommendations for health care organizations that plan to implement AI analytics in hospitals ( Textbox 2 ). These recommendations are not the ultimate solution but rather a flexible action plan to facilitate AI implementation and mitigate potential challenges regarding the digital transformation of hospitals.

Horizon 1: establishing digital infrastructure

Implement functional electronic medical record system
Focus on improving data quality
Maintain data privacy and security
Facilitate data availability

Horizon 2: create AI analytics

Co-design with multidisciplinary team
Employ experienced data scientists
Adopt interoperability methods
Focus on AI usability
Continuously develop and evaluate AI results
Enhance data security and privacy
Improve computational capabilities
Focus on seamless integration
Enhance customization capability
Demonstrate AI added value
Improve team communication
Define design standards for AI output
Focus on vendor-agnostic architecture

Horizon 3: create new models of care

Restructure the clinical care models using insights from AI analytics
Provide user training
Continuously improve quality to produce reliable AI output and minimize data shift and alert fatigue
Leverage hospital leaders to drive AI adoption
Appoint innovation managers
Provide incentive for using AI

Horizon 1: Establishing Digital Infrastructure

Data form the core of AI development to create clinical analytics. Some information barriers emerging in horizon 2, presented in Table 4 , may be associated with challenges regarding EMR data, for example, quality of data (barrier 8), data heterogeneity (barrier 9), and data privacy (barrier 10). In the integration of EMR systems within hospital settings, careful attention must be paid to the functionality of the system to enable routine data collection to support the continuous development of AI analytics. Prioritizing the enhancement of data quality through the implementation of rigorous validation processes is a key factor in producing generalizable, reliable, and effective AI outputs. It is also imperative to ensure strict adherence to data privacy protocols during the EMR implementation, safeguarding sensitive patient information and maintaining ethical standards in handling health care data.

Horizon 2: Creating Analytics

Horizon 2 primarily focuses on data extraction and developing AI analytics. The successful implementation of AI in this horizon will be discussed within the following themes.

Form a Diverse Team of Experts

There is evidence suggesting that building a multidisciplinary team consisting of clinicians, nurses, end users, and data scientists can facilitate the successful design and implementation of AI in hospitals (enabler 1; Table 3 ). Experienced data scientists can potentially increase the success of AI in health care by ensuring accurate, reliable, and fair AI output in addition to identifying biases, handling complex medical data effectively, and optimizing AI algorithms (enabler 2; Table 3 ).

Enhance the Existing Processes

While horizon 2 revolves around technical aspects of AI implementation, the evidence indicates that involving clinicians, end users, and technical staff in the design and implementation stages is needed for successful integration (enabler 3; Table 3 ). The co-design strategy can alleviate challenges such as the lack of consensus on alert definitions (barrier 6; Table 4 ), leading to usability improvement (enabler 28; Table 3 ). Enhancing the understanding of AI output through training end users has the potential to alleviate concerns about the usability of AI output, fostering a smoother adoption of AI technologies in hospitals.

The studies recognized that minimizing workflow disruption is key for the successful implementation of AI (enabler 5; Table 3 ). To minimize workflow disruption and ensure a smooth transition when implementing new AI solutions in hospitals, it is important to engage end users from the early stage of the development process [ 56 ], although training and education should be provided to help staff members effectively incorporate the AI solution into their daily routines. For successful implementation of less disruptive technologies such as AI, it is recommended to establish a clear vision and communication by the leadership team (enabler 23; Table 3 ), have innovation champions (enabler 24; Table 3 ), and provide incentives (enabler 26; Table 3 ) to drive long-term adoption and habit formation [ 57 ].

Continuous AI development with the use of routinely collected data and clinicians’ feedback ensures that AI results accurately reflect the current clinical situations in hospital settings (enabler 13; Table 3 ). This can support clinicians in making more accurate diagnoses and treatment decisions by leveraging the latest insights derived from AI analytics. While insufficient assessment of AI performance in hospital settings is considered a prominent obstacle to successful implementation (barrier 1; Table 4 ), continuous development and monitoring helps avoid “data drift,” a phenomenon in which AI models lose accuracy over time due to changes in the data or environment [ 32 , 47 ].

Strive for Better Data Quality and Security

The studies indicated that the implementation of AI is hindered by data privacy concerns (barrier 10; Table 4 ). Hospitals can mitigate the risks associated with data handling and storage by adopting standardized data frameworks and interoperability techniques (enabler 15; Table 3 ). These measures help minimize vulnerabilities and enhance overall data security.

The quality of data in developing AI analytics refers to the accuracy, completeness, consistency, reliability, and relevance of the data used to implement AI analytics and is considered a crucial enabler for successful AI implementation in hospitals [ 58 ]. Hospitals are encouraged to improve their data quality by implementing robust data governance protocols [ 21 , 23 , 31 , 41 , 42 ], adopting standardized data protocols to facilitate interoperability [ 24 , 34 , 35 , 39 , 45 ], and actively validating and verifying the accuracy of the data with clinicians and data scientists [ 30 , 41 , 54 ].

Strengthen Technological Infrastructures

The use of third-party hardware and software in AI solutions can limit control and raise security and privacy concerns [ 43 ]. Open-source software can improve transparency and accountability by allowing experts to identify vulnerabilities, but it can also make it easier for malicious actors to exploit them [ 35 ]. To mitigate this risk, hospitals can adopt validated open-source software with appropriate security and privacy measures, such as standardized databases and interoperability protocols [ 24 , 34 , 35 , 39 , 45 ].

Horizon 3: New Models of Care

The objective of horizon 3 is to restructure the clinical care model by harnessing the insights generated from AI analytics. While the main focus of this horizon is on clinicians and processes, fewer practical experiences are available for health organizations to help in shaping the implementation strategy.

Training end users to understand AI output is suggested to enhance the adoption of AI in hospitals (enabler 25; Table 3 ). Hospital leadership plays a pivotal role in facilitating the adoption of AI by providing strategic guidance, allocating necessary resources, and fostering a supportive environment for the implementation of AI initiatives (enabler 23; Table 3 ). Hospitals are suggested to appoint innovation managers to actively promote and facilitate the applications of AI, fostering uptake and driving the implementation process in health care (enabler 24; Table 3 ). Resourcing is the crucial enabler of AI integration, in particular adequate skill sets. Experienced clinicians who can interpret AI results are essential for ensuring that AI systems are used effectively and responsibly in health care organizations (enabler 22; Table 3 ). As a result, this can redefine the traditional models of care by advocating for evidence-based practices, patient-centered care, collaborative care, and continuous quality improvement to enhance patient outcomes and the overall quality of the care provided by health care organizations.

Limitations

Our search strategy identified 26 studies that met the inclusion criteria. All 26 studies were conducted in high-income countries. As a result, the diversity and applicability of the findings to other health care systems were constrained.

By excluding regulatory frameworks from this review in the rapidly evolving regulatory landscape, we may have limited the important implementation guidelines that ensure patient safety and ethical use of AI provided by health care regulatory bodies.

We conducted a thorough examination of the reference lists in the included studies to ensure the inclusion of all relevant papers. Despite a valid research methodology, this approach may introduce publication bias, a factor to consider when appraising the study’s findings.

The methodological reporting of most studies included in this review was assessed as poor, potentially limiting the quality of the findings of this study. While consensus discussions were held after the quality assessment to mitigate potential discrepancies in the final evaluations, it is worth recognizing that this process is subjective and the perspectives of reviewers may evolve over time, resulting in variations when assessed by different individuals.

Although our intention was to identify successful implementations, it is possible that we missed significant enablers or barriers present in failed implementations.

Conclusions

This review incorporated the identified enablers of and barriers to the implementation of AI into a 3-horizon framework to guide future implementations of hospital AI analytics to evolve practice toward an LHS. Successful AI implementation in hospitals requires a shift in conventional resource management to support a new AI implementation and maintenance strategy. Using analytics to enable better decisions in hospitals is critical to enable the ever-increasing need for health care to be met.

Acknowledgments

The authors would like to acknowledge the valuable assistance of Mr Lars Eriksson, a research librarian at the Faculty of Medicine, University of Queensland, Australia, for his expertise and guidance in identifying the search strategy for this study. The authors did not use any generative artificial intelligence tools for this research paper. This study was funded by Digital Health Cooperative Research Centre Limited (DHCRC). DHCRC is funded under the Commonwealth Government’s Cooperative Research Centres program. AKR and OJC are supported by DHCRC (DHCRC-0083). The funder played no role in the study design, data collection, analysis and interpretation of the data, or writing of this manuscript.

Data Availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

Authors' Contributions

AKR, CS, SS, JDP, and OJC conceptualized this paper. This research was supervised by our senior researchers CS, SS, JDP, MG, and OJC. The development of the search strategy was conducted by AKR, CS, OJC, and JDP. In total, 2 authors (AKR and OP) conducted the screening process and extracted excerpts that were included in the tables of this paper. AKR, CS, SS, JDP, MG, AV, and OJC reviewed the findings. AKR drafted the manuscript with input from CS, SS, JDP, AV, MG, and OJC. AKR prepared all the figures in this manuscript. All authors reviewed the final manuscript.

Conflicts of Interest

None declared.

Enablers and barriers identified in previous reviews.

Search strategies across 4 databases for this review.

Quality assessment of the included studies.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.

Williams F, Boren SA. The role of the electronic medical record (EMR) in care delivery development in developing countries: a systematic review. Inform Prim Care. 2008;16(2):139-145. [ FREE Full text ] [ CrossRef ] [ Medline ]
Mann DM, Chen J, Chunara R, Testa PA, Nov O. COVID-19 transforms health care through telemedicine: evidence from the field. J Am Med Inform Assoc. Jul 01, 2020;27(7):1132-1135. [ FREE Full text ] [ CrossRef ] [ Medline ]
Reeves JJ, Hollandsworth HM, Torriani FJ, Taplitz R, Abeles S, Tai-Seale M, et al. Rapid response to COVID-19: health informatics support for outbreak management in an academic health system. J Am Med Inform Assoc. Jun 01, 2020;27(6):853-859. [ FREE Full text ] [ CrossRef ] [ Medline ]
Barlow J, Bayer S, Curry R. Implementing complex innovations in fluid multi-stakeholder environments: experiences of ‘telecare’. Technovation. Mar 2006;26(3):396-406. [ CrossRef ]
Sikka R, Morath JM, Leape L. The Quadruple Aim: care, health, cost and meaning in work. BMJ Qual Saf. Oct 2015;24(10):608-610. [ CrossRef ] [ Medline ]
Kamel Rahimi A, Canfell OJ, Chan W, Sly B, Pole JD, Sullivan C, et al. Machine learning models for diabetes management in acute care using electronic medical records: a systematic review. Int J Med Inform. Apr 02, 2022;162:104758. [ CrossRef ] [ Medline ]
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. Oct 2018;2(10):719-731. [ CrossRef ] [ Medline ]
Maddox TM, Rumsfeld JS, Payne PR. Questions for artificial intelligence in health care. JAMA. Jan 01, 2019;321(1):31-32. [ CrossRef ] [ Medline ]
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. Oct 29, 2019;17(1):195. [ FREE Full text ] [ CrossRef ] [ Medline ]
Aung YY, Wong DC, Ting DS. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. Sep 10, 2021;139(1):4-15. [ CrossRef ] [ Medline ]
Yin J, Ngiam KY, Teo HH. Role of artificial intelligence applications in real-life clinical practice: systematic review. J Med Internet Res. Apr 22, 2021;23(4):e25759. [ FREE Full text ] [ CrossRef ] [ Medline ]
Nilsen P. Making sense of implementation theories, models and frameworks. Implement Sci. Apr 21, 2015;10:53. [ FREE Full text ] [ CrossRef ] [ Medline ]
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. Jan 2019;25(1):30-36. [ FREE Full text ] [ CrossRef ] [ Medline ]
Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial intelligence in health care: bibliometric analysis. J Med Internet Res. Jul 29, 2020;22(7):e18228. [ FREE Full text ] [ CrossRef ] [ Medline ]
Sullivan C, Staib A, McNeil K, Rosengren D, Johnson I. Queensland Digital Health Clinical Charter: a clinical consensus statement on priorities for digital health in hospitals. Aust Health Rev. Sep 2020;44(5):661-665. [ CrossRef ] [ Medline ]
Etheredge LM. A rapid-learning health system. Health Aff (Millwood). 2007;26(2):w107-w118. [ CrossRef ] [ Medline ]
Mandl KD, Kohane IS, McFadden D, Weber GM, Natter M, Mandel J, et al. Scalable collaborative infrastructure for a learning healthcare system (SCILHS): architecture. J Am Med Inform Assoc. 2014;21(4):615-620. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lim HC, Austin JA, van der Vegt AH, Rahimi AK, Canfell OJ, Mifsud J, et al. Toward a learning health care system: a systematic review and evidence-based conceptual framework for implementation of clinical analytics in a digital hospital. Appl Clin Inform. Mar 2022;13(2):339-354. [ FREE Full text ] [ CrossRef ] [ Medline ]
Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res. Jan 27, 2022;24(1):e32215. [ FREE Full text ] [ CrossRef ] [ Medline ]
Joshi M, Mecklai K, Rozenblum R, Samal L. Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study. JAMIA Open. Apr 18, 2022;5(2):ooac022. [ FREE Full text ] [ CrossRef ] [ Medline ]
Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial intelligence applications in health care practice: scoping review. J Med Internet Res. Oct 05, 2022;24(10):e40238. [ FREE Full text ] [ CrossRef ] [ Medline ]
Lee TC, Shah NU, Haack A, Baxter SL. Clinical implementation of predictive models embedded within electronic health record systems: a systematic review. Informatics (MDPI). Sep 2020;7(3):25. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wolff J, Pauling J, Keck A, Baumbach J. Success factors of artificial intelligence implementation in healthcare. Front Digit Health. Jun 16, 2021;3:594971. [ FREE Full text ] [ CrossRef ] [ Medline ]
Chomutare T, Tejedor M, Svenning TO, Marco-Ruiz L, Tayefi M, Lind K, et al. Artificial intelligence implementation in healthcare: a theory-based scoping review of barriers and facilitators. Int J Environ Res Public Health. Dec 06, 2022;19(23):16359. [ FREE Full text ] [ CrossRef ] [ Medline ]
Platt JE, Raj M, Wienroth M. An analysis of the learning health system in its first decade in practice: scoping review. J Med Internet Res. Mar 19, 2020;22(3):e17026. [ FREE Full text ] [ CrossRef ] [ Medline ]
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372(1):n71. [ FREE Full text ] [ CrossRef ] [ Medline ]
Covidence systematic review software. Veritas Health Innovation. URL: https://www.covidence.org/ [accessed 2022-04-13]
Hong QN, Gonzalez-Reyes A, Pluye P. Improving the usefulness of a tool for appraising the quality of qualitative, quantitative and mixed methods studies, the Mixed Methods Appraisal Tool (MMAT). J Eval Clin Pract. Jun 2018;24(3):459-467. [ CrossRef ] [ Medline ]
Fervers B, Burgers JS, Voellinger R, Brouwers M, Browman GP, Graham ID, et al. Guideline adaptation: an approach to enhance efficiency in guideline development and improve utilisation. BMJ Qual Saf. Mar 2011;20(3):228-236. [ CrossRef ] [ Medline ]
Wilson A, Saeed H, Pringle C, Eleftheriou I, Bromiley PA, Brass A. Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment. BMJ Health Care Inform. Jul 2021;28(1):e100323. [ FREE Full text ] [ CrossRef ] [ Medline ]
Svedberg P, Reed J, Nilsen P, Barlow J, Macrae C, Nygren J. Toward successful implementation of artificial intelligence in health care practice: protocol for a research program. JMIR Res Protoc. Mar 09, 2022;11(3):e34920. [ FREE Full text ] [ CrossRef ] [ Medline ]
Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics. Apr 01, 2020;21(2):345-352. [ CrossRef ] [ Medline ]
Pianykh OS, Langs G, Dewey M, Enzmann DR, Herold CJ, Schoenberg SO, et al. Continuous learning AI in radiology: implementation principles and early applications. Radiology. Oct 2020;297(1):6-14. [ CrossRef ] [ Medline ]
Leiner T, Bennink E, Mol CP, Kuijf HJ, Veldhuis WB. Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure. Insights Imaging. Feb 02, 2021;12(1):11. [ FREE Full text ] [ CrossRef ] [ Medline ]
Gruendner J, Schwachhofer T, Sippl P, Wolf N, Erpenbeck M, Gulden C, et al. KETOS: clinical decision support and machine learning as a service - a training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services. PLoS One. Oct 03, 2019;14(10):e0223010. [ FREE Full text ] [ CrossRef ] [ Medline ]
Eche T, Schwartz LH, Mokrane FZ, Dercle L. Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification. Radiol Artif Intell. Oct 27, 2021;3(6):e210097. [ FREE Full text ] [ CrossRef ] [ Medline ]
Allen B, Dreyer K, Stibolt RJ, Agarwal S, Coombs L, Treml C, et al. Evaluation and real-world performance monitoring of artificial intelligence models in clinical practice: try it, buy it, check it. J Am Coll Radiol. Nov 2021;18(11):1489-1496. [ CrossRef ] [ Medline ]
Verma AA, Murray J, Greiner R, Cohen JP, Shojania KG, Ghassemi M, et al. Implementing machine learning in medicine. CMAJ. Aug 30, 2021;193(34):E1351-E1357. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wiggins WF, Magudia K, Schmidt TM, O'Connor SD, Carr CD, Kohli MD, et al. Imaging AI in practice: a demonstration of future workflow using integration standards. Radiol Artif Intell. Oct 27, 2021;3(6):e210152. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wang B, Jin S, Yan Q, Xu H, Luo C, Wei L, et al. AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system. Appl Soft Comput. Jan 2021;98:106897. [ FREE Full text ] [ CrossRef ] [ Medline ]
Strohm L, Hehakaya C, Ranschaert ER, Boon WP, Moors EH. Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol. Oct 2020;30(10):5525-5532. [ FREE Full text ] [ CrossRef ] [ Medline ]
Soltan AA, Yang J, Pattanshetty R, Novak A, Yang Y, Rohanian O, et al. Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening. Lancet Digit Health. Apr 2022;4(4):e266-e278. [ FREE Full text ] [ CrossRef ] [ Medline ]
Sohn JH, Chillakuru YR, Lee S, Lee AY, Kelil T, Hess CP, et al. An open-source, vender agnostic hardware and software pipeline for integration of artificial intelligence in radiology workflow. J Digit Imaging. Aug 2020;33(4):1041-1046. [ FREE Full text ] [ CrossRef ] [ Medline ]
Pierce JD, Rosipko B, Youngblood L, Gilkeson RC, Gupta A, Bittencourt LK. Seamless integration of artificial intelligence into the clinical environment: our experience with a novel pneumothorax detection artificial intelligence algorithm. J Am Coll Radiol. Nov 2021;18(11):1497-1505. [ CrossRef ] [ Medline ]
Kanakaraj P, Ramadass K, Bao S, Basford M, Jones LM, Lee HH, et al. Workflow integration of research AI tools into a hospital radiology rapid prototyping environment. J Digit Imaging. Aug 2022;35(4):1023-1033. [ FREE Full text ] [ CrossRef ] [ Medline ]
Jauk S, Kramer D, Großauer B, Rienmüller S, Avian A, Berghold A, et al. Risk prediction of delirium in hospitalized patients using machine learning: an implementation and prospective evaluation study. J Am Med Inform Assoc. Jul 01, 2020;27(9):1383-1392. [ FREE Full text ] [ CrossRef ] [ Medline ]
Davis SE, Greevy RA, Fonnesbeck C, Lasko TA, Walsh CG, Matheny ME. A nonparametric updating method to correct clinical prediction model drift. J Am Med Inform Assoc. Dec 01, 2019;26(12):1448-1457. [ FREE Full text ] [ CrossRef ] [ Medline ]
Blezek DJ, Olson-Williams L, Missert A, Korfiatis P. AI integration in the clinical workflow. J Digit Imaging. Dec 2021;34(6):1435-1446. [ FREE Full text ] [ CrossRef ] [ Medline ]
Pantanowitz L, Quiroga-Garza GM, Bien L, Heled R, Laifenfeld D, Linhart C, et al. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit Health. Aug 2020;2(8):e407-e416. [ FREE Full text ] [ CrossRef ] [ Medline ]
Fujimori R, Liu K, Soeno S, Naraba H, Ogura K, Hara K, et al. Acceptance, barriers, and facilitators to implementing artificial intelligence-based decision support systems in emergency departments: quantitative and qualitative evaluation. JMIR Form Res. Jun 13, 2022;6(6):e36501. [ FREE Full text ] [ CrossRef ] [ Medline ]
Pou-Prom C, Murray J, Kuzulugil S, Mamdani M, Verma AA. From compute to care: lessons learned from deploying an early warning system into clinical practice. Front Digit Health. 2022;4:932123. [ FREE Full text ] [ CrossRef ] [ Medline ]
Baxter SL, Bass JS, Sitapati AM. Barriers to implementing an artificial intelligence model for unplanned readmissions. ACI Open. Jul 2020;4(2):e108-e113. [ FREE Full text ] [ CrossRef ] [ Medline ]
Sandhu S, Lin AL, Brajer N, Sperling J, Ratliff W, Bedoya AD, et al. Integrating a machine learning system into clinical workflows: qualitative study. J Med Internet Res. Nov 19, 2020;22(11):e22421. [ FREE Full text ] [ CrossRef ] [ Medline ]
Sendak MP, Ratliff W, Sarro D, Alderton E, Futoma J, Gao M, et al. Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study. JMIR Med Inform. Jul 15, 2020;8(7):e15182. [ FREE Full text ] [ CrossRef ] [ Medline ]
Wang F, Preininger A. AI in health: state of the art, challenges, and future directions. Yearb Med Inform. Aug 2019;28(1):16-26. [ FREE Full text ] [ CrossRef ] [ Medline ]
Li JP, Liu H, Ting DS, Jeon S, Chan RV, Kim JE, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective. Prog Retin Eye Res. May 2021;82:100900. [ FREE Full text ] [ CrossRef ] [ Medline ]
Sibbald M, Zwaan L, Yilmaz Y, Lal S. Incorporating artificial intelligence in medical diagnosis: a case for an invisible and (un)disruptive approach. J Eval Clin Pract. Feb 2024;30(1):3-8. [ CrossRef ] [ Medline ]
Ehsani-Moghaddam B, Martin K, Queenan JA. Data quality in healthcare: a report of practical experience with the Canadian Primary Care Sentinel Surveillance Network data. Health Inf Manag. 2021;50(1-2):88-92. [ CrossRef ] [ Medline ]

