The DOI matches its Crossref record; 151 of the 153 references it cites were found in a registry or an index.
Machine Learning: Algorithms, Real-World Applications and Research Directions
Open accessPublished version of a preprint
Abstract
In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The regularization techniques such as Ridge, Lasso, Elastic-Net, etc. [85, 105] can be used to optimize the linear regression model.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Besides, support vector regression, decision tree regression, random forest regression techniques [85, 105] can be used for building effective regression models depending on the problem type, e.g., non-linear tasks.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Advanced analytics of these data using machine learning techniques [105] can facilitate the efficient management of urban areas including real-time management, e.g., traffic flow management, evidence-based planning decisions which pertain to the longer-term strategic role of forecasting for urban planning, e.g., crime prevention, public safety, and security, or framing the future, e.g.,”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Advanced analytics of rural data including machine learning [105] modeling can facilitate providing new opportunities for them to build insights and capacity to meet current needs and prepare for their futures.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“These deep learning methods can perform well, particularly, when learning from large-scale datasets [105, 140].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
The cited source says what the claim attributes to it.
In the citing paper
“Such advanced analytics and decision-making based on machine learning techniques [105], a major part of artificial intelligence (AI) [102] can also play a significant role in the Fourth Industrial Revolution (Industry 4.0) due to its learning capability for smart computing as well as automation [121].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
The cited source says what the claim attributes to it.
In the citing paper
“The data can be structured, semi-structured, or unstructured, which increases day by day [105].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1×
“…based on CNN can be used in the field, such as AlexNet [60], Xception [24], In-Preprints ception [118], Visual Geometry Group (VGG) [44], ResNet [45], etc. – LSTM-RNN: Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the area of deep learning…”
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
(1967) · Cited by 23,719
Research works printed but not linked: 1
- Baldi P. Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, 2012; 37–49 .
Cited by
Sorted by citations
Items 701–800 of 1,013
Bacterial image analysis using multi-task deep learning approaches for clinical microscopy
Chin, S. Y., Dong, J., Hasikin, K. B., Ngui, R., Lai, K. W., Yeoh, P. S. Q., & Wu, X. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2180 · Cited by 31
“…or object detection, natural language processing (NLP) like language translation or sentiment analysis, robotics such as object manipulation or navigation and healthcare such as medical diagnosis, medical image analysis, or patient care improvement ( Sarker, 2021 ; Yusoff, Isa & Hasikin, 2013 ).”
MethodKATAR, O., Yıldırım, Ö., Tan, R., & Acharya, U. R. (2024) · Diagnostics · doi:10.3390/diagnostics14222497 · Cited by 29
“The experiments were conducted using the lung cancer subset of the LC25000 dataset, which contains 15,000 samples. contains 15,000 samples.”
Enhancing Electricity Load Forecasting with Machine Learning and Deep Learning
Percuku, A. S., Minkovska, D. V., & Hinov, N. L. (2025) · Technologies · doi:10.3390/technologies13020059 · Cited by 30
“The paper continues by reviewing and analyzing several research paper in the regard of short term load forecasting [27–34].”
Özdemir, Ö., Köker, R., & Pamuk, N. (2025) · Processes · doi:10.3390/pr13020527 · Cited by 25
“This comparison helps to assess the model’s accuracy and its ability to classify data correctly [31,32].”
Sivaraman, S., Radhika, N., & Khan, M. A. (2025) · IEEE Access · doi:10.1109/access.2025.3542507 · Cited by 25
“Machine Learning (ML) is an optimal solution in this context, which improves the accuracy of the results [12].”
Breaking Away From AI: The Ontological and Ethical Evolution of Machine Learning
Barbierato, E., Gatti, A., Incremona, A., Pozzi, A., & Toti, D. (2025) · IEEE Access · doi:10.1109/access.2025.3553032 · Cited by 31
“Mohri et al. [72] provide a broad analysis of ML algorithms, real-world applications, and emerging research directions, underscoring the diversity of the field.”