Abbreviations

artificial intelligence

electronic medical record

learning health system

Medical Subject Heading

Mixed Methods Appraisal Tool

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

research question

Edited by T de Azevedo Cardoso, S Ma; submitted 11.08.23; peer-reviewed by M Nair, L Weik; comments to author 13.10.23; revised version received 08.02.24; accepted 22.05.24; published 02.08.24.

©Amir Kamel Rahimi, Oliver Pienaar, Moji Ghadimi, Oliver J Canfell, Jason D Pole, Sally Shrapnel, Anton H van der Vegt, Clair Sullivan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 02.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests
US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support
About IEEE Xplore
Accessibility
Terms of Use
Nondiscrimination Policy
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Architecture and Design
Asian and Pacific Studies
Business and Economics
Classical and Ancient Near Eastern Studies
Computer Sciences
Cultural Studies
Engineering
General Interest
Geosciences
Industrial Chemistry
Islamic and Middle Eastern Studies
Jewish Studies
Library and Information Science, Book Studies
Life Sciences
Linguistics and Semiotics
Literary Studies
Materials Sciences
Mathematics
Social Sciences
Sports and Recreation
Theology and Religion
Publish your article
The role of authors
Promoting your article
Abstracting & indexing
Publishing Ethics
Why publish with De Gruyter
How to publish with De Gruyter
Our book series
Our subject areas
Your digital product at De Gruyter
Contribute to our reference works
Product information
Tools & resources
Product Information
Promotional Materials
Orders and Inquiries
FAQ for Library Suppliers and Book Sellers
Repository Policy
Free access policy
Open Access agreements
Database portals
For Authors
Customer service
People + Culture
Journal Management
How to join us
Working at De Gruyter
Mission & Vision
De Gruyter Foundation
De Gruyter Ebound
Our Responsibility
Partner publishers

Your purchase has been completed. Your documents are now available to view.

Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies and methodologies in adversarial attacks using machine learning (ML) and deep learning methods. A methodology was conducted to guarantee a thorough literature analysis of the studies using sources such as ScienceDirect, Scopus, IEEE Xplore, and Web of Science. A question was shaped to retrieve articles published from 2019 to April 2024, which ultimately produced a total of 704 papers. A rigorous screening, deduplication, and matching of the inclusion and exclusion criteria were followed, and hence 42 studies were included in the quantitative synthesis. The considered papers were categorized into a coherent and systematic classification including three categories: security enhancement techniques, adversarial attack strategies and defense mechanisms, and innovative security mechanisms and solutions. In this article, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth four challenges and motivations of adversarial attacks, while three recommendations have been discussed. A systematic science mapping analysis was also performed to reorganize and summarize the results of studies to address the issues of trustworthiness. Moreover, this research covers a large variety of network and cybersecurity applications of defense in adversarial attack subjects, including intrusion detection systems, anomaly detection, ML-based defenses, and cryptographic techniques. The relevant conclusions well demonstrate what have achieved in defense mechanisms against adversarial attacks. In addition, the analysis revealed a few emerging tendencies and deficiencies in the area to be remedied through better and more dependable mitigation methods against advanced persistent threats. The findings of this review have crucial implications for the community of researchers, practitioners, and policy makers in network and cybersecurity using artificial intelligence applications.

1 Introduction

With the proliferation of network gear and technologies, virtually every individual now accesses the Internet on a daily basis. According to projection studies, the number of Internet users will surpass 7.5 billion by 2030 [ 1 ]. These individuals regularly engage with Internet services such as online banking, healthcare transactions, marketing, entertainment, and education. Just as in the physical realm, malicious entities online, known as cybercriminals, seek to deceive and exploit genuine users for their gain. They utilize various cyberattacks, such as malware attacks, as tools to achieve their objectives. Cyberattacks represent the swiftest expanding form of crime worldwide, resulting in financial losses that exceed the global trade in all illicit drugs [ 2 ]. According to projections, the global cost of cybercrime is expected to exceed more than ten trillion US dollars annually by 2025, which is a significant increase from the three trillion US dollars recorded in 2015 [ 1 ]. Information to be succeeded by new technology and methods of detection and identification of immediate threats and attacks is much needed to fill in the cybersecurity gap due to a rapid change in the cyberthreat environment.

Cybersecurity experts are looking into the application of deep learning (DL), a machine learning (ML) technique. This capability has partly become a reason why this particular domain is so sprung and expanded [ 3 , 4 , 5 ]. DL-integrated cybersecurity solutions are implied to spontaneously expect and detect malicious threats and later on, automatically updating themselves by expanding associated capabilities [ 6 ]. Not surprisingly, DL models are increasingly hailed as a key tool for combating various cyberattacks that have been seen in recent years. For example, the range of security data refers to many data sources including network sensors and logs, so become more numerous types. Moreover, they have a rich depth and diversity of the data as well as several connections [ 7 ]. Traditional ML algorithms cannot make use of the high dimensional information and do not benefit from larger volumes of data either. Therefore, for analysts, many problems have become too difficult or advanced [ 3 ].

On the other hand, data hierarchy is a key concept that enables DL to work without specific domain knowledge for feature extraction [ 8 , 9 ]. To sum up, DL actually substitutes feature engineering by using multilayered, nonlinear hidden layers for feature representation learning. This minimizes the cost and time taken in hiring staff to re-engineer the functionality at any time a new change in cyberattacks comes in, say zero-day malware. The satisfaction from these productive uses will spotlight the developing trust of both the public and private sectors in the usage of DL in the cybersecurity arena [ 10 , 11 ].

Both the nature and complexity of the task of protecting digital environments in the face of the special threats are illustrated in the provided study [ 12 ]. This methodical review aims to enlighten readers on the cybersecurity as well as network defense strategies that are being used through an in-depth analysis of current tactics, pitfalls, and what is new in cyberattack methods [ 13 ]. Aversive assaults cover techniques, such as attack libraries, homomorphic encryption, and side channel attacks to break through the systems’ notoriety, compromise the integrity of data and hamper essential services [ 14 ]. The variety of the threat landscape is wide and dynamic, including distributed denial of service (DDoS) aimed to spoil the Internet infrastructures to ransomware cases that target confidential data. To establish comprehensive defenses and prevent possible damage from these threats, it is vital to explore in depth the features of these measures [ 15 ].

The first line of the defense against hostile invasions is provided by cyber and network security measures, which are the spectrum of the technologies, mechanisms, and practices that should be adopted either individually or together [ 16 ]. The target of this evidence-based study is the evaluation of both positive and negative aspects of current cyber defense mechanisms to have a deeper understanding of their readiness and ability to detect, respond, and counteract hostile intrusions. The integration of validated information, theoretical models, and practical aspects could be considered the principal purpose of this review, which aims to provide the subject of the current state of security in computer networks from different points of view. The article also makes it obvious how attackers and defenders play the cat-and-mouse game by trying to resolve problems between offensive strategies and the security systems they apply. Adversaries mutate their strategies by applying new methods as cybersecurity techniques become more advanced, and hence, this points out the flaws and detects vulnerabilities to overcome traditional defenses. This piece of mystery becomes a cornerstone in the issue of what new threats may arise and how we can make our network security more secure.

The following inquiries help clarify the study goals aligned with these reasons:

Q1: What is the appropriate taxonomy for incorporating protection techniques and strategies against defense from adversarial attacks in networks and cybersecurity?

Q2: What are the motives, challenges, recommendations, and limitations related to the integration of defense methods addressed in research?

Q3: What are the most significant gaps in the literature currently concerning network and cybersecurity applications of defense in adversarial attacks security strategies against adversarial attacks?

A comprehensive investigation of adversarial attacks in defense technique invention and optimization, adversarial attack generation, defensive strategies, defense robustness, and applications in malware, intrusion, and anomaly detection is performed.

A thorough literature analysis is offered that delves into current research trends, obstacles, drivers, constraints, and suggestions in the area of network and cybersecurity applications of defense in adversarial attacks and tactics against adversarial attacks.

Identifying research gaps and suggesting future lines of inquiry, providing a roadmap for improving defense tactics in a variety of adversarial attack scenarios.

The planned outline of the research article contributes structure to the entire paper that can be followed step by step by the readers and starts with presenting the objectives, the applied methodology, the results, and implications. It is the beginning section of the article that gives a concise introduction to the research, thus setting the canvas to be further developed. The second part gives an explanation of why the research topic was selected starting with a general introduction to adversarial attacks and followed by the best existing papers. Then a finer analysis was performed that resulted in the research project. As a result, the research methodology in Section 3 explicates the rational pattern used to focus and pick research for examination purposes, which incontrovertibly augments the transparency and reliability of the research approach. Section 4 highlights the critical steps of this study, including the analysis of research articles, the extraction of key terms, and the division of results. These elements provide a solid basis for the study’s findings. Section 5 consolidates the main points in previous studies and presents them through motivations, challenges, and recommendations. In Section 6, which highlights areas for future research, gaps in this systematic field are mapped, and some points are reviewed to locate their deficiencies and to reveal the new exploratory field. Finally, the conclusion part gives a well-grounded statement on a major finding of the study and its implications in a gist.

2 Adversarial attack: Overview and analysis

This section commences with an introduction to adversarial attacks, including frameworks of their functioning, methods of implementation, and strategies used. The phenomenon of adversarial attacks explains the much deeper level of how such attacks are executed and the techniques used to destabilize ML systems. The second section provides a critical analysis of the most important and recent papers in this field. It introduces the global indicator (citations) for the papers within this field, showing their importance and connection to the debate on choosing the research project topic, and explains the logic behind this choice.

2.1 Overview

Adversarial attacks are deliberate modifications to inputs that cause ML models to make incorrect predictions, posing serious risks in applications such as autonomous vehicles (AV) and medical diagnosis [ 17 ]. Many researchers have worked in the field of adversarial attacks. First, Chakraborty et al. [ 18 ] described the goals of adversarial learning, as shown in Figure 1 .

The adversarial goals [ 18 ].

The process of determining the circumstances that may lead to undesirable results or anomalies in the ML and DL models is called an adversarial attack. Such occurrences are security concerns, especially when the model includes private or sensitive data [ 19 ]. Across diverse domains, extensive research endeavours are ongoing to explore adversarial ML attacks, which pose a substantial threat to the broad adoption of ML and DL solutions in contexts crucial for security [ 20 , 21 ].

Since Szegedy et al. [ 22 ] asserted that neural networks are susceptible to adversarial attacks, there has been an increasing focus on examining adversarial technologies within AI. Scholars continually devise novel methods to counter hostile attacks [ 23 ].

During the life cycle of ML and DL systems, five types of security threats can be distinguished: (1) poisoning attacks; (2) backdoor attacks; (3) adversarial example attacks; (4) model theft; and (5) recovery of critical training data. The initial three attacks occur during the testing phase, whereas the first two attacks transpire during the training phase ( Figure 2 ) [ 24 ].

ML attacks [ 24 ].

For example, in a poisoning attack targeting an automated vehicle, the attacker interferes with the laser signal and visual input during the ML model’s training. This alteration aims to cause the automated vehicle system to inaccurately classify traffic signs and misinterpret object distances during the testing phase [ 25 ]. Backdoor attacks implant concealed associations or triggers within ML and DL models to supersede accurate inference, such as classification, compelling the system to operate maliciously based on the attacker’s designated target. In the absence of the trigger, the system behaves normally [ 26 ].

Conversely, white-box attacks and black-box attacks represent two categories of adversarial attacks. In a white-box attack scenario, the attacker possesses information regarding the system’s architecture, modelling, weights, training set, and sample data. Within this context, the classification function becomes vulnerable to adversarial attacks, which can jeopardize the system due to the attacker’s comprehensive knowledge [ 27 ]. In black-box scenarios, adversaries lack access to information about the target model. However, they can employ techniques such as model inversion, exploit the transferability of adversarial samples, or query the target model to create a local substitute model [ 23 ].

Conversely, ML and DL are gaining traction in the field of malware detection because they cannot only detect known malware but also uncover new and covert malicious software [ 28 ]. When using ML/DL for malware analysis, there are two primary steps [ 29 ]. In the training phase, ML/DL algorithms process a set of features derived from both malicious and non-malicious data to create a predictive model [ 19 ]. In the following phase, the testing phase, the predictive model developed in the training phase is utilized to forecast the benign behaviour of the malware. ML-based adversarial models are often incorporated into various solutions to assess the impact of an attacker’s manipulation of a classifier during the training phase on the testing phase [ 19 , 20 , 30 , 31 ].

On the other hand, the latest research about adversarial attacks has been rich in its content and diversity, and this study elaborates on these concepts. Zhu et al. [ 32 ] propose an approach for image-to-image translation without paired training data, employing adversarial loss to learn a mapping from a source domain to a target domain. Coupling this with a cycle consistency loss ensures high-quality results across various tasks. Isola et al. [ 33 ] explore conditional adversarial networks, offering a versatile solution for diverse image translation tasks with effective results demonstrated and popular adoption evident from community engagement. Despite the significant progress completed in enhancing the accuracy and speed of single-image super-resolution through the utilization of faster and deeper convolutional neural networks, a fundamental challenge persists: How can we effectively restore the intricate texture details when performing super-resolution at considerable upscaling ratios? [ 34 ]. The introduction of new technologies like big data [ 35 ], cloud computing, and Internet of things (IoT) is the main driving force in technology development of network attacks, and also this triggers network attack detection techniques to always evolve. Three main problems are associated with these technologies: complex traffic from networks automatic representation, skew of attacks by samples in networks, and trade-off between anomaly detection model accuracy and repeated notice evolution [ 36 ]. In addition, in the modern world, web applications are a vital means of facilitating the provision of services. In addition, web app usage has experienced phenomenal growth, resulting in a greater number of cyberattacks. Cross-site scripting (XSS) is one of the most prevalent attack vectors in cybersecurity and affects both end users and service providers to an equal degree. In the past few years, there has been a clear increase in the adoption of ML/DL techniques for XSS attack detection. Therefore, it was aimed at highlighting ML and DL techniques [ 37 ]. Aldhaheri et al. [ 38 ] discuss recent advancements in IDS for the IoT, focusing on embedded DL algorithms and associated datasets, the types of attacks encountered by each model defense model, and the evaluation of the detection metrics. This case study broadly describes a number of challenges involved. A network-based intrusion detection system (IDS) sets up the initial defense against network attacks that breach the integrity of data, systems, and networks [ 38 ]. In recent years, deep neural networks (DNNs) have been widely applied in this domain to identify malicious traffic because they can efficiently detect malicious traffic and achieve high detection accuracy. Nevertheless, He et al. [ 39 ] established that this NIDS attack launching and detection problem has been a considerable research challenge.

2.2 Analysis

The topic of adversarial attack is important, and researchers began researching and developing it nearly 20 years ago. According to the IEEE website, when searching for “Adversarial Attack” and filtering the results according to years, this topic is considered to be at the height of its spread and work on it, as shown in Figure 3 with the number of contributions in adversarial last 5 years.

On the other hand, the topic of adversarial attacks resonates deeply within the research community and beyond. This resonance is evidenced by the substantial number of citations garnered by studies exploring this phenomenon. When conducting a search in IEEE “Adversarial Attack,” the 10 highest number of research contributions in terms of the number of citations from the papers [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ], most of them in the last 10 years and on various topics related to the adversarial attack ( Figure 4 ).

The highest contributions in terms of the number of citations from the papers in the adversarial attack.

Furthermore, studies that analyse the previous literature on adversarial attacks, whether it be reviews, surveys, or state-of-the-art, by simple search query ((“Adversarial Attacks”) AND ((“Review”) OR (“Survey”) OR (“State-of-the-Art”))), come into sight the top ten research papers that received the highest number of citations related to adversarial attacks over the last decade [ 44 , 48 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ], gathered more than 5,000 citations. This great deal of citations is an indication that the subject’s needs are paramount and that it deserves more emphasis and focus. In addition, it stresses the importance of the adversarial attack issues for the technological advancements in the ML as a key part of the security and reliability of these systems. The aforementioned research findings on the phenomenon of adversarial attacks clearly show the attention given and also the huge visibility of such research. The knowledge across this area should continue to be pursued.

On the other hand, an examination of ScienceDirect using the query ((“Defense”) AND (“Adversarial Attack”)) and filtering for the years from 2014 to 2023 reveals a notable trend: the research papers related to the mix of defense mechanisms and adversarial attacks are in the stage of strong growth. Figure 5 demonstrates this trend using the strategies of combating the adversarial attacks on the ML and DL models which have been depicted in it. The increasing trend of research in this area signifies the growing consciousness of the need for robust defense measures against the adversary’s manipulation, on the part of the researchers who are on the constant quest for novel solutions to strengthen the ML and DL systems. This pattern is a sign that the research community puts a great emphasis on addressing the constantly changing nature of security risks caused by aggressive actions and the creation of the most advanced methods of counteraction. Not only so, it underscores the significance of taking the initiative to make ML and DL models secure against possible risks through embracing pro-active approaches, hence enhancing trust and reliability in this technology being used in a broad spectrum of the domains.

Number of papers related to defense in adversarial attack.

In the same context, upon searching the ScienceDirect website using the query ((“Network”) OR (“Cybersecurity”)) AND (“Adversarial Attack”) and filtering for the years from 2014 to 2023, a discernible trend emerges: the vast number of research papers is now getting involved in research on network security, cybersecurity, and hacker attacks. Figure 6 illustrates the ascending growth pattern, which portrays the intellectual involvement of researchers in the understanding and management of digital risks characterized by adversarial manipulation in technological domains and networks.

Number of papers related to network and cybersecurity in adversarial attack.

The boosting of the output of research resembles the growing recognition of the multidimensional problems that cyberattacks pose to digital spaces and stimulates the conjoined design of holistic defense and counter-measures. While attackers are constantly inventing new attacks and using vulnerabilities among networked systems and their cyber security frameworks, students and researchers are trying to increase their capacities in detecting, preventing, and responding to threats. This increase is an outcome of collaborative efforts to strengthen networked systems against cyberattacks, which gives protection to critical infrastructure, sensitive information, and digital assets in spite of unlawful and unethical behaviours ( Figure 6 ).

Moreover, according to the aforementioned analysis, the intersection of defense mechanisms in adversarial network attacks and cybersecurity is a vital and enthralling research topic, artistically presented in academic research. This convergence brings together three fundamental areas of study: network security, cybersecurity, and adverse attacks. This research has great significance from the point of view of actually tackling the growing threats of networked malicious automatic identification system. By exposing the scenarios and methods of adversarial attacks and the invention of defense plans, researchers improve cyber security practices. In addition, the interdisciplinary aspect of this topic facilitates closer cooperation, which enhances academic discussion conclusively leading to meaningful advancements in the field ( Figure 7 ).

The topic of the study.

Figure 7 explains why there is a need for defense against intrusion in networks and cybersecurity in academic research, which demonstrates its importance. To put this in another way, the network security, cybersecurity, and threat points of origin are in constant evolution processes to match the increase of scholars in these areas. The rationale established reveals the significance of this subject, referring to its multifaceted aspect, in conjunction with the practical factors liberating digital security and the possibility of interdisciplinary cooperativity. Summing up, through the aforementioned analysis and my proposed reasons, the selected issue which is highlighted can be noticed as one of the key issues that require further study in academic research.

3 Methodology

This research followed the approach already adopted in earlier studies [ 58 , 59 ], where a systematic literature review was performed with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The analysis was subdivided as stated in the guidelines ( Figure 5 ) [ 58 , 60 ]. The recommended reporting guidelines for systematic reviews and meta-analyses filing of tabular sections outlined earlier were followed during various bibliographic citation databases drawn from a wide range of inclusion criteria that covered scientific and social science journals across different disciplines. The searching for relevant papers was conducted by using four widely recognized and reliable digital databases: SD, Scopus, IEEE, and Web of Science (WoS) [ 61 , 62 ]. These databases are invaluable to researchers because they provide thorough documentation of science and technology research and early indicators for further review and probing [ 63 ].

3.1 Search strategy

The search strategy process included all the scientific publications recorded in 2019 – source years up until April 2024. To search, a Boolean query was developed that utilized the AND operator for combining two sets of keywords, “adversarial attack” and (“ML” or “DL”) (consulting Figure 5 , to read the most detailed original version). The choice of these keywords was adopted to guarantee an effective and efficient search strategy for relevant literature.

3.2 Inclusion and exclusion criteria

The articles are written in English and published through credible scholarly journals or conference papers.

The papers should encompass the adversarial that incorporates ML and DL.

As discussed earlier, the chosen articles had to address networks and cybersecurity under adversarial attacks.

The following exclusion criteria were applied:

We omitted papers that discussed adversarial attacks in areas not related to ML and DL, and vice versa.

We omitted studies that involved adversarial attacks in ML and DL but did not capture anything relevant to network or cybersecurity and vice versa.

3.3 Study selection

This approach involves a sequence of structured components first by identification as well as the removal of papers in duplicate. To analyse the titles and abstracts of the selected articles, the use of Mendeley software was used. This preliminary sieving technique led to the elimination of many unconnected research papers that included only relevant literature. Where some variations or findings did not agree with the authors’ appraisals, a key role was played by the corresponding author in bringing about uniformity. The following step was a comprehensive text review of the entire article after matching very carefully against Section 3.2 inclusion criteria, for which the purpose of this step was to increase the precision of the selection scenario by denying access to those materials that did not correspond with what criteria were set in advance. Figure 8 reflects the process and its results, whereby it becomes possible not only to outline the stages of filtering searched for articles but also to single out those that require proper analysis.

SLR protocol.

This research focused on identifying and selecting those articles that met a set of specified criteria. First, a comprehensive search revealed 704 entries comprising the articles from the SD, totalling 659; furthermore, only Scopus 34, only one article in IEEE, contributed 10 to the WoS. Two duplicate records were found and removed solely to eliminate redundancy. leaving no remaining number of papers at this number (702). Therefore, detailed scrutiny of titles and abstracts revealed that 442 articles were excluded because they did not comply with the predefined criteria. A comprehensive analysis was then performed for the subsequent 260 contributions. A total of 218 studies were excluded due to failure to meet other inclusion criteria. In the end, 42 of these studies were included in the final collection of articles.

4 Findings analysis

Through a systematized effort to classify and analyse attacks, this study endeavours to offer deep knowledge about adversarial attacks in networks and cybersecurity using ML and DL methods. The results of the final set of articles were obtained through two main analyses: bibliometric analysis and taxonomy analysis. Section 4.1 presents nine statistical analyses using several figures to visualize and understand publications on networks and cybersecurity relationships. In taxonomy analysis, the final set of findings in the articles is discussed in Section 4.2 , where a comprehensive analysis and segregation are conducted by dividing the findings into different categories based on their specific objectives as well as what they contribute to this perspective article.

4.1 Bibliometric analysis

An influx of contributions and project research has made the identification of essence in older studies difficult. Currently, with thousands of practical and theoretical inputs, it is quite difficult to follow everything in the literature [ 64 ]. Some academics have proposed the PRISMA paradigm as a means of reworking previous reports, highlighting problems, and identifying research gaps. Furthermore, although systematic reviews expand the body of knowledge, clarify research paradigms, and synthesize literature products, they still face reliability and objectivity challenges. This arises from the fact that they rely on the authors’ opinions to rephrase the earlier findings. To improve transparency in summarizing past study findings, myriad research efforts have called for performing more holistic science mapping analysis through RStudio [ 65 ]. The use of a bibliometric method produces indisputable results, exposes gaps, and combines literary findings with high clarity and trustworthiness [ 66 ]. Furthermore, the proposed tools are easy to use and free, and no advanced skills are necessary [ 67 , 68 ]. Therefore, this article employs the more comprehensive bibliometric approach explained below. The shapes produced are based on default settings regarding the number of articles using the R Studio program ( Figures 9 – 21 ).

Most relevant words.

Most relevant sources.

Word frequency over time.

Source frequency over time.

Trend topics over time.

Word cloud of the titles of the papers.

Word cloud from the keywords of the papers.

Word cloud of the abstracts of the papers.

The tree map for the study.

Co-occurrence network for the titles of papers.

Co-occurrence network for abstracts of papers.

The thematic map for the study.

The factorial analysis for our study.

4.1.1 Most relevant words

Common words that are often repeated in research papers in this field are indicated in Figure 9 . These words are the key descriptors of popular themes and topics in the literature, providing an idea of the major areas of concentration as well as research trends. Through the identification of the recurring terms, researchers can identify the focused and central issues of the scholarly discourse in the field of network and cybersecurity applications of defense against adversarial attacks.

We selected the top essential words from 42 papers. For this purpose, we identified the main concepts and significant terms of each article. With a wide variety of topics presented in these papers, the most useful words will change based on which article they are associated with. There are many shared subjects in these studies, such as ML, cybersecurity, IDSs, adversarial attacks, DL, and other similar ones.

4.1.2 Most relevant sources

Figure 10 shows the most significant journals as sources of publications based on the number of times they were cited in the papers. The figure demonstrates the most influential and frequently cited journals as sources for papers.

The chosen relevant sources among the 42 papers involve respectable academic publications, which include Computers & Security, the Internet of Things, the Journal of Information Security and Applications, Computer Communications, Computers and Security, Expert Systems with Applications, the Journal of King Saud University-Computer and Information Sciences, Ad Hoc Networks, Applied Soft Computing, and Computer Networks. These journals are also commonly distinguished because they make significant innovative contributions to cybersecurity, network security, artificial intelligence, and related areas. High-quality research papers, reviews, and theoretical concepts are published by the magazine with works that make profound contributions to societies. Researchers frequently use papers published in these journals because they are recognized to be accurate, the methodology is rigorous, and such research corresponds to areas of interest in cybersecurity and networking.

4.1.3 Words, sources frequency, and trend topic over time

The rarest words of the most frequent words found in the titles or abstracts of scientific papers, sorted by publication year, are represented in Figure 11 . Through such visualization, one can deduce which themes are gaining momentum within the field over time and which terms have been most appealing for researchers to engage with in each particular year. In contrast, Figure 12 represents various sources that served as a research base over time. This visualization provides insight into the research flow and represents the journals and platforms through which research works in different years were disseminated. Figure 13 shows the trend topics analyzed over time, providing an interpretation of the changing subject niches and study focus within the field. Through this visualization, these choices can be revealed, such as recurrent themes, emerging trends, and evolving research continuing lines, which together provide a holistic overview of the advancement of networks and cybersecurity based on defense against adversarial attacks.

The compilation of 42 papers covers a broad spectrum of issues concerning cybersecurity, ML, and network security. In-depth papers have been written on many topics, including intrusion detection, adversarial attacks, anomaly detection in IoT devices, malware detection, federated learning, and predictive maintenance in smart systems. Technologies include but are not limited to DL, reinforcement learning (RL), adversarial learning, and blockchain technologies. The conclusions of those articles contribute to the development of reliable security strategies for the IoT. The incorporation of ML along with cutting-edge methods, such as federated learning and blockchain, gives rise to interesting avenues for preventing and protecting against emerging cyber threats.

4.1.4 Word cloud

The word cloud delivers a holistic view of certain topics [ 69 ] through the most frequent terms picked from Document titles ( Figure 14 ), Keywords ( Figure 15 ), and Abstracts ( Figure 16 ). The expression “adversarial attack” takes the lead in statistics. Other notable terms that have been used by researchers most frequently include “cybersecurity,” “IoTs,” “defense,” “security,” and “network.” These outcomes represent the main areas and the most relevant concepts that are focused on cyber and network security system protection against hostile attacks and unknown elements, opening more possibilities for further studies in this area.

The 42 papers covered a vast field covering all the cybersecurity viewpoints within the framework of network defense. Some professional fields, such as “computer and network security,” indicate the importance of keeping IT structures secure. In addition, the two most significant trending topics were “ML” and “adversarial attacks,” which indicated that there was a rapid increase in the number of studies on the use of ML techniques to detect and handle adversarial attacks. In addition to “network,” “IoT” also appears, which can be assumed to focus on ensuring the security of IoT devices and the overall networked infrastructure. “Intrusion detection” or “anomaly detection” is paramount in this context because surveillance systems are fortified to address and address deviant behaviour products of abnormalities. These keywords outline what is typically done by the teams that carry out research to bolster Internet safety and shield against cyber threats that are ever-changing.

4.1.5 Tree map

The traditional approach of depicting hierarchical information, which mostly utilizes structured trees, has some flaws when portraying large and multilayered data since it become difficult to visualize such information in the given space constraints. The Tree-Map algorithm had to be designed to solve this problem, and this algorithm renders large trees for thousands of nodes [ 70 ].

For the application of this empirical research, Figure 17 shows the tree map generated by using the algorithm. In the higher stratum of the hierarchy, the definitions of words such as “adversarial,” “attack,” “network,” and “detection” are visualized. The words probably depict the broadest subject or anchor point for the research subdomain. The next step involves a visualization that shows further terms such as “IoT” at the lower level of the hierarchy, depicted in a forest plot that demonstrates the hierarchical relationship to the first-level terms.

Owing to the Tree-Map technique, the map covers a wide range of research subjects without causing overcomplication, which is essential for researchers to determine the correlation between various terms and concepts faster and more effectively.

The term “security” is clearly evident in the tree map visualization, symbolizing the central attention given to diverse cyber-security issues. It can refer to various issues such as adversarial, attack, network, and detection. It acts as the main hub in the tree map, which adds attention to the overall importance of the research area in the papers.

4.1.6 Co-occurrence network

Bibliometric studies incorporate co-occurrence networks as one of the main instruments that they investigate. The broad network of key concepts entails the previous terms that have been connected with the analysis, and then the professionals are provided policy-makers and experts with the conceptual structure of such a given area of study [ 61 ]. Figures 18 and 19 consist of co-occurrence networks created from titles and abstract articles in the literature.

The relevant terms and concepts in the research cybersecurity papers are linked in the co-occurrence network, and 42 papers reveal the cohesiveness of these terms and concepts. Along with these significant categories, the main categories displayed in the titles and abstracts are “learning,” “detection,” “adversarial,” and “attack.” It can be concluded that these particular terms frequently appear in the same context among the subject matter of the artworks. This implies that most of the period when one is looking for “learning,” they are doing so in the context of “detection,” which shows the wide application of ML methods in building IDSs. In addition, “adversarial” and “networks” are especially connected, which points to the continuous actions taken to develop cybersecurity defenses for fast-changing threats. As a result, the network of terms offers extremely useful data on the main ideas and passages in the topic study of cybersecurity research represented by the 42 articles.

4.1.7 Thematic map

A map of thematic map was also created based on the density and centrality indices to be divided into four topological regions ( Figure 20 ). This conclusion was derived by surveying the papers analyzed in this study with additional relevant keywords.

The cybersecurity thematic map, generated from the 42 papers, illustrates the wide spectrum of topics covered by cybersecurity research. Thematic consists of associated concepts that are spread across abstracts. For instance, one topic may be “ML in Cybersecurity”, which comprises various elements, including anomaly detection, adversarial attacks, and IDSs. There can also be a theme called “IoT Security,” which might cover topics such as vulnerability analysis, anomaly detection in IoT networks, and blockchain-based security solutions for IoT devices. Each group characterizes the complex interrelations among research subjects within the larger framework of cybersecurity themes in the study, thereby clarifying the overall research domain.

4.1.8 Factorial analysis

Factorial analysis assesses similarity by enabling users to standardize bibliographic coupling, co-occurrence, and cocitation measures. It is utilized to chart the conceptual framework of a discipline by analysing word frequency within specific bibliographic clusters [ 71 ] ( Figure 21 ).

Factorial analysis of the 42 articles allowed us to study the interrelation and trend among distinguished issues such as research methods and findings. Through examination of the factors listed earlier, discerning trends as well as the interrelations they have with each other can be accomplished. Suppose we realize that texts about “adversarial attacks,” in most cases, take “ML” techniques into account and evaluate “robustness evaluation” as major issues of their analysis. Factorial analysis, in turn, purposefully reveals the backstage structure and tendencies of cybersecurity research in general.

4.2 Network and cybersecurity applications of defense in adversarial attacks: Taxonomy

Security enhancement techniques : This category included 16 of 42 contributions (38.1%).

Adversarial attack strategies and defense mechanisms : This category included 7 of 42 contributions (16.7%).

Innovative security mechanisms and solutions : This category included 19 of 42 contributions (45.2%).

Taxonomy of networks and cybersecurity in adversarial attacks.