Nanogenerator-induced personalized wearable health monitoring electronics: A review
Shahat, A., Mahmoud, M. A., El‐Sewify, I. M., Reda, A., Akter, N., Alharbi, A., Radwan, A. G., Hasan, M., Shenashen, M. A., & El‐Safty, S. A. (2025) · Nano Energy · doi:10.1016/j.nanoen.2025.110897 · Cited by 26
Zyoud, S. H., & Zyoud, A. H. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104917 · Cited by 27
Dulaj, K., Alhammadi, A., Shayea, I. A. M., El‐Saleh, A. A., & Alnakhli, M. A. (2025) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2025.3564361 · Cited by 30
“Together, 5G and ML enable a smarter, more secure, and sustainable future for networking [21], [22], [57] and as depicted in Figure 9.”
Zhao, Z., Dong, J., Qi, B., Duan, N., & He, Q. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110960 · Cited by 26
Negative impacts of climate change on crop yields are underestimated
Júnior, R. D. S. N., Asseng, S., Müller, C., Deswarte, J., Cohan, J., & Martre, P. (2025) · Trends in Plant Science · doi:10.1016/j.tplants.2025.05.002 · Cited by 25
Noebels, M., Preece, R., & Panteli, M. (2021) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.12287 · Cited by 25
Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images
Akinbo, R. S., & Daramola, O. A. (2021) · Artificial intelligence · doi:10.5772/intechopen.100602 · Cited by 28
Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization
Yu, D., Lee, H., Kim, T., & Hwang, S. (2021) · Sensors · doi:10.3390/s21238152 · Cited by 26
“Decent prediction performance can be expected, even from a basic model, if the model uses large amounts of data that properly depict the situations to be learned [64,65].”
BackgroundArtificial intelligence in aquaculture: basis, applications, and future perspectives
Quispesivana, W. V., Inga, M., & Betalleluz‐Pallardel, I. (2022) · Scientia Agropecuaria · doi:10.17268/sci.agropecu.2022.008 · Cited by 29
“Utiliza la aleatoriedad para resolver problemas deterministas; optimización, integración numérica y hacer dibujos a partir de la distribución de probabilidad (Sarker, 2021).”
BackgroundInfluentialLandslides and flood multi-hazard assessment using machine learning techniques
Youssef, A. M., Mahdi, A. M., & Pourghasemi, H. R. (2022) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-022-02874-x · Cited by 25
“…classifier (Breiman 2001), suitable for both problem categorization and regression and optimal for continuous and categorical data types (Sarker 2021), is used to classify (4)f (z) = 1 (1 + e−z) (5)z = b0 + B1X1 + B2X2 +⋯ + BnXn data based on feature detection, identify the…”
MethodGaurav, A., Agrawal, N., Al-Nema, M. Y., & Gautam, V. (2022) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026623666221019110334 · Cited by 26
The impact of cost function globality and locality in hybrid quantum neural networks on NISQ devices
Kashif, M., & Al-Kuwari, S. M. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acb12f · Cited by 33
“Unlike traditional approaches, where explicit programming is required to perform a specific task, in ML, the machine is trained to learn from data [1] to solve the problems on new unseen data [2].”
BackgroundAnalysis of Diabetes mellitus using Machine Learning Techniques
Bhat, S. S., Selvam, V., Ansari, G. A., & Ansari, M. D. (2022) · International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT) · doi:10.1109/impact55510.2022.10029058 · Cited by 25
“This type of diabetes is frequently discovered in the middle or final phase of pregnancy [6-7].”
BackgroundIntelligent credit scoring using deep learning methods
Gicić, A., Đonko, D., & Subaşı, A. (2023) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7637 · Cited by 25
Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease
Gilotra, K., Swarna, S., Mani, R., Basem, J. I., & Dashti, R. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1254417 · Cited by 28
“With unsupervised learning, pre-existing information is absent which means the model must cluster a group of cases together based on similar characteristics and identify the relationships between said groups (Sarker, 2021).”