4.2.1 Security enhancement techniques

In the realm of this category, a notable subset comprises 16 articles out of the 42 chosen articles. Two studies delve into neural network-based malware detectors . Shaukat et al. [ 72 ] developed 10 malware classifiers based on neural networks. Among this group of systems, nine were trained to face a specific type of assault, whereas the tenth was not. The approach involved not only the creation of but also the introduction to the defending system of new techniques. Such a mode of training a neural network requires the application of different adversarial strategies to the network. A similar work by Falana et al. [ 73 ] used an ensemble of deep convolutional neural network (CNN)s and GANs (known as Mal-Detect) to perform malware analysis, classification, and detection. Two studies delve into this field, but more research is needed because it is a critical area in cybersecurity. Enhancing these detectors can bolster digital defense strategies effectively

In the field of IDSs, seven studies have been conducted. Sharma et al. [ 74 ] proposed a novel anomaly-based IDS system for IoT networks using the DL technique. In particular, a filter-based feature selection DNN model in which highly correlated features are removed has been presented. In the study by Sethi et al. [ 75 ], a multiagent IDS model in which deep Q-networks are engaged by several agents and core attention mechanisms are applied to effectively perceive and categorize sophisticated network intrusions was introduced. Mishra et al. [ 76 ] presented a weighted stacked ensemble model that combined bidirectional long short-term memory networks with deep convolutional generative adversarial networks. The main objective of Khan et al. [ 77 ] was to examine various forms of CAN-bus traffic data for intrusion attack detection while also taking into account time complexity. In addition, Saheed et al. [ 78 ] proposed an IoT defender framework that employs a modified genetic algorithm (MGA) and an LSTM model to detect cyberattacks in IoT networks. The implementation is pioneering because the MGA is used for feature selection, while the GA for the refinement of the LSTM parameters is incorporated within the evolutionary computation framework. Furthermore, Rookard and Khojandi [ 79 ] defined RRIoT, which is based on a RL algorithm that works in an adversarial environment. Along with these factors, an LSTM layer is also applied. Here, the performance of the aforementioned approach is evaluated against that of both novel methods and state-of-the-art ML/RL algorithms. Finally, the research conducted by van Geest et al. [ 80 ] brought to light a new experiment of simulating bypass attacks on models whose principle of operation is limited to a single analysis. Then, the potential of hybrid methods was explored, and the advantages of these techniques with multiple models are that they compromise confidentiality and effectiveness. These studies further revealed the drawbacks of hybrid models, which largely favour their effectiveness while prioritizing their universality. These studies could improve clarity by condensing lengthy sentences and emphasizing the practical implications of the research findings for cybersecurity practices.

In addition, seven papers on cybersecurity tactics and procedure identification were published. Imran et al. [ 81 ] focused on identifying and detecting cybersecurity tactics, techniques, and procedures based on an ML approach. For I-IoT systems, Gungor et al. [ 82 ] showed that cybersecurity is a great challenge because of inadequate standardization and the lack of skills required to implement such systems. The goal of Alzahrani and Asghar [ 83 ] was to ascertain how IoT frameworks centred around logistics can categorize different threats. Consequently, this study sought to explore the optimal approach for deploying a system to detect cyber vulnerabilities within the data exchange of logistics-based IoT, utilizing historical data. Alshaikh et al. [ 84 ] aimed to understand decision makers’ and specialists’ perceptions of ML capabilities for defensive cybersecurity applications. The contributions are made in the following three areas: (1) MLCS capabilities, (2) MLCS implementation, and (3) MLCS evaluation and communication. Aurangzeb et al. [ 85 ] aimed to commence a study on deep black box adversarial attacks against smart power grids, demonstrating that statistically significant impacts on a national smart power grid can be achieved with absolute security. In addition, they investigated the detection of cybersecurity attacks on smart power grids. In addition, Nkoro et al.’s [ 86 ] methodology endeavours to identify various cyber threat categories that could impact virtual reality (VR) learning platforms by employing a straightforward DNN that offers explanations for its detection. In addition, Aygul et al. [ 87 ] investigated the capabilities and possible weaknesses of ML-driven transient stability prediction (TSP) models when confronted with such cyber threats. By tackling the hurdles associated with integrating renewable energy and modifying grids, their objective is to offer valuable insights that can bolster grid security and maintain a dependable power distribution system. From these studies, we infer insufficient practical implementation insights in the study of IoT frameworks, potential oversight of technical feasibility in perception-based analysis in defensive cybersecurity, lack of scalability considerations in the study of adversarial attacks, incomplete coverage of potential threats in the methodology employed for threat identification in VR platforms, and a failure to address broader system vulnerabilities beyond cyber threats in the investigation of ML-driven TSP models.

4.2.2 Adversarial attack strategies and defense mechanisms

This section includes adversarial attack strategies and defense mechanisms , as this section consists of seven contributions out of 42.

Two studies of secure physical systems and interconnection architecture were conducted. Jia et al. [ 88 ] devised an attack that bypasses the anomaly detectors and rules checkers of cyber-physical systems (CPSs). Due to the popularity of gradient-based methods, adversarial attacks produce noise in sensor action values, and a genetic algorithm is used to enhance this noise. Ahmed et al. [ 89 ] presented a secure mm-wave wireless interconnection architecture for mobile communication management centre systems. Even though wireless interconnects can provide an advantage to off-chip communication in MCMC systems by decreasing single-hop link energy consumption, when they are attacked, for example, by jamming-based DoS attacks, they will offer little protection. Sports the ML algorithm-based firewall and protection schemes our architecture believed to counter both external and internal persistent DoS attacks based on jamming.

Four studies on generating adversarial attacks and model robustness . Dai et al. [ 90 ] present negative sampling-based network embedding adversarial training with refinement for subtraction and for textured models that evolve in a specific manner. The central aspect in Pawlicki et al. [ 91 ] is to provide a way to counter the attacks on ML due to cyberattacks with a suggestion of the adversarial ML solution for such detection, and after the evaluation of the possibility of reducing the performance of the well-optimized intrusion detection, four methods for attack detection are provided. Furthermore, Duy et al. [ 92 ] explored the method of generating adversarial attack samples via the GAN model to develop an IDS. They proposed DIGFuPAS, a framework that can create attack samples that can bypass ML-based IDSs in software-defined networks in a black-box manner. Research on the weaknesses of wireless communication toward remote connections as a means of control in mini-electric AVs was performed [ 93 ]. The study consisted of vehicle production and the demonstration of the existence of threats in autonomous driving technology via an attack on the testing environment in the form, for example, DDoS.

Moreover, the field of disablement of IDSs consisted of only one study. Chen et al. [ 94 ] designed an attack model characteristic of IDS evasion – the Anti-Intrusion Detection AutoEncoder – to generate features that can bypass an IDS. The proposed structure works out with an encoding feature, and in the opposite direction, different decoders reconstruct continuous and discrete features. In addition, a GAN is utilized to gather the expansive prior distribution of the latent space. These studies provide insights into adversarial attack strategies and defense mechanisms with many contributions. This article details studies on secure physical systems, wireless interconnection architecture, and adversarial attacks on ML models. However, it lacks clarity due to dense information and could benefit from clearer organization and summarization of key findings.

4.2.3 Innovative security mechanisms and solutions

Within the innovative security mechanisms and solutions category, 19 of 42 papers focused on six subcategories. There were two papers in the field of blockchain and semantic computing networks (SCN). In the study by Mirsky et al. [ 95 ], (1) a novel approach for anomaly detection and (2) a lightweight framework that utilizes the blockchain to assemble an anomaly detection model in a distributed environment were proposed. Shi et al. [ 96 ] proposed a knowledge-guided SCN constructed with a primary knowledge-guided semantic tree module and an auxiliary data-driven lightweight neural network module. Blockchain and SCNs are crucial areas requiring extensive research. Two studies alone cannot adequately address the complexities and potential of these fields. Further investigation is necessary for comprehensive understanding and development. Blockchain and SCNs are crucial areas requiring extensive research. Two studies alone cannot adequately address the complexities and potential of these fields. Further investigation is necessary for comprehensive understanding and development.

Four contributions to privacy preservation and adversarial robustness . Bai et al. [ 97 ] proposed detecting evidence of LM using ML and Windows RDP event logs. They explored different feature sets extracted from these logs and evaluated various supervised ML techniques for classifying RDP sessions with high precision and recall. Chen et al. [ 98 ] demonstrated that the multiexit network can reduce the impact of adversarial perturbations by outputting easily identified samples at early exits. Therefore, it can improve the adversarial robustness. Furthermore, the multiexit network can prevent catastrophic overfitting in single-step adversarial training. In the study by Roshan and Zafar [ 99 ], the objective was to introduce a dual-phase defense strategy against the potent optimization-based adversarial attack known as Carlini & Wagner (C&W). These two defense phases consisted of training and testing phases. Through the training phase, a modified adversarial training approach employing Gaussian data augmentation is utilized. In the testing phase, the feature squeezing technique is applied to the generated list of adversarial samples before they are fed into the resilient NIDS model for the ultimate classification. In the study by Sharma et al. [ 100 ], a GAN architecture called MIGAN was proposed for the generation of malware images. This system has the advantage of being able to generate malware images of very high quality and sorting out the malware samples according to families. One potential downside of these studies is their focus on specific techniques or approaches, which do not fully address the diverse range of adversarial threats and privacy concerns present in complex real-world scenarios. In addition, the effectiveness of these methods in practical applications outside controlled environments may vary.

In addition, there are five studies in the field of federated learning security optimization . Wan et al. [ 101 ] proposed integrating blockchain-enabled FL with Wasserstein generative adversarial network (WGAN)-enabled differential privacy (DP) to protect the model parameters of edge devices in B5G networks. OQFL is a newly introduced federated learning scheme that optimizes hyperparameters by employing various adversarial attacks in AV settings [ 102 ]. In the study by Ahmad and Shah [ 103 ], the goal was to identify attacks while safeguarding the privacy of IoT users. To achieve this goal, they pursued a lightweight mini-batch federated learning approach that is computationally efficient and demands only a minimal number of federation rounds to detect malicious activity in an IoT network. FedGenID is a novel and highly valuable federated generative IDS, for the protection of industrial Internet of thing (IIoT) networks that was proposed by [ 104 ]. Compared to the existing attacks, the FedGenID assessment based on the sophisticated industrial cybersecurity dataset reveals that it is capable of detecting class imbalances and multiclass cyberattacks. In addition, its ability to remain stable against adversarial attacks is proven. Bukhari et al. [ 105 ] proposed a new SCNN-Bi-LSTM model for intrusion detection in wireless sensor networks (WSNs). This model is based on FL, which enables better intrusion detection performance and privacy. The FL-based SCNN-Bi-LSTM model applies a novel strategy in which multiple sensor nodes coordinate when training a global model without disclosing private data; as a result, privacy issues are resolved. While these studies have made progress in federated learning security optimization, they exhibit limitations. They primarily focus on specific aspects of security without considering the broader spectrum of potential threats. In addition, their efficacy in real-world scenarios with diverse network architectures and data distributions needs further validation.

The category of improving the detection of unknown attacks consists of four papers. Roshan et al. [ 106 ] studied important aspects related to NIDS, adversarial attacks, and defense mechanisms to increase the robustness of ML- and DL-based NIDS. In the study by Nguyen and Le [ 107 ], the research aimed to address limitations in detecting unknown attacks and provide better protection for IoT networks against DoS/DDoS attacks. The study by Xie and Chen [ 108 ] strives to offer a streamlined yet potent approach to intrusion detection, designed to operate efficiently even in settings with restricted computational and energy capacities. Liu et al. [ 109 ] focused on SeMalBERT, which is an adaptive malware detection model used for detecting malicious software in Windows-based systems. It trains the features on the utilization of API function sequences as learned features. For word representation, BERT is being used, as is semantic chaining. Moreover, CNNs and LSTMs can be used to explore chaining relationships. Moreover, an outgoing attention mechanism makes a model stay focused on the relevant information in the text. While these studies aim to enhance the detection of unknown attacks, they have some shortcomings. These methods may lack comprehensive validation in real-world environments, potentially limiting their applicability. In addition, their focus on specific aspects of intrusion detection may overlook broader security concerns and fail to address evolving threats effectively. While these studies aim to enhance the detection of unknown attacks, they have some shortcomings. These methods may lack comprehensive validation in real-world environments, potentially limiting their applicability. In addition, their focus on specific aspects of intrusion detection may overlook broader security concerns and fail to address evolving threats effectively.

Furthermore, there are three papers on advancements in security . In the study by Ardito et al. [ 110 ], the study aimed to encourage the implementation of ML security models in the context of smart grids. In the study by Duy et al. [ 111 ], the authors explored the ability of the WGAN with gradient penalty (WGAN‒GP) by generating perturbed attack samples to bypass attack detectors. This method can be used for regular assessment of the robustness of ML-based IDS in software-defined networks (SDN) to achieve the objective of upgrading it as a service in SDN. Albahri et al. [ 112 ] presented a fuzzy MCDM structure incorporating multiple perspectives of data fusion. Its purpose is to evaluate diverse ML models to quickly detect adversarial attacks in VANETs. This implies reckoning dedicated short-range communication systems as well as developing multi-ML models by applying standard and feature fusion preprocessing to the data and finally evaluating and benchmarking these models with fuzzy decision-making logic by using FDOSM (fuzzy decision by opinion score). In this field, authors can have the flexibility to explore various DL approaches beyond WGAN and MCDM, allowing for a diverse range of methodologies to address security challenges.

However, in the field of automatic generation of imperceptible adversarial examples, there was one contribution. Marchisio et al. [ 113 ] suggested a “greedy” algorithm for the automatization of imperceptible adversarial samples when an attacker has limited feedback. The limited scope of research on the automatic generation of imperceptible adversarial examples poses a critical drawback. Relying solely on one paper may hinder a comprehensive understanding and development of effective defense mechanisms against evolving adversarial threats.

5 Discussion

This section focuses on three key aspects related to networks and cybersecurity in adversarial attacks: motivations, challenges, and recommendations.

5.1 Motivations

This section addresses four main topics related to the motivation for networks and cybersecurity in adversarial attacks: (1) Improving IoT security, (2) enhancement of IDSs, (3) cyber-physical system security, and (4) general ML security and defense strategies ( Figure 23 ).

Motivations of networks and cybersecurity in adversarial attacks.

5.1.1 Improving IoT security

The IoT is the aggregation of the numerous individual physical devices currently called things that form a network. The connected products in the network are constrained by limited processing power and memory storage resources. As the number of IoT heterogeneous physical devices, through which the internet is accessed, continues to increase, the amount of data generated also becomes enormous; therefore, IoT networks have become more lucrative targets for attackers [ 74 ]. The study by Gungor et al. [ 82 ] showed that computational systems in the IIoT are usually not designed with security in mind. Their limited computational power creates security vulnerabilities that attackers can exploit to prevent asset availability, sabotage communication, and corrupt system data. In addition, Alzahrani and Asghar [ 83 ] mentioned the need for a cyber vulnerability detection system in logistics-based IoT data exchange. The sharing of IoT data with the cloud data centre may affect the privacy of the user’s sensitive data [ 103 ]. In the study by Nkoro et al. [ 86 ], the swift incorporation of the Internet of artificial intelligence and IoTs (AI-IoT) technologies has brought forth a crucial aspect of the forthcoming digital age: the Metaverse. Furthermore, with the rapid development of technology, cyber threats and privacy issues always arise before the IIoT. To solve this problem, there is a great demand for an advanced sequence detection system for the protection of IIoT networks [ 104 ]. The articles seem to assert the necessity for cyber vulnerability detection systems, protective measures, and the integration of novel technologies without thoroughly exploring the intricacies of these subjects. Such broad generalizations could undermine the credibility of the arguments and fail to sufficiently address the complexities inherent in the discussed issues.

5.1.2 Enhancement of IDSs

Regarding the enhancement of IDSs [ 75 ] reported that there was a need to design IDSs for networks. The implementation of a sophisticated adversary assault against an IPS may lead to a failure of detection; hence, this can be viewed as a direct problem in the application of ML models in IDS [ 91 ]. To evaluate and improve the weaknesses of IDSs against the latest attack methods, adding traffic is necessary [ 92 ]. In addition, the primary goal of Mishra et al. [ 76 ] is to create the best possible arrangement for the detection of security breaches. The optimal selection of the activation function, optimization process, epoch count, and batch size are the main topics of this study. In the realm of intrusion detection [ 108 ], there is a research void concerning the effectiveness and flexibility of such models in environments with limited resources. On the other hand, despite the immense growth of the digital sphere nudged by innovative technologies, cybersecurity vulnerabilities have reached a high level of importance. Accordingly, IDSs are the cardinal parts of network security systems that help in identifying abnormal network traffic, which may be indicative of malicious activity [ 105 ]. In addition, automated antiphishing detection systems have become a non-negotiable necessity, given that cybercriminals are continuing to innovate their methods [ 80 ].

5.1.3 Cyberscale physical system security

CPSs of critical infrastructure are exposed to a series of threats, and this has prompted investigations into the different detection techniques that can be deployed in such environments, including the use of neural network-based anomaly detection systems [ 88 ]. Research has shown that intelligent transportation systems, namely, AVs, are gaining stronger influence and that their power to transform the modern world is enormous. However, the security and privacy of these systems should be protected [ 102 ]. The controller area network protocol serves as a vital communication mechanism in vehicular systems. Nevertheless, its extensive utilization has led to vulnerabilities in in-vehicle communication channels, rendering them prone to diverse security risks such as denial-of-service, fuzzy, and impersonation attacks [ 77 ]. The rise in successful cyberattacks raises doubts about the efficacy of ML in cybersecurity applications [ 84 ]. There have been numerous concerns raised about the vulnerability of smart grid technologies to hacking [ 85 ]. In addition, cyberattacks have the potential to induce inaccurate forecasts, resulting in power outages, as shown by Aygul et al. [ 87 ]. TSP models must be engineered with robustness to withstand such cyberattacks. As a result of a global increase in cyberattacks occurring through computer systems, specifically IoT devices, the need for robust and automated methods to discover and mitigate these attacks in real time is urgent and sharp, and these techniques are fundamental for that purpose [ 79 ].

5.1.4 General ML security and defense strategies

Supervised learning models, even though they are being used to identify threats and address them, are being challenged in regard to detecting unfamiliar types of attacks, which could be very serious [ 107 ]. Mirsky et al. [ 95 ] showed that anomaly detection models need to be trained to consider all legitimate behaviours and actions. Moreover, the models are vulnerable to adversarial attacks because they presume that all observations during training are harmless [ 98 ]. It is observed that compared to normal training, adversarial training takes less time to achieve resilience against attacks, but it is limited by the number of epochs, resulting in suboptimal performance. In addition, ML has advanced at a great pace, and it will be necessary to study adversarial attacks and defense mechanisms to mitigate multiple cybersecurity problems [ 106 ]. The omission of the intuitive consideration of both normal and adversarial attack perspectives during the ML model generation stage, the absence of various preprocessing methods for VANET communication data, the insufficiency of the selection criteria for real-time adversarial attack detection models, and the minimum focus on explainability in adversarial attack detection procedures inspired the research carried out by Albahri et al. [ 112 ].