BackgroundHan, Q., Zeng, Y., Zhang, L., Cira, C.-I., Prikaziuk, E., Duan, T., Wang, C., Szabó, B., Manfreda, S., Zhuang, R., et al. (2023) · Geoscientific model development · doi:10.5194/gmd-16-5825-2023 · Cited by 26
“Eight algorithms were selected based on their popularity and their proven performance with respect to regression tasks (Sarker, 2021 The algorithms considered for optimisation were (1) Random Forest Regressor (RFR) (Belgiu and Dr˘agu¸t, 2016; Breiman, 2001), (2) K-neighbours Re-gressor (KNR)…”
MethodEngagement assessment in project-based education: a machine learning approach in team chat analysis
Farshad, S., Zorin, E., Amangeldiuly, N., & Fortin, C. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12381-5 · Cited by 31
“If the experiment supports the idea that the collaboration is reflected in the conversation, then the feasibility of utilizing a single source to utilize state-of-the-art technology, e.g., ML and NLP through text classification methods, is more likely (Sarker, 2021).”
A comprehensive review of machine learning's role in enhancing network security and threat detection
Atadoga, A., Sodiya, E. O., Umoga, U. J., & Amoo, O. O. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.2.0501 · Cited by 25
“Machine learning has found applications across a wide range of domains, including healthcare, finance, retail, marketing, transportation, and cybersecurity (Sarker, 2021).”
BackgroundDevelopment of sensor system and data analytic framework for non-invasive blood glucose prediction
Rȧjeswari, S., & Vijayakumar, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-59744-7 · Cited by 25
“The advantage of Lasso CV over LR and PR is its ability to automatically select features, leading to a sparse model and handling multicollinearity in the dataset 71–73 .”
BackgroundInfluentialQuetschlich, N., Burgholzer, L., & Wille, R. (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3673241 · Cited by 40
“(1) Training Circuits : Similar to the RL models, used here to generate labeled training data .”
Ahamad, M. M., Aktar, S., Uddin, M. J., Rashed-Al-Mahfuz, M., Azad, A., Uddin, S., Alyami, S. A., Sarker, I. H., Lió, P., Quinn, J. M. W., et al. (2021) · medRxiv · doi:10.1101/2021.04.16.21255618 · Cited by 25
Self-citationHealthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A SurveyPreprint
Badawy, M., Ramadan, N., & Hefny, H. A. (2022) · Research Square · doi:10.21203/rs.3.rs-1885746/v1 · Cited by 26
Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon
Bassani, D., Brigo, A., & Morger, A. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00137 · Cited by 25
Machine Learning-Based Text Classification Comparison: Turkish Language Context
Alzoubi, Y. I., Topcu, A. E., & Erkaya, A. E. (2023) · Applied Sciences · doi:10.3390/app13169428 · Cited by 24
Chandrashekar, K. S., Niranjan, V., Vishal, A., & Setlur, A. S. (2024) · Current Bioinformatics · doi:10.2174/0115748936284044240108074937 · Cited by 28
Wattanapanich, C., Imjai, T., Sridhar, R. K., Garcia, R., & Thomas, B. S. (2024) · Engineered Science · doi:10.30919/es1191 · Cited by 24
“By employing image processing and ML algorithms, researchers have quantified and enhanced the morphological properties of RCA, leading to improved density and reduced crushing value.”
Zaffar, A., Prabhakar, M. R., Liu, C., Sivaraman, J., & Balasubramanian, P. (2024) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2024.114271 · Cited by 25
Cheligeer, K., Wu, G., Laws, A., Quan, M. L., Li, A., Brisson, A.-M., Xie, J., & Xu, Y. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02677-y · Cited by 26
Identifying the best ML model for predicting the bandgap in a perovskite solar cell
Samantaray, N., Singh, A., & Tonk, A. (2024) · RSC Sustainability · doi:10.1039/d4su00370e · Cited by 24
McKee, M., Rosenbacke, R., & Stückler, D. (2024) · The International Journal of Health Planning and Management · doi:10.1002/hpm.3864 · Cited by 24
Pawar, N. S., & Sharma, K. V. (2025) · Natural Hazards · doi:10.1007/s11069-025-07171-w · Cited by 24
“Several industries, including computer vision, e-commerce, healthcare, autonomous