5.2 Challenges

This section discusses the difficulties in four groups ( Figure 24 ).

Challenge of networks and cybersecurity in adversarial attacks.

5.2.1 Adversarial attacks and security vulnerabilities

DL malware detectors are being implemented as one of the solutions to malware detection challenges. Although no system is completely secure, these detectors are at risk of being compromised by an adversary’s assault [ 72 ]. In regard to the challenges associated with the IoT, resource-constrained peripheral devices (i.e., those with low computational and storage capacity) and security (i.e., those for which it is more difficult to implement necessary security measures) both make implementation more difficult [ 74 ]. Although well adapted in disciplines such as images and audio, adversarial attacks turning on CPSs prove to be difficult to implement due to the presence of other built-in defense mechanisms such as rule checkers (or invariant checkers) [ 88 ]. As DNNs are widely used in various challenging ML tasks in real life, the threat of adversarial examples is receiving particular attention in the DL community [ 98 ]. In Khan et al. [ 77 ], two notable challenges must be addressed. First, the evaluation lacks exploration of diverse real-world scenarios, limiting the understanding of the robustness and generalizability of DivaCAN beyond the experimental setup. Second, practical implementation in real vehicular systems requires the consideration of hardware constraints to ensure efficient real-time operation without compromising vehicle performance.

Currently, the problem of securing IoT technologies has become very significant because of the increased complexity, imprecise settings, and conflicts between old and new systems. Such problems heavily affect the availability and reliability of existing essential infrastructure [ 78 ]. In addition, Nkoro et al. [ 86 ] reported that the increasing dependence on the Metaverse has led to the recognition of the critical importance of implementing strong cybersecurity measures. This is essential for identifying and addressing cyber threats effectively, thus safeguarding user safety. In addition, the challenge addressed in the study by Aygul et al. [ 87 ] is accurate online transient stability prediction in modern power systems that are increasingly dependent on smart grid technology and are susceptible to cyberattacks. Furthermore, automatic driving systems, whose main goal is to enhance the safety and comfort of passengers, simultaneously become prone to attack by hackers or other malicious actors because they are dependent on network technologies and require remote connections [ 93 ]. The systems of IDS can have either a hardware or software basis. Nevertheless, classic IDS schemes are not always able to efficiently perform accurate information security tasks and identify sophisticated, rare types of cyberattacks, particularly in WSN environments [ 105 ]. These articles highlight challenges in securing IoT, metaverse power systems, and autonomous driving due to the complexity, cybersecurity risks, and limitations of existing IDSs, necessitating innovative solutions.

5.2.2 Cybersecurity datasets

By far, the greatest hurdle for creating advanced ML-based fault and security defensive systems is the current scarcity of significant datasets on smart electrical grids, which provide both a wide category of such grids and their characteristics [ 110 ]. Labelling such a large dataset is very challenging since it can be performed only by an expert trained for at least 5 years [ 96 ]. Nguyen and Le [ 107 ] outlined three existing security challenges with IoT networks in terms of DoS/DDoS attacks. The first problem is related to the fact that the state-of-the-art popular datasets for IDS assessment develop certain restrictions. The second challenge is that the detection of new attacks should not require any training data. The third point is adversarial attacks that can allow the attacker to use vulnerabilities of the ML/DL-based NIDS to bypass the security mechanism. A significant obstacle in the study by Mishra et al. [ 76 ] lies in the absence of balance within real-time data, posing difficulties in adapting DL techniques to function effectively as real-time classifiers for detecting attacks when dealing with imbalanced data. According to Ahmad and Shah [ 103 ], increased training time and computational expensiveness are challenges in an IoT network. The articles highlight significant data-related challenges, so they underscore the scarcity of large, labelled datasets for ML in smart grids and IoT networks, hindering the development of effective security systems.

5.2.3 Complexity and evolution of attacks

The design of an efficient network intrusion system (IDS) is a serious problem considering the large number of new emerging attacks and complex network applications [ 75 ]. Identifying attacks can prove to be very difficult because one attack may develop in several steps, each of which is created to hinder the defense and at a separate timeline [ 97 ]. In Falana et al. [ 73 ], the challenge is that malware has become more prevalent given the complexity of today’s network and the attack landscape. Computer users and business owners find it very difficult to keep pace with cybercriminals.

5.2.4 Robustness of ML models

With the application of ML-based solutions and AI technologies in almost every sphere of present-day life, several issues regarding this pursuit have appeared as well. The immediate problem relates to adversarial attacks. In a recent study, algorithms, which are the most commonly used data-driven mechanisms in many intelligent systems, have become the targets of many attacks [ 91 ]. In the same vein, SDNs, when employed with IDSs, take advantage of the centralized control plane in an SDN to support massive-scale monitoring in a network. Nevertheless, ML-based IDSs can be overlooked and fooled by adversarial examples with the addition of natural perturbations to the original IDSs [ 92 ]. The proposed system in Alzahrani and Asghar [ 83 ] exhibits various limitations, such as (i) relying solely on one statistical method, the chi-squared measure, to identify significant features (predictors) and (ii) failing to leverage pretrained DL models. In addition, Xie and Chen [ 108 ] possess the potential vulnerability to novel or hostile attacks that were not encountered during the training phase, and the complexity of the CSNN model increases to accommodate a wider range.

Gungor et al. [ 82 ] demonstrated how cyberattacks can have a dramatic effect on the efficiency of ML methods aimed at process diagnosis and monitoring (PDM), and the results were up to 120× less efficient. Thereafter, they worked on constructing a multiple-layer combination learning environment that is unchanging against an assortment of various white-box adversarial attacks. Duy et al. [ 111 ] were exposed to two problems. First, the threat should not cause any other technologies to respond in an interlocked security system. Next, the initiator must be able to cause a given output by receiving corresponding feedback. Along these lines, it is important to mention that although federated learning is one of the most suitable mechanisms for data privacy, and it is prone to various attacks as well, especially data poisoning attacks where the adversary adds vectors in the training phase [ 102 ]. Even though hyperparameters are very useful in constructing an efficient federated learning model, they are robust against possible sideline events. The convolution layers of CNNs are not able to retain hierarchical spatial relationships such as the orientations, positions, and scaling of objects. CNNs tend to locate an object of interest via featurewise pattern recognition, not by deducing spatial relations within larger spatial structures [ 113 ].

On the other hand, [ 85 ], given the computational capabilities of quantum computers, existing encryption techniques are under significant threat, and it is only a matter of time before secure cryptography is compromised. In addition, the ML technique has shown potential in the detection of some types of attacks. However, despite its reasonable success in subsequent assaults, this approach does not provide an ideal solution [ 79 ]. In addition, a variety of current systems utilize a single-analysis model structure, resulting in weak points and, therefore, easier targets for hackers [ 80 ]. These models can be vulnerable to adversarial examples that are dependent on a single statistical technique and are incapable of accurately predicting complicated attacks. Although ML reportedly produces positive outcomes in the detection of attacks, its effectiveness is still inadequate, especially because of the danger of a mono-variate model system structure.

5.3 Recommendations

This section discusses the recommendations and future directions for researchers in the field of networks and cybersecurity in adversarial attacks ( Figure 25 ).

Recommendation categories for networks and cybersecurity in adversarial attacks.

5.3.1 Defense and protection against adversarial attacks

This category addresses the difficulties and potential of using ML in adversarial environments, such as malware detection, network intrusion detection, and failure prediction. In Shaukat et al. [ 72 ], the authors envisaged several future research directions. For instance, it would be useful to investigate the performance of DL models other than the models proposed by Shaukat et al. [ 72 ] in adversarial settings. In addition, it would also be useful to examine the robustness of malware detectors trained against other evasion attacks. The proposed model by Sharma et al. [ 74 ] can be applied to other datasets with imbalanced class labels, and GANs can be adopted to generate traffic from minority attacks in the dataset. In addition, other minority attack classes can be categorized into new DNN-based classifiers with increased accuracy and reduced loss in terms of false negative (FN) and false positive (FP) predictions. Duy et al. [ 92 ] recommended conducting the framework on more datasets with diverse modern attacks, considering other more complex detection algorithms, and using other GAN formulations for training improvement. Ardito et al. [ 110 ] highlighted that defending these systems against alternative adversarial training and detection techniques would require more nuanced and in-depth research, which they hope to pursue in the future work. Another interesting future direction is to consider the privacy of fault-prediction systems such that separate zones do not need to exchange their data with a central server.

Chen et al. [ 98 ] explored other potential causes of catastrophic overfitting and discovered more properties of adversarial examples in DNNs. Wan et al. [ 101 ] recommended simultaneously improving the learning efficiency of non-independent and identically distributed (NIID) dataset data and further improving the robustness of the adversarial training process. Roshan et al. [ 106 ] explored the implications of adopting the described method on other kinds of ML and DL architectures. In the same context, there is a possibility to examine the transferability concept in the field of adversarial ML, where the approach that works in one case can also be developed for other purposes. The method should be used in the series in the same way, which also addresses the concept of drift network streaming data-based NIDS system.

On the other hand, in the ever-changing arena of adversarial attacks, forthcoming studies need to delve into the consequences of emerging attack methodologies, encompassing white-box, black-box, and gray-box approaches [ 99 ]. Xie and Chen [ 108 ] focused on mitigating constraints by exploring methods to enhance model resilience, decrease computational intricacy, and augment detection capabilities tailored to particular attack types. Nkoro et al. [ 86 ] recommended exploring the computational complexities of the SHAP and LIME XAI methods and investigating the integration of an explainable adversarial protection mechanism to fortify the NIDS against potential DNN adversarial attacks. Albahri et al. [ 112 ] moved to a stage that demands rigorous testing and validation. This involved the setting of boundaries of operation, such as attacks and examination of frameworks regarding stability. The performance metrics of framework development should focus on issues such as the amount of data and model complexity, and the real-world benefit of the implemented framework should also be evaluated. Sharma et al. [ 100 ] introduce a hybrid approach for Windows Malware Classification. Despite this, an area that is still under debate is the reliability of the model against adversarial attacks. In addition, the model should be checked for the ability to identify malware created for other platforms, such as Android or Linux [ 100 ]. A study conducted by Hamouda et al. [ 104 ] suggested exploring ensemble learning methods for collective decision-making and self-supervised learning techniques to improve the capabilities of generative models. Further investigations can focus on developing complex feature-selecting methods that perceive subtle attack strategies to be detected by the model. In addition, the model can also evolve to be more efficient, especially in real-time detection scenarios, which is another area that needs to be emphasized [ 105 ]. Finally, van Geest et al. [ 80 ] proposed an extended duration trial of the framework including more models, complex bypass simulations, and the integration of existing models to refine the hybrid design. There is a need for broader investigations into ML model performance in adversarial settings, including robustness against various evasion attacks and diverse attack types.

5.3.2 Data generation and augmentation

The studies in this category are concerned with the methods and use cases of creating artificial or enhanced data employing methods such as RL and GANs. Gungor et al. [ 82 ] were the first to plan to add black-box attack methods that do not have any knowledge about the attacked models. Duy et al. [ 111 ] recommended RL by being able to learn the behaviour of modifying network traffic; hence, this approach can be used to obtain adversarial network traffic at the byte level. The suggestion put forward by Mishra et al. [ 76 ] involves integrating temporal features and improving benchmark datasets. In addition, there are plans to enhance the proposed model by integrating federated learning methodologies, which can address concerns regarding data confidentiality and privacy more effectively. Alzahrani and Asghar [ 83 ] aimed to assess its performance in the future using more datasets. (ii) In addition to chi-squared analysis, they investigated additional feature selection techniques. (iii) Currently, each attack class is categorized separately. (iv) Furthermore, researchers looking into logistics-based IoT vulnerability screening may find it helpful to use the BoT-IoT dataset in combination with hybrid DL. However, there is a need for further refinement and diversification in data generation and augmentation methods.

5.3.3 IoT security and analysis

This category includes the papers that address the security and analysis of IoT devices and systems – malware, threat case generation, framework design, and log analysis. Imran et al. [ 81 ] recommended that future work aims at developing use cases based on different log outputs and deducing exact results by using ML algorithms that will remove false positives. The proposed framework by Mirsky et al. [ 95 ] has the potential to provide IoT manufacturers with a cheap and effective solution. They hope that this framework, and its variants, will assist researchers and the IoT industry in securing the future of the IoT. Bai et al. [ 97 ] evaluate the developed approach to other session-based protocols, such as Secure Shell. In addition, Windows event logs contain a variety of event types that can be leveraged to identify different stages of an APT (advanced persistent threat) attack. Finally, in the study by Falana et al. [ 73 ], MalDetect was tested against larger datasets, and the proposed framework was integrated into an IoT-based system for assessment and precision. In the study by Ahmad and Shah [ 103 ], further assessments were conducted to evaluate the computational complexity and effectiveness of the proposed model across various IoT application scenarios. In addition, they recommend exploring the implementation of an efficient data compression technique to minimize the volume of data transmitted to a cloud data centre in a federated learning framework. These studies need more comprehensive evaluation and practical implementation of the proposed frameworks in real-world IoT environments. Recommendations include developing use-case scenarios based on different log outputs, refining frameworks to ensure affordability and effectiveness for IoT manufacturers, and conducting further assessments to evaluate computational complexity and effectiveness across various IoT application scenarios. In addition, suggestions involve exploring efficient data compression techniques to minimize data transmission volumes in federated learning frameworks. However, there is a lack of discussion on potential challenges and limitations that may arise during implementation and operation in diverse IoT settings.

5.4 Gaps, open issues, and some innovative key solutions

Constructive criticism within a literature review serves as a crucial component of scholarly discourse, offering a balanced assessment of the strengths and weaknesses of the existing body of literature. By critically evaluating the methodologies, findings, and theoretical frameworks employed in previous studies, researchers can identify gaps and opportunities for advancement within their field of study. In this context, constructive criticism is not intended to disparage or undermine the contributions of previous researchers but rather to foster intellectual growth and promote academic rigor. It offers an opportunity to reflect on the limitations of existing research, thereby laying the groundwork for more robust and comprehensive investigations in the future. Moreover, constructive criticism can stimulate dialog and collaboration among researchers, encouraging them to exchange ideas and perspectives in the pursuit of shared research goals. By fostering an environment of constructive critique, scholars can collectively contribute to the advancement of knowledge and the development of innovative solutions to pressing challenges. Therefore, constructive criticism in a literature review plays a pivotal role in shaping the trajectory of academic inquiry, and guiding researchers toward more nuanced, inclusive, and impactful research agendas. It serves as a catalyst for intellectual growth, challenging researchers to critically assess existing paradigms and explore new avenues for exploration and discovery.

This section attempts to map the gaps within the current scholarly discussion to provide a platform for further exploration by upcoming researchers. Within each subtopic given, the description revolves around the mentioned gaps, highlighting the situation where networks and cybersecurity are interrelated, especially when they follow adversarial attacks.

As a general outline, the issue of the application of defense for networks and cybersecurity in adversarial attacks seems to be a very hot and fertile topic within the corresponding academic field. Comparing the number of published articles for the past 4 years with those of the first quarter of 2024 indicates a substantial and rather remarkable increase, as shown in Figure 26 . This influx of publications is therefore a great testimony to the fact that the field is increasingly gaining significance and is expected to have a substantive impact. This manifestation reflects increased scholarly interest and important breakthroughs and achievements in this sphere.

Comparison of the number of published studies for the past 4 years with the first quarter of the year 2024.

An overview of such recent advances in networks and cybersecurity as adversarial attacks is presented in the following subsections with significant tables and analyses.

5.4.1 Available datasets

This approach contributes to the basic function of training AI models in the case of adversarial attacks [ 114 ]. In particular, within the adversarial attack setting, the dataset turns out to be fundamental for identifying subtleties and for pinpointing possible dangers. This impedes the evaluation of model applicability and generalizability due to the lack of specific details on the datasets, as is evident from the literature review. The nature of the data type, size, composition, and aspects specific to the training and test datasets are pivotal parts of the data used to evaluate the robustness of adversarial attacks in AI models ( Table 1 ).

The dataset of networks and cybersecurity in adversarial attacks

Ref.	Dataset name	Dataset size	Dataset link (if available)	Public or private
[ ]	VirusShare	19,000		Public
[ ]	VXHeaven	19,000		Public
[ ]	UNSW-NB15	257,673		Public
[ ]	NSL-KDD	—		Public
[ ]	CICIDS2017	170,360		Public
[ ]	—	—	—	Private
[ ]	SWaT	496,800		Public
[ ]	WADI	449,909		Public
[ ]	Cora	2,708	—	Public
	Citeseer	3,264
	Wiki	2,363
	CA-GrQc	5,242
	CA-HepT	9,877
[ ]	CICIDS2017	As explained in the study of [ ]
[ ]	NSL-KDD	As explained in the study of [ ]
[ ]	CICIDS2018	—		Public
[ ]	NSL-KDD	—		Private
	UNSW-NB15	—		Private
	CICIDS2017	As explained in the study of [ ]
[ ]	MNIST and GTSRB	70,000, 51,839	,	Public
[ ]	LANL	222,692	—	Public
[ ]			—	Private
[ ]	MaleVis, Mallmg, and Virushare	2,000, 1,744, and 9,339	—	Private
[ ]	NASA C-MAPSS and UNIBO Powertools	—		Public
[ ]	CICIDS-2017	As explained in the study of [ ]
[ ]	CICIDS2018 and InSDN	645,669	,	Public
[ ]	MNIST and FashionMNIST	70,000		Public
[ ]	GTSRB and CIFAR10	47,429 and 60,000	,	Public
[ ]	BoT-IoT	73 million samples and 80 features		public
	CIC-IDS-2017	As explained in the study of [ ]
	CIC-IDS-2018	As explained in the study of [ ]
[ ]	BOT-IoT, IoT-23, UNSWNB15, and ToN-IoT	—		Public


[ ]	CIC-DDoS-2019	107,764	—	Private
[ ]	BOT-IoT	As explained in the study of [ ]
[ ]	CIC-IDS2018 CIC-DDoS2019	As explained in the study of [ ], As explained in the study of [ ]
[ ]	ToN-IoT	As explained in the study of [ ]
[ ]	EdgeIIoT, CICIoT, and UNSW-NB15	—	, It was explained above in [ ]	Public
[ ]	Two DSRC datasets	390		Public
[ ]	BoT-IoT, UNSW-NB15, and N-BaIoT	—	(BoT-IoT, and UNSW-NB15) was explained above in [ ],	Public
[ ]	Edge-IIoTset	—		Public
[ ]	TON-IoT	As explained in the study of [ ]
[ ]	WSN-DS CIC-IDS-2017	3,74,661	, As explained in the study of [ ]	Public
[ ]	EMBER	1,100,000		Public
[ ]	—	—		Public

The validity and homogeneity of the training data are further emphasized, which is what validation provides, as well as the depiction of reality and consistency for successful generalization across a variety of cases [ 115 ]. It is critical to ensure transparency in the revelation of the source of the dataset, whether it is available from public sources or has been collected in the field of research on adversarial attacks; the replicability and validity of research findings depend on it.

The total number of datasets deployed throughout all the experiments is 37. The total number of datasets that are used for the development of taxonomy-related portions of the study attests to the fact that many of them still deserve further analysis. The high number of datasets applied and the discrepancy in the quantity of the studies being conducted throughout this period were remarkable because for the period from 2020 to 2023, there were 23 studies, and during only the first quarter of 2024, 19 studies were conducted. For the sake of deriving future research options in the context of past analysis, we performed an intersection analysis taking the two aforementioned intervals ( Figure 27 ).

The datasets used for the period from 2020–2023 vs the datasets used in the first quarter of 2024.

Such meticulous observations are graphically represented in Figures 28 and 29 , as they are a library of datasets for future studies. Through this visualization, the first figure shows the correlation between the datasets used in the research from 2020 to 2023, while the second figure shows the datasets used for the first quarter of 2024. Therefore, these graphs highlight the pursuing trend for further research in this specific sector. This connection shows datasets that at times are scanned across multiple studies, indicating their ongoing value and appropriateness for more research in the future.

Datasets used vs unused for the period from 2020 to 2023.

Datasets used vs unused datasets in the first quarter of 2024.

The examination of the datasets utilized versus those left unused in comparison with the first quarter of 2024 revealed a microcosm of the evolution of the cybersecurity research landscape. Initially, researchers mainly used available datasets such as NSL-KDD, CICIDS2017, and UNSW-NB15 to investigate various branches of intrusion detection, anomaly detection in IoT networks, and attacks for ML models. On the other hand, the transition of dataset utilization to more complex and specific data is evident in 2024. This suggests that people are beginning to acknowledge that more different and particular data may have to be used in addressing emerging cybersecurity challenges. This transition implies a shift toward more advanced implementation in the field, as researchers have reviewed and explored other advanced methods, such as federated learning, quantum-based strategies, and ensemble methods, for the purpose of boosting the reliability and resiliency of cybersecurity systems. To fill the gap between the dataset used and that unused and solve the challenges in the taxonomy, the vital solutions include the promotion of the diversity of the dataset that will be achieved by collaboration with industrial partners and cybersecurity organizations, standardizing the protocol for the sharing of the dataset that would stimulate the use, leveraging synthetic data generation techniques to supplement the existing datasets, and conducting longitudinal studies to observe the changes in cyber threats. On the one hand, researchers can apply these solutions to circumvent the complexities of modern cybersecurity scape, and on the other hand, they can develop efficient measures to counter emerging cyber threats.

5.4.2 ML/DL techniques

ML and DL techniques provide many benefits but also pose considerable difficulties. Fundamentally, it has been proven that they can adequately perform image classification, object recognition, and natural language data processing [ 116 ], the latter of which is authenticated by progress in data representation [ 117 ]. In ML [ 118 ], transferring data from models trained with training sets to other models is an interesting topic. However, one of the primary challenges is obtaining the large amounts of qualitative data needed for training these ML algorithms [ 119 , 120 ]. It takes time and money to collect, label, and annotate the data [ 121 ]. Moreover, the ethical implications, possible biases, and consequences of AI-driven content call for a thorough assessment, especially in the fields of NLP, computer vision, and image analysis [ 122 , 123 , 124 ].

However, during ML thinking, there are many security threats that ML and DL face [ 8 ]. As illustrated, attackers have a massive incentive to alter the results of ML and DL model outputs or acquire confidential information for their benefit [ 25 ]. The analysis was carried out for networks and cybersecurity in adversarial attacks, and based on the algorithms given in Table 2 , we considered this unique feature to define gaps in the use of ML and DL techniques in the literature. Consequently, one of our contributions is identifying algorithms that have not hitherto been researched as such subjects for separate studies, constituting unique classifications and inquiries.

ML and DL technique contributions of networks and cybersecurity in adversarial attacks

Ref.	Methods	Metrics
[ ]	NN	N/M
[ ]	CNN and GAN	Accuracy
[ ]	DNN, GAN	Accuracy
[ ]	RF, MLP, SVM, AB, SGD, GBC, RNN, GRU, LSTM	Precision, Recall, 1-score, Accuracy
[ ]	RNN, LSTM, BLSTM, GRU, BGRU, CNN, WAVE, CLSTM, CGRU, and GLSTM	Mean
[ ]	RNN	Precision, Recall, 1-score, Accuracy
[ ]	ANN, SVM, KNN, DT, Thresh	Accuracy, Recall, 1-score, Precision
[ ]	AdvT, DEEPWALK, LINE, and node2vec	Accuracy
[ ]	ANN	Precision, Recall, 1-score, Accuracy
[ ]	GAN	N/M
[ ]	GAN	N/M
[ ]	CNN, CapsNet, and SCN	Accuracy
[ ]	LR, DT, FNN, GNB, RF, LB, and LGBM	Accuracy, Precision, Recall, and 1-score
[ ]	Projected Gradient Descent (PGD)	Accuracy
[ ]	DP-WGAN.	Accuracy
[ ]	DNN	Accuracy
[ ]	FGSM, JSMA, PGD and C&W	Accuracy, Precision, Recall, 1-score, and False Positive Rate
[ ]	SOCNN, LOF, and INNE	UDR, Accuracy, TPR, FPR, and 1-score
[ ]	MLP	N/M
[ ]	DT, LR, CNN, MLP, and LSTM	Accuracy, DR, and 1-score
[ ]	CNN	N/M
[ ]	DCGAN + Bi-LSTM	Precision, Recall, 1-score, Accuracy
[ ]	Neural Network	Precision, Recall, 1-score, Accuracy
[ ]	LGBM, RF, ET, CNN, DNN	Precision, Recall, 1-score
[ ]	CNN, LSTM, LSTM + CNN, BiLSTM, LSTM-CNN, CNN-RNN, LSTM-CNN	Precision, Recall, 1-score, Accuracy
[ ]	CSNN	Precision, Recall, 1-score, Accuracy
[ ]	MLP	Precision, Recall, 1-score, Accuracy
[ ]	SVM, KNN, NB, Quantum Hybrid Voting	Precision, Recall, 1-score, Accuracy
[ ]	DNN	Precision, Recall, 1-score, Accuracy
[ ]	LightGBM, LSTM, MLP, SVM, RF, and KNN.	Precision, Recall, 1-score, Accuracy
[ ]	KNN, RF, GB, MLPNN, SGD, SVM, LSTM, XBOOST, DBSCAN	Precision, Recall, 1-score, Accuracy
[ ]	RF, GB, KNN, NN, SGD, SVM	Precision, Recall, 1-score, Accuracy
[ ]	CNN	Accuracy
[ ]	GA-LSTM	Accuracy, DR, Precision, Sensitivity, False Alarm, Training time
[ ]	GAN	Recall, Fpr, Fnr, and Accuracy
[ ]	SVM, NB, MLP, DQN, AE-RL, AE-Dueling, DQN RIoT	Precision, Recall, 1-score, Accuracy, G-Mean
[ ]	SCNN-Bi-LSTM	Precision, Recall, 1-score, Accuracy
[ ]	BERT, CNN, LSTM	1-score, Accuracy, Loss
[ ]	DT, RF, LR	Precision, Recall, 1-score, Accuracy, ROC AUC

N/M: not mentioned.

This study incorporated 62 different methods used in all the contributions. By comparing the presented taxonomy with how frequently each of these methods was employed in papers, it is revealed that there are plenty of avenues for future study. The remarkable techniques illustrated by the green accent in Figure 30 are explained below.

The methods of ML and DL used for the period from 2020 to 2023 vs the methods used in the first quarter of 2024.

Figure 31 shows an overview of the interlinkages between the methods applied by researchers in the period of 2020–2023 and shows the methods employed in the quarter of 2024 (1st Q 2024). Figures 31 and 32 expand on this idea, showing a list of methods previously used in research papers and methods that are not yet used (for the future). These graphical illustrations serve more as the result of an ongoing investigation within this given sector, with an emphasis on where further research may lead.

Methods of ML used vs unused for the period from 2020 to 2023.

Methods of ML used vs unused in the first quarter of 2024.

By comparing the ML and DL methods employed in 24 papers (2020–2023) to the methods used in 42 papers (first quarter of 2024), it is clear that there have been significant shifts and advancements in cybersecurity research methodology. At the beginning of ML evolution, researchers mostly worked with classical algorithms such as decision trees, random forests, support vector machines (SVMs), and k-nearest neighbours (KNNs) to design IDSs, anomaly detection models, and malware detectors, but now, these algorithms have been revolutionized by the latest DL approaches. While traditional methods, such as supervised learning and simple approaches, have been in use for a long time, recent developments in the realm of DL techniques, especially DNNs, CNNs, and recurrent neural networks (RNNs), have led to the emergence of more advanced and data-driven solutions. In 2024, DL is booming, while various cutting-edge approaches, including adversarial autoencoding, GANs, and federated learning, are increasingly adopted in cybersecurity research to move the direction toward resilient security systems. In addition, hybrids dominate, which use the possibility of both logic and DL as the most important techniques. This change is symbolic of the overall event of capitalizing on the potential of DL to solve sophisticated cybersecurity problems through techniques such as malware detection, adversarial signals, and network nastiness detection. DL methodologies will help researchers manoeuvre dynamically and develop better approaches to fight cyber threats, which will continue to evolve in the future.

5.4.3 New insights into cybersecurity

The future applications of defense networks and cybersecurity concerning adversarial attacks span a wide range. These include benchmarking DL systems, malware detectors, and other models to study their operational variability in adversarial settings and devising new classifiers for higher precision by increasing the rationality of the decision conditional on representational evidence while generating threat cases from several log sources based on ML accuracy. Moreover, researchers also seek to widen the datasets for diverse purposes and investigate complicated detection algorithms as well as utilize several formulations of GANs that are designed to be used only during training. The scope also includes supplying cost-efficient IoT security systems, combining semantic tree neural networks and survey approaches over various protocols as well as event logs. In addition, goals are set to address overfitting in DNNs and software acceleration for non-independent and identically distributed dataset optimization of learning rate tests against larger datasets. In pursuit of realism, researchers have aimed to consider black-box attack methods and study transferability within adversarial ML and DL. The use of RL in GAN-based attack generation and walking on federated learning-based IDSs contributes to expanding the research scope. In general, the focus is on handling a broad spectrum of attacks and compromising false alarms under good restoration conditions in different adversary settings.

6 Conclusion

In this systematic study, comprehensive network and cybersecurity analysis related to attacks from opponents was conducted on adversarial attacks. The study clarified different security mechanisms, vulnerability types, and tactics to fight the ever-changing cyber threat landscape of an organization. Examination, however, showed that special attention to temporal parameters was the main feature of the analysis. The defense of network and cybersecurity systems, which is integrally related to digital sphere safety against threats, has come to light. Adversaries are prone to continuously tweaking their tactics; therefore, the need for something responsive and adaptable becomes increasingly critical.

The principle element of our analysis emphasizes the interactive dynamics between attackers and defenders, where it is a never-ending battle similar to that of cats and mice. To successfully counter the constantly increasing dangers, cybersecurity regulations should evolve at the same pace as adversary innovations and methodical enhancements. Deciphering this intricate link is crucial for shaping efficient countermeasures that will be strong enough to impede adversary penetrations.

This review emphasizes the vital role that DL and ML techniques play in cybersecurity. Organizations can directly identify and manage hazards before they occur by applying the predictive abilities of ML algorithms. However, implementing ML-based security mechanisms comes with its own set of challenges, such as adversarial attacks, through which the elements of fraud in ML models are identified.

Overall, this complete analysis presents a unique tool for understanding how networks and cybersecurity operate during hostile attacks. We strive to strengthen security skills and lessen the overall negative impact of adversary operatives in the digital domain. The state of the art and related issues are revealed, as well as the parameters of innovation are indicated. The imperative of fostering cooperation and proactivity in response to the dynamically evolving cybersecurity landscape is emphasized in this study. In this regard, there are general and specific limitations to the study at hand. From the specific dimension, the information fails to describe the investigation of the parameter types and nature in the development of the adversarial ML/DL techniques. In contrast, the scope of the analysis is specifically narrowed down to the applications of defense in network and cybersecurity against adversarial attacks. Under this context, the results cannot be generalized to other areas or uses.

Funding information: The author states no funding involved.

Author contributions: Yahya Layth Khaleel: data curation, writing – original draft preparation, and supervision. Mustafa Abdulfattah Habeeb: visualization, investigation, and supervision. A.S. Albahri: conceptualization, methodology, supervision, and editing. Tahsien Al-Quraishi: writing – reviewing and editing. O. S. Albahri: reviewing and editing. A. H. Alamoodi: writing – reviewing and editing.

Conflict of interest: The authors declare no conflict of interest.

Data availability statement: Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

[1] Morgan S. Humans on the internet will triple from 2015 to 2022 and hit 6 billion. Cybercrime Magazine, July 18. https://cybersecurityventures.com/how-many-Internet-users-will-the-world-have-in-2022-and-in-2030/. Search in Google Scholar

[2] Macas M, Wu C, Fuertes W. Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems. Expert Syst Appl. 2024 Mar;238:122223. 10.1016/j.eswa.2023.122223 . Search in Google Scholar

[3] Taye MM. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers. 2023 Apr;12(5):91. 10.3390/computers12050091 . Search in Google Scholar

[4] Alzubaidi L, Salhi A, Fadhel MA, Bai J, Hollman F, Italia K, et al. Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images. PLoS One. 2024;19(3):e0299545. 10.1371/journal.pone.0299545 . Search in Google Scholar PubMed PubMed Central

[5] Al-qaysi ZT, Albahri AS, Ahmed MA, Salih MM. Dynamic decision-making framework for benchmarking brain–computer interface applications: a fuzzy-weighted zero-inconsistency method for consistent weights and VIKOR for stable rank. Neural Comput Appl. 2024;36(17):10355–78. 10.1007/s00521-024-09605-1 . Search in Google Scholar

[6] Al-Qaysi ZT, Albahri AS, Ahmed MA, Mohammed SM. Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery. Phys Eng Sci Med. 2023;46(4):1519–34. 10.1007/s13246-023-01316-6 . Search in Google Scholar PubMed

[7] Dixit P, Silakari S. Deep learning algorithms for cybersecurity applications: a technological and status review. Comput Sci Rev. 2021 Feb;39:100317. 10.1016/j.cosrev.2020.100317 . Search in Google Scholar

[8] Sharaf IM, Alamoodi AH, Albahri OS, Deveci M, Talal M, Albahri AS, et al. Architecture selection for 5G-radio access network using type-2 neutrosophic numbers based decision making model. Expert Syst Appl. 2024;237:121420. 10.1016/j.eswa.2023.121420 . Search in Google Scholar

[9] Albahri AS, Jassim MM, Alzubaidi L, Hamid RA, Ahmed MA, Al-Qaysi ZT, et al. A trustworthy and explainable framework for benchmarking hybrid deep learning models based on chest X-Ray analysis in CAD systems. Int J Inf Technol Decis Mak. 2024;1–54. 10.1142/S0219622024500019 . Search in Google Scholar

[10] Zhang C, Patras, Haddadi H. Deep learning in mobile and wireless networking: a survey. IEEE Commun Surv Tutor. 2019;21(3):2224–87. 10.1109/COMST.2019.2904897 . Search in Google Scholar

[11] Abdulsattar NF, Alsalamy A, Shayea GG. Ubiquitous trust management and power optimization for UAV assisted mobile communication. Mesopotamian J Cybersecur. 2022;2022:560–71. 10.58496/MJCS/2022/003 . Search in Google Scholar

[12] Muhsen DH, Haider HT, Al-Nidawi Y, Shayea GG. Operational scheduling of household appliances by using triple-objective optimization algorithm integrated with multi-criteria decision making. Sustainability. 2023;15(24):16589. 10.3390/su152416589 . Search in Google Scholar

[13] Jagielski M, Oprea A, Biggio B, Liu C, Nita-Rotaru C, Li B. Manipulating machine learning: poisoning attacks and countermeasures for regression learning. In 2018 IEEE Symposium on Security and Privacy (SP); 2018 May. p. 19–35. 10.1109/SP.2018.00057 . Search in Google Scholar

[14] Team TCR. The U.S. threat landscape in 2023. Cyberint; 2023. Search in Google Scholar

[15] Najar AA, Naik SMa. Cyber-Secure SDN: A CNN-based approach for efficient detection and mitigation of DDoS attacks. Comput Secur. 2024 Apr;139:103716. 10.1016/j.cose.2024.103716 . Search in Google Scholar

[16] Mishra A, Alzoubi YI, Anwar MJ, Gill AQ. Attributes impacting cybersecurity policy development: An evidence from seven nations. Comput Secur. 2022 Sep;120:102820. 10.1016/j.cose.2022.102820 . Search in Google Scholar

[17] Devabhakthini P, Parida S, Shukla RM, Nayak SC. Analyzing the impact of adversarial examples on explainable machine learning. arXiv Prepr arXiv230708327; 2023. Search in Google Scholar

[18] Chakraborty A, Alam M, Dey V, Chattopadhyay A, Mukhopadhyay D. A survey on adversarial attacks and defences. CAAI Trans Intell Technol. 2021;6(1):25–45. 10.1049/cit2.12028 Search in Google Scholar

[19] Reshmi TR. Information security breaches due to ransomware attacks - a systematic literature review. Int J Inf Manag Data Insights. 2021;1(2):100013. 10.1016/j.jjimei.2021.100013 . Search in Google Scholar

[20] Biggio B, Roli F. Wild patterns: ten years after the rise of adversarial machine learning. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. 2018. p. 2154–6. 10.1145/3243734.3264418 . Search in Google Scholar

[21] Pierazzi F, Pendlebury F, Cortellazzi J, Cavallaro L. Intriguing properties of adversarial ML attacks in the problem space. 2020 IEEE Symposium on Security and Privacy (SP). 2020. p. 1332–49. 10.1109/SP40000.2020.00073 . Search in Google Scholar

[22] Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, et al. Intriguing properties of neural networks. 2nd International Conference on Learning Representations, ICLR 2014 – Conference Track Proceedings. Search in Google Scholar

[23] Qiu S, Liu Q, Zhou S, Wu C. Review of artificial intelligence adversarial attack and defense technologies. Appl Sci. 2019;9(5):909. 10.3390/app9050909 . Search in Google Scholar

[24] Xue M, Yuan C, Wu H, Zhang Y, Liu W. Machine learning security: threats, countermeasures, and evaluations. IEEE Access. 2020;8:74720–42. 10.1109/ACCESS.2020.2987435 . Search in Google Scholar

[25] Wang C, Chen J, Yang Y, Ma X, Liu J. Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects. Digit Commun Netw. 2022;8(2):225–34. 10.1016/j.dcan.2021.07.009 . Search in Google Scholar

[26] Gao Y, Doan BG, Zhang Z, Ma S, Zhang J, Fu A, et al. Backdoor attacks and countermeasures on deep learning: A comprehensive review. arXiv Prepr. arXiv2007.10760; 2020. Search in Google Scholar

[27] Al Jaberi SM, Patel A, AL-Masri AN. Object tracking and detection techniques under GANN threats: A systemic review. Appl Soft Comput. 2023;139:110224. 10.1016/j.asoc.2023.110224 . Search in Google Scholar

[28] Rieck K, Trinius P, Willems C, Holz T. Automatic analysis of malware behavior using machine learning. J Comput Secur. 2011;19(4):639–68. 10.3233/JCS-2010-0410 Search in Google Scholar

[29] Handa A, Sharma A, Shukla SK. Machine learning in cybersecurity: A review. Wiley Interdiscip Rev Data Min Knowl Discov. 2019;9(4):e1306. 10.1002/widm.1306 Search in Google Scholar

[30] Chen B, Ren Z, Yu C, Hussain I, Liu J. Adversarial examples for cnn-based malware detectors. IEEE Access. 2019;7:54360–71. 10.1109/ACCESS.2019.2913439 Search in Google Scholar

[31] John TS, Thomas T. Adversarial attacks and defenses in malware detection classifiers. Handbook of research on cloud computing and big data applications in IoT. IGI global; 2019. p. 127–50. 10.4018/978-1-5225-8407-0.ch007 . Search in Google Scholar

[32] Zhu J-Y, Park T, Isola, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. 2017 IEEE International Conference on Computer Vision (ICCV); 2017. p. 2242–51. 10.1109/ICCV.2017.244 . Search in Google Scholar

[33] Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017. p. 5967–76. 10.1109/CVPR.2017.632 . Search in Google Scholar

[34] Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, et al. Photo-realistic single image super-resolution using a generative adversarial network. Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017. p. 105–14. 10.1109/CVPR.2017.19 . Search in Google Scholar

[35] Yaseen MG, Albahri AS. Mapping the evolution of intrusion detection in big data: a bibliometric analysis. Mesopotamian J Big Data. 2023;2023:138–48. 10.58496/mjbd/2023/018 . Search in Google Scholar

[36] Yi T, Chen X, Zhu Y, Ge W, Han Z. Review on the application of deep learning in network attack detection. J Netw Comput Appl. 2023;212:103580. 10.1016/j.jnca.2022.103580 . Search in Google Scholar

[37] Thajeel IK, Samsudin K, Hashim SJ, Hashim F. Machine and deep learning-based XSS detection approaches: a systematic literature review. J King Saud Univ – Comput Inf Sci. 2023;35(7):101628. 10.1016/j.jksuci.2023.101628 . Search in Google Scholar

[38] Aldhaheri A, Alwahedi F, Ferrag MA, Battah A. Deep learning for cyber threat detection in IoT networks: A review. Internet Things Cyber-Phys Syst. 2024;4:110–28. 10.1016/j.iotcps.2023.09.003 . Search in Google Scholar

[39] He K, Kim DD, Asghar MR. Adversarial machine learning for network intrusion detection systems: a comprehensive survey. IEEE Commun Surv Tutor. 2023;25(1):538–66. 10.1109/COMST.2022.3233793 . Search in Google Scholar

[40] Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A. The limitations of deep learning in adversarial settings. 2016 IEEE European Symposium on Security and Privacy (EuroS&P); 2016. p. 372–87. 10.1109/EuroSP.2016.36 . Search in Google Scholar

[41] Shokri R, Stronati M, Song C, Shmatikov V. Membership inference attacks against machine learning models. 2017 IEEE Symposium on Security and Privacy (SP); 2017. p. 3–18. 10.1109/SP.2017.41 . Search in Google Scholar

[42] Papernot N, McDaniel P, Wu X, Jha S, Swami A. Distillation as a defense to adversarial perturbations against deep neural networks. 2016 IEEE Symposium on Security and Privacy (SP); 2016. p. 582–97. 10.1109/SP.2016.41 . Search in Google Scholar

[43] Dong Y, Liao F, Pang T, Su H, Zhu J, Hu X, et al. Boosting adversarial attacks with momentum. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2018. p. 9185–93. 10.1109/CVPR.2018.00957 . Search in Google Scholar

[44] Akhtar N, Mian A. Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access. 2018;6:14410–30. 10.1109/ACCESS.2018.2807385 . Search in Google Scholar

[45] Su J, Vargas DV, Sakurai K. One pixel attack for fooling deep neural networks. IEEE Trans Evol Comput. 2019;23(5):828–41. 10.1109/TEVC.2019.2890858 . Search in Google Scholar

[46] Fawzi H, Tabuada, Diggavi S. Secure estimation and control for cyber-physical systems under adversarial attacks. IEEE Trans Autom Contr. 2014;59(6):1454–67. 10.1109/TAC.2014.2303233 . Search in Google Scholar

[47] Eykholt K, Evtimov I, Fernandes E, Li B, Rahmati A, Xiao C, et al. Robust physical-world attacks on deep learning visual classification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. vol. 2018; 2018. p. 1625–34. 10.1109/CVPR.2018.00175 . Search in Google Scholar

[48] Yuan X, He P, Zhu Q, Li X. Adversarial examples: attacks and defenses for deep learning. IEEE Trans Neural Netw Learn Syst. 2019;30(9):2805–24. 10.1109/TNNLS.2018.2886017 . Search in Google Scholar PubMed

[49] Nasr M, Shokri R, Houmansadr A. Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. 2019 IEEE Symposium on Security and Privacy (SP). 2019. p. 739–53. 10.1109/SP.2019.00065 . Search in Google Scholar

[50] Xie C, Wang J, Zhang Z, Zhou Y, Xie L, Yuille A. Adversarial examples for semantic segmentation and object detection. 2017 IEEE International Conference on Computer Vision (ICCV). 2017 Oct. p. 1378–87. 10.1109/ICCV.2017.153 . Search in Google Scholar

[51] Xie C, Zhang Z, Zhou Y, Bai S, Wang J, Ren Z, et al. Improving transferability of adversarial examples with input diversity. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2019. p. 2725–34. 10.1109/CVPR.2019.00284 . Search in Google Scholar

[52] Gu T, Liu K, Dolan-Gavitt B, Garg S. BadNets: evaluating backdooring attacks on deep neural networks. IEEE Access. 2019;7:47230–44. 10.1109/ACCESS.2019.2909068 . Search in Google Scholar

[53] Ruff L, Kauffmann JR, Vandermeulen RA, Montavon G, Samek W, Kloft M, et al. A unifying review of deep and shallow anomaly detection. Proc IEEE. 2021;109(5):756–95. 10.1109/JPROC.2021.3052449 . Search in Google Scholar

[54] Liao F, Liang M, Dong Y, Pang T, Hu X, Zhu J. Defense against adversarial attacks using high-level representation guided denoiser. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018 Jun. p. 1778–87. 10.1109/CVPR.2018.00191 . Search in Google Scholar

[55] Pan Z, Yu W, Yi X, Khan A, Yuan F, Zheng Y. Recent progress on generative adversarial networks (GANs): A survey. IEEE Access. 2019;7:36322–33. 10.1109/ACCESS.2019.2905015 . Search in Google Scholar

[56] Dong Y, Pang T, Su H, Zhu J. Evading defenses to transferable adversarial examples by translation-invariant attacks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019 Jun. p. 4307–16. 10.1109/CVPR.2019.00444 . Search in Google Scholar

[57] Xie C, Wu Y, van der Maaten L, Yuille AL, He K. Feature denoising for improving adversarial robustness. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019 Jun. p. 501–9. 10.1109/CVPR.2019.00059 . Search in Google Scholar

[58] Sohrabi C, Franchi T, Mathew G, Kerwan A, Nicola M, Griffin M, et al. PRISMA 2020 statement: What’s new and the importance of reporting guidelines. Int J Surg. 2021;88:105918. 10.1016/j.ijsu.2021.105918 . Search in Google Scholar PubMed

[59] Khaw KW, Alnoor A, Al-Abrrow H, Tiberius V, Ganesan Y, Atshan NA. Reactions towards organizational change: a systematic literature review. Curr Psychol. 2022;1–24. 10.1007/s12144-022-03070-6 Search in Google Scholar PubMed PubMed Central

[60] David D, Alamoodi AH, Albahri OS, Garfan S, Albahri AS, Zaidan BB, et al. Sign language mobile apps: a systematic review of current app evaluation progress and solution framework. Evol Syst. 2023;15(2):669–86. 10.1007/s12530-023-09494-0 . Search in Google Scholar

[61] Albahri AS, Duhaim AM, Fadhel MA, Alnoor A, Baqer NS, Alzubaidi L, et al. A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Inf Fusion. 2023;96:156–91. 10.1016/j.inffus.2023.03.008 . Search in Google Scholar

[62] Fadhel MA, Duhaim AM, Saihood A, Sewify A, Al-Hamadani MN, Albahri AS, et al. Comprehensive systematic review of information fusion methods in smart cities and urban environments. Inf Fusion. 2024;107:102317. 10.1016/j.inffus.2024.102317 . Search in Google Scholar

[63] Alamoodi AH, Garfan S, Al-Zuhairi O, Zaidan BB, Zaidan AA, Albahri OS, et al. Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis. Eng Appl Artif Intell. 2024;133:108023. 10.1016/j.engappai.2024.108023 . Search in Google Scholar

[64] Spring M, Faulconbridge J, Sarwar A. How information technology automates and augments processes: Insights from Artificial‐Intelligence‐based systems in professional service operations. J Oper Manag. 2022;68(6–7):592–618. 10.1002/joom.1215 Search in Google Scholar

[65] Peikert A. Towards transparency and Open Science: A principled perspective on computational reproducibility and preregistration. PhD Thesis. Humboldt-Universität zu Berlin; 2023. Search in Google Scholar

[66] Alsalem MA, Alamoodi AH, Albahri OS, Albahri AS, Martínez L, Yera R, et al. Evaluation of trustworthy artificial intelligent healthcare applications using multi-criteria decision-making approach. Expert Syst Appl. 2024;246:123066. 10.1016/j.eswa.2023.123066 . Search in Google Scholar

[67] Qin Y, Xu Z, Wang X, Skare M. Artificial intelligence and economic development: An evolutionary investigation and systematic review. J Knowl Econ. 2023;1–35. 10.1007/s13132-023-01183-2 . Search in Google Scholar

[68] Albahri OS, AlAmoodi AH. Cybersecurity and artificial intelligence applications: a bibliometric analysis based on scopus database. Mesopotamian J Cybersecur. 2023;158–69. 10.58496/MJCS/2023/018 . Search in Google Scholar

[69] Rusydiana AS. Bibliometric analysis of journals, authors, and topics related to COVID-19 and Islamic finance listed in the Dimensions database by Biblioshiny. Sci Ed. 2021;8(1):72–8. 10.6087/kcse.232 . Search in Google Scholar

[70] Jadeja M, Shah K. Tree-Map: A visualization tool for large data. GSB@ SIGIR. CEUR Workshop Proceedings. Vol. 1393; 2015. p. 9–13. Search in Google Scholar

[71] Muhsen YR, Husin NA, Zolkepli MB, Manshor N, Al-Hchaimi AAJ, Albahri AS. Routing techniques in network-on-chip based multiprocessor-system-on-chip for IOT: a systematic review. Iraqi J Comput Sci Math. 2024;5(1):181–204. 10.52866/ijcsm.2024.05.01.014 . Search in Google Scholar

[72] Shaukat K, Luo S, Varadharajan V. A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks. Eng Appl Artif Intell. 2022;116:105461. 10.1016/j.engappai.2022.105461 . Search in Google Scholar

[73] Falana OJ, Sodiya AS, Onashoga SA, Badmus BS. Mal-Detect: An intelligent visualization approach for malware detection. J King Saud Univ – Comput Inf Sci. 2022;34(5):1968–83. 10.1016/j.jksuci.2022.02.026 . Search in Google Scholar

[74] Sharma B, Sharma L, Lal C, Roy S. Anomaly based network intrusion detection for IoT attacks using deep learning technique. Comput Electr Eng. 2023;107:108626. 10.1016/j.compeleceng.2023.108626 . Search in Google Scholar

[75] Sethi K, Madhav YV, Kumar R, Bera P. Attention based multi-agent intrusion detection systems using reinforcement learning. J Inf Secur Appl. 2021;61:102923. 10.1016/j.jisa.2021.102923 . Search in Google Scholar

[76] Mishra AK, Paliwal S, Srivastava G. Anomaly detection using deep convolutional generative adversarial networks in the internet of things. ISA Trans. 2024;145:493–504. 10.1016/j.isatra.2023.12.005 . Search in Google Scholar PubMed

[77] Khan MH, Javed AR, Iqbal Z, Asim M, Awad AI. DivaCAN: Detecting in-vehicle intrusion attacks on a controller area network using ensemble learning. Comput Secur. 2024;139:103712. 10.1016/j.cose.2024.103712 . Search in Google Scholar

[78] Saheed YK, Abdulganiyu OH, Tchakoucht TA. Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities. Appl Soft Comput. 2024;155:111434. 10.1016/j.asoc.2024.111434 . Search in Google Scholar

[79] Rookard C, Khojandi A. RRIoT: Recurrent reinforcement learning for cyber threat detection on IoT devices. Comput Secur. 2024;140:103786. 10.1016/j.cose.2024.103786 . Search in Google Scholar

[80] van Geest RJ, Cascavilla G, Hulstijn J, Zannone N. The applicability of a hybrid framework for automated phishing detection. Comput Secur. 2024;139:103736. 10.1016/j.cose.2024.103736 . Search in Google Scholar

[81] Imran M, Siddiqui HUR, Raza A, Raza MA, Rustam F, Ashraf I. A performance overview of machine learning-based defense strategies for advanced persistent threats in industrial control systems. Comput Secur. 2023;134:103445. 10.1016/j.cose.2023.103445 . Search in Google Scholar

[82] Gungor O, Rosing T, Aksanli B. STEWART: stacking ensemble for white-box adversarial attacks towards more resilient data-driven predictive maintenance. Comput Ind. 2022;140:103660. 10.1016/j.compind.2022.103660 . Search in Google Scholar

[83] Alzahrani A, Asghar MZ. Cyber vulnerabilities detection system in logistics-based IoT data exchange. Egypt Inform J. 2024;25:100448. 10.1016/j.eij.2024.100448 . Search in Google Scholar

[84] Alshaikh O, Parkinson S, Khan S. Exploring perceptions of decision-makers and specialists in defensive machine learning cybersecurity applications: The need for a standardised approach. Comput Secur. 2024;139:103694. 10.1016/j.cose.2023.103694 . Search in Google Scholar

[85] Aurangzeb M, Wang Y, Iqbal S, Naveed A, Ahmed Z, Alenezi M, et al. Enhancing cybersecurity in smart grids: Deep black box adversarial attacks and quantum voting ensemble models for blockchain privacy-preserving storage. Energy Rep. 2024;11:2493–515. 10.1016/j.egyr.2024.02.010 . Search in Google Scholar

[86] Nkoro EC, Nwakanma CI, Lee J-M, Kim D-S. Detecting cyberthreats in Metaverse learning platforms using an explainable DNN. Internet Things. 2024;25:101046. 10.1016/j.iot.2023.101046 . Search in Google Scholar

[87] Aygul K, Mohammadpourfard M, Kesici M, Kucuktezcan F, Genc I. Benchmark of machine learning algorithms on transient stability prediction in renewable rich power grids under cyber-attacks. Internet Things. 2024;25:101012. 10.1016/j.iot.2023.101012 . Search in Google Scholar

[88] Jia Y, Wang J, Poskitt CM, Chattopadhyay S, Sun J, Chen Y. Adversarial attacks and mitigation for anomaly detectors of cyber-physical systems. Int J Crit Infrastruct Prot. 2021;34:100452. 10.1016/j.ijcip.2021.100452 . Search in Google Scholar

[89] Ahmed MM, Ganguly A, Vashist A, Pudukotai Dinakarrao SM. AWARe-Wi: A jamming-aware reconfigurable wireless interconnection using adversarial learning for multichip systems. Sustain Comput Inform Syst. 2021;29:100470. 10.1016/j.suscom.2020.100470 . Search in Google Scholar

[90] Dai Q, Shen X, Zheng Z, Zhang L, Li Q, Wang D. Adversarial training regularization for negative sampling based network embedding. Inf Sci (Ny). 2021;579:199–217. 10.1016/j.ins.2021.07.018 . Search in Google Scholar

[91] Pawlicki M, Choraś M, Kozik R. Defending network intrusion detection systems against adversarial evasion attacks. Futur Gener Comput Syst. 2020;110:148–54. 10.1016/j.future.2020.04.013 . Search in Google Scholar

[92] Duy PT, Tien LK, Khoa NH, Hien DTT, Nguyen AGT, Pham VH. DIGFuPAS: Deceive IDS with GAN and function-preserving on adversarial samples in SDN-enabled networks. Comput Secur. 2021;109:102367. 10.1016/j.cose.2021.102367 . Search in Google Scholar

[93] Onur F, Gönen S, Barışkan MA, Kubat C, Tunay M, Yılmaz EN. Machine learning-based identification of cybersecurity threats affecting autonomous vehicle systems. Comput Ind Eng. 2024;190:110088. 10.1016/j.cie.2024.110088 . Search in Google Scholar

[94] Chen J, Wu D, Zhao Y, Sharma N, Blumenstein M, Yu S. Fooling intrusion detection systems using adversarially autoencoder. Digit Commun Network. 2021;7(3):453–60. 10.1016/j.dcan.2020.11.001 . Search in Google Scholar

[95] Mirsky Y, Golomb T, Elovici Y. Lightweight collaborative anomaly detection for the IoT using blockchain. J Parallel Distrib Comput. 2020;145:75–97. 10.1016/j.jpdc.2020.06.008 . Search in Google Scholar

[96] Shi G, Zhang Z, Gao D, Lin J, Xie X, Liu D. Knowledge-guided semantic computing network. Neurocomputing. 2021;426:70–84. 10.1016/j.neucom.2020.09.075 . Search in Google Scholar

[97] Bai T, Bian H, Salahuddin MA, Abou Daya A, Limam N, Boutaba R. RDP-based lateral movement detection using machine learning. Comput Commun. 2021;165:9–19. 10.1016/j.comcom.2020.10.013 . Search in Google Scholar

[98] Chen S, Shen H, Wang R, Wang X. Towards improving fast adversarial training in multi-exit network. Neural Network. 2022;150:1–11. 10.1016/j.neunet.2022.02.015 . Search in Google Scholar PubMed

[99] Roshan MK, Zafar A. Boosting robustness of network intrusion detection systems: A novel two phase defense strategy against untargeted white-box optimization adversarial attack. Expert Syst Appl. 2024;249:123567. 10.1016/j.eswa.2024.123567 . Search in Google Scholar

[100] Sharma O, Sharma A, Kalia A. MIGAN: GAN for facilitating malware image synthesis with improved malware classification on novel dataset. Expert Syst Appl. 2024;241:122678. 10.1016/j.eswa.2023.122678 . Search in Google Scholar

[101] Wan Y, Qu Y, Gao L, Xiang Y. Privacy-preserving blockchain-enabled federated learning for B5G-Driven edge computing. Comput Network. 2022;204:108671. 10.1016/j.comnet.2021.108671 . Search in Google Scholar

[102] Yamany W, Moustafa N, Turnbull B. OQFL: an optimized quantum-based federated learning framework for defending against adversarial attacks in intelligent transportation systems. IEEE Trans Intell Transp Syst. 2023;24(1):893–903. 10.1109/TITS.2021.3130906 . Search in Google Scholar

[103] Ahmad MS, Shah SM. A lightweight mini-batch federated learning approach for attack detection in IoT. Internet Things. 2024;25:101088. 10.1016/j.iot.2024.101088 . Search in Google Scholar

[104] Hamouda D, Ferrag MA, Benhamida N, Seridi H, Ghanem MC. Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques. Internet Things. 2024;26:101149. 10.1016/j.iot.2024.101149 . Search in Google Scholar

[105] Bukhari SM, Zafar MH, Abou Houran M, Moosavi SK, Mansoor M, Muaaz M, et al. Secure and privacy-preserving intrusion detection in wireless sensor networks: Federated learning with SCNN-Bi-LSTM for enhanced reliability. Ad Hoc Netw. 2024;155:103407. 10.1016/j.adhoc.2024.103407 . Search in Google Scholar

[106] Roshan K, Zafar A, Ul Haque SB. Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system. Comput Commun. 2023;218:97–113. 10.1016/j.comcom.2023.09.030 . Search in Google Scholar

[107] Nguyen XH, Le KH. Robust detection of unknown DoS/DDoS attacks in IoT networks using a hybrid learning model. Internet Things (Neth). 2023;23:100851. 10.1016/j.iot.2023.100851 . Search in Google Scholar

[108] Xie Y, Chen H. A novel method for effective intrusion detection based on convolutional speaking neural networks. J King Saud Univ – Comput Inf Sci. 2024;36(2):101975. 10.1016/j.jksuci.2024.101975 . Search in Google Scholar

[109] Liu J, Zhao Y, Feng Y, Hu Y, Ma X. SeMalBERT: Semantic-based malware detection with bidirectional encoder representations from transformers. J Inf Secur Appl. 2024;80:103690. 10.1016/j.jisa.2023.103690 . Search in Google Scholar

[110] Ardito C, Deldjoo Y, Di Noia T, Di Sciascio E, Nazary F. IEEE13-AdvAttack a novel dataset for benchmarking the power of adversarial attacks against fault prediction systems in smart electrical grid. International Conference on Information and Knowledge Management, Proceedings. 2022. p. 3817–21. 10.1145/3511808.3557612 . Search in Google Scholar

[111] Duy PT, Khoa NH, Hien DTT, Do Hoang H, Pham VH. Investigating on the robustness of flow-based intrusion detection system against adversarial samples using generative adversarial networks. J Inf Secur Appl. 2023;74:103472. 10.1016/j.jisa.2023.103472 . Search in Google Scholar

[112] Albahri AS, Hamid RA, Abdulnabi AR, Albahri OS, Alamoodi AH, Deveci M, et al. Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in vehicular ad-hoc networks. Inf Fusion. 2024;105:102208. 10.1016/j.inffus.2023.102208 . Search in Google Scholar

[113] Marchisio A, Nanfa G, Khalid F, Hanif MA, Martina M, Shafique M. SeVuc: A study on the security vulnerabilities of capsule networks against adversarial attacks. Microprocess Microsyst. 2023;96:104738. 10.1016/j.micpro.2022.104738 . Search in Google Scholar

[114] Albahri AS, Joudar SS, Hamid RA, Zahid IA, Alqaysi ME, Albahri OS, et al. Explainable artificial intelligence multimodal of autism triage levels using fuzzy approach-based multi-criteria decision-making and LIME. Int J Fuzzy Syst. 2024;26(1):274–303. 10.1007/s40815-023-01597-9 . Search in Google Scholar

[115] Albahri OS, Alamoodi AH, Deveci M, Albahri AS, Mahmoud MA, Al-Quraishi T, et al. Evaluation of organizational culture in companies for fostering a digital innovation using q-rung picture fuzzy based decision-making model. Adv Eng Inform. 2023;58:102191. 10.1016/j.aei.2023.102191 . Search in Google Scholar

[116] Habeeb MA. Hate speech detection using deep learning. Master thesis. Hungary: University of Miskolc; 2021. Search in Google Scholar

[117] Khaleel YL. Fake news detection using deep learning. Hungary: University of Miskolc; 2021. 10.13140/RG.2.2.31151.75689 . Search in Google Scholar

[118] Dadvandipour S, Khaleel YL. Application of deep learning algorithms detecting fake and correct textual or verbal news. Prod Syst Inf Eng. 2022;10(2):37–51. 10.32968/psaie.2022.2.4 . Search in Google Scholar

[119] Al-Samarraay M, Al-Zuhairi O, Alamoodi AH, Albahri OS, Deveci M, Alobaidi OR, et al. An integrated fuzzy multi-measurement decision-making model for selecting optimization techniques of semiconductor materials. Expert Syst Appl. 2024;237:121439. 10.1016/j.eswa.2023.121439 . Search in Google Scholar

[120] Alzubaidi L, Chlaib HK, Fadhel MA, Chen Y, Bai J, Albahri AS, et al. Reliable deep learning framework for the ground penetrating radar data to locate the horizontal variation in levee soil compaction. Eng Appl Artif Intell. 2024;129:107627. 10.1016/j.engappai.2023.107627 . Search in Google Scholar

[121] Hassan A, Mahmood A. Efficient deep learning model for text classification based on recurrent and convolutional layers. 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). 2017. p. 1108–13. 10.1109/ICMLA.2017.00009 . Search in Google Scholar

[122] Albahri AS, Khaleel YL, Habeeb MA. The considerations of trustworthy ai components in generative AI; A Letter to Editor. Appl Data Sci Anal. 2023 Oct;2023:SE-Articles):108–9. 10.58496/ADSA/2023/009 . Search in Google Scholar

[123] Ray PP. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Physical Syst. 2023;3:121–54. 10.1016/j.iotcps.2023.04.003 . Search in Google Scholar

[124] Mihna FKH, Habeeb MA, Khaleel YL, Ali YH, Al-saeedi LAE. Using information technology for comprehensive analysis and prediction in forensic evidence. Mesopotamian J Cybersecur. 2024;2024:4–16. 10.58496/mjcs/2024/002 . Search in Google Scholar

This work is licensed under the Creative Commons Attribution 4.0 International License.

X / Twitter

Supplementary Materials

Please login or register with De Gruyter to order this product.

Journal and Issue

Articles in the same issue.

IMAGES

(PDF) IRJET- Analysis of Music Recommendation System using Machine
Music Recommendation System Using Machine Learning
(PDF) Music Recommendation System Using Facial Expression Recognition
Music Recommendation System In Machine Learning
Applied Sciences
Music Recommendation System In Machine Learning

COMMENTS

Music Recommendation System Using Machine Learning
Since this paper proposes another approach for the recommendation, the other approach utilises the Spotify API calls. It aims at the implementation using the various machine learning algorithms which includes K Nearest Neighbor Classification, Decision Tree Classifier and the Random Forest Classifier.
Music Recommendation System Using Machine Learning
Music Recommendation System Using Machine Learning. November 2021. International Journal of Scientific Research in Computer Science Engineering and Information Technology. DOI: 10.32628 ...
Music Recommendation System using Machine Learning Methods
This paper is made with a purpose of comparing each machine learning algorithm for music recommendation system to find which of them provides a higher performance, efficiency, and accuracy for a better music recommendation system, with the hopes of contributing to future research on recommendation systems, specifically related to music or any ...
PDF Music Recommendation System Using Machine Learning
Initially, the system collects large amount of user data, including listening history and ratings to create a detailed profile. To construct a music recommendation system, we can use different machine learning algorithms, such as cosine similarity, K-nearest neighbor, Weighted Product Method.
Music Recommendation System Using Machine Learning
In our project, we plan to employ a dataset containing songs to discover relationships between users and songs, enabling us to provide song recommendations based on their historical preferences. We intend to execute this project by utilizing libraries such as NumPy and Pandas, as well as employing techniques like Cosine similarity, TfidfVectorizer, and tokenization. Additionally, we will ...
Music Recommendation System Using Machine Learning
Insight is provided into the possibilities of machine learning algorithms for music recommendations settings depending on the user and the generalizability of the model to other Spotify users is limited. This research paper is about a music recommendation system that uses machine learning algorithms and binary text classification, we looked at the performance of models and the results which we ...
Music Recommendation System Using Machine Learning
The most well-known applications of the recommender system are in the areas of books, news, articles, music, records, movies, and other media. The outline of the machine learning calculations and tactics developed for the proposal framework are covered in this study.We have suggested a music suggestion framework dataset in this article.
Music Recommendation System Based on Machine Learning
In this paper, we categorize and outline several mainstream music recommendation methods: content-based and collaborative filtering recommendations, as well as weighted hybrid models of both.
Music Recommendation System Using Machine Learning
This research article presents Tamil songs compilation and recommendation system that is developed in Java programming language by adopting the techniques of Machine Learning based on the emotion and interest, ratings provided by listeners and search patterns.
Music Recommendation System Using Multiple Machine Learning Models
Abstract Music recommendation systems play a crucial role in helping users discover new music based on their preferences and interests. This paper presents a novel approach to music recommendation using three distinct models: topic-based, feature-based, and text-based.
Music Recommendation System Using Machine Learning
Download Citation | On Dec 16, 2022, Rajesh Kumar and others published Music Recommendation System Using Machine Learning | Find, read and cite all the research you need on ResearchGate
PDF Music Recommendation Using Machine Learning Algorithms
This introduction highlights the significance of music recommendation systems, the benefits they bring, and the role of Machine Learning Algorithms in making music discovery and recommendations more effective.
Music Recommendation System Using Machine Learning
The most well-known applications of the recommender system are in the areas of books, news, articles, music, records, movies, and other media. The outline of the machine learning calculations and tactics developed for the proposal framework are covered in this study.We have suggested a music suggestion framework dataset in this article.
Enhanced Music Recommendation Systems: A Comparative Study of Content
In the dynamic landscape of digital music services, recommendation systems play a pivotal role, evolving in tandem with advances in artificial intelligence and machine learning. This research undertakes a comparative exploration of two distinct approaches to song recommendations: content-based filtering and K-means clustering.
Music Recommender System Based on Genre using ...
By using music recommender system, the music provider can predict and then offer the appropriate songs to their users based on the characteristics of the music that has been heard previously. Our research would like to develop a music recommender system that can give recommendations based on similarity of features on audio signal.
Music Recommendation System Using Machine Learning
In our project, we will be using a sample data set of songs to find correlations between users and songs so that a new song will be recommended to them based on their previous history. We will implement this project using libraries like NumPy,
Music genre classification and music recommendation by using deep learning
One approach is based on the acoustic characteristics of music. In this study, a music genre classification system and music recommendation engine, which focuses on extracting representative features that have been obtained by a novel deep neural network model, have been proposed.
PDF JETIR Research Journal
This research aims to identify these concerns and provide recommendations to ensure the safe and ethical use of this technology. Ultimately, this paper aims to contribute to the development of facial emotion-based music recommendation systems and provide insights for future research in this field.
Music Recommendation System Using Machine Learning
This research paper is about a music recommendation system that uses machine learning algorithms and binary text classification, we looked at the performance of models and the results which we achieved are: By the Naïve Bayes Classification achieved an accuracy of 75.01%, with the XG Boost Classification we got an accuracy of 100%, going forward Logistic Regression got an accuracy of 78.6% ...
Music Recommendation System Using Machine Learning
This is nothing but an application of Machine Learning using which recommender systems are built to provide personalized experience and increase customer engagement. In this article, we will try to build a very basic recommender system that can recommend songs based on which songs you hear.
Music Recommendation System Using Machine Learning
filtering, content-based filtering, and hybrid method. Initially, the system collects large amount of user data, including listening history and ratings to create a detailed profile. To construct a music recommendation system, we can use different machine learning algorithms, such as cosine similarity, K-nearest neighbor, Weighted Product Method.
2024 Conference
The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research advances in Artificial Intelligence and Machine Learning, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.
Full article: Smart energy management: real-time prediction and
Evolution of Smart Home Energy Management System Using Internet of Things and Machine Learning Algorithms (Singh et al., Citation 2022). In smart cities, this research helps and solve energy management problems. The system reduces the energy costs of a smart home or building through recommendations and predictions.
Music Mood Prediction and Playlist Recommendation based on Facial
Nowadays, automation is at its peak. The article provides a base to examine the weather through the machine without human intervention. This study offers a thorough classification model to ...
A personalized music recommendation system using convolutional neural
In this paper, we present a personalized music recommendation system (PMRS) based on the convolutional neural networks (CNN) approach. The CNN approach classifies music based on the audio signal beats of the music into different genres. In PMRS, we propose a collaborative filtering (CF) recommendation algorithm to combine the output of the CNN with the log files to recommend music to the user ...
Online Courses Student Performance Prediction with ...
DOI: 10.1145/3672758.3672793 Corpus ID: 271755551; Online Courses Student Performance Prediction with Multi-model Stacking Ensemble Classifier @article{Zheng2024OnlineCS, title={Online Courses Student Performance Prediction with Multi-model Stacking Ensemble Classifier}, author={Tianci Zheng and Zhurong Zhou and Zhuang Wang and Yi Chen}, journal={Proceedings of the 3rd International Conference ...
Implementing AI in Hospitals to Achieve a Learning Health System
Background: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets.
Emotion based music player using machine learning
As a matter of fact, music emotion recognition based on machine learning has been a trend in recent years. This study describes the analysis of the realization for emotion recognition based on ...
Music Genre Classification and Recommendation by Using Machine Learning
Abstract: Music genre prediction is one of the topics that digital music processing is interested in. In this study, acoustic features of music have been extracted by using digital signal processing techniques and then music genre classification and music recommendations have been made by using machine learning methods.
Network and cybersecurity applications of defense in adversarial
This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies and methodologies in adversarial attacks using machine learning (ML) and deep learning methods. A methodology was conducted to guarantee a thorough literature analysis of the studies using sources such as ScienceDirect, Scopus, IEEE Xplore, and Web of Science. A ...

Music Recommendation System Using Multiple Machine Learning Models

Mudit Kumar Tyagi

Muhammad Ali

Gunjan Chugh

Mudit Kumar Tyagi (Contact Author)

Maharaja Agrasen Institute of Technology ( email )

Do you have a job opening that you would like to promote on SSRN?

International Conference on Innovative Computing & Communication (ICICC) 2024

Art & Science eJournal

Music Recommendation System Using Machine Learning

Music Similarity Measures: What's the use?

Search form

Music Recommendation System Using Machine Learning

Related Papers

RELATED PAPERS

RELATED TOPICS

Music Recommendation System Using Machine Learning

Importing Libraries & Dataset

Data Cleaning

Exploratory Data Analysis

Please Login to comment...

What kind of Experience do you want to share?

Main Navigation

Registration

Announcements

Latest NeurIPS Blog Entries [ All Entries ]

Important Dates

Organizing Committee

About the Conference

Online Courses Student Performance Prediction with Multi-model Stacking Ensemble Classifier

5 References

This paper is in the following e-collection/theme issue:

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers

Corresponding Author:

Introduction

Toward the Digital Transformation of Health Care

Analysis of Prior Work

Research Significance and Objectives

Search Strategy

Eligibility Criteria

Data Extraction and Synthesis

Quality Assessment

Study Selection

Study Characteristics

RQ Findings

RQ 1B Findings: Barriers to AI Implementation in Hospitals

RQ 2 Findings: Mapping the Findings to the 3-Horizon Framework

Principal Findings

Toward AI Implementation in Hospitals

Horizon 1: Establishing Digital Infrastructure

Horizon 2: Creating Analytics

Form a Diverse Team of Experts

Enhance the Existing Processes

Strive for Better Data Quality and Security

Strengthen Technological Infrastructures

Horizon 3: New Models of Care

Limitations

Conclusions

Acknowledgments

Data Availability

Authors' Contributions

Conflicts of Interest

Abbreviations

IEEE Account

Purchase Details

Profile Information

Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

1 Introduction

2 Adversarial attack: Overview and analysis

2.1 Overview

2.2 Analysis

3 Methodology

3.1 Search strategy

3.2 Inclusion and exclusion criteria

3.3 Study selection

4 Findings analysis

4.1 Bibliometric analysis

4.1.1 Most relevant words

4.1.2 Most relevant sources

4.1.3 Words, sources frequency, and trend topic over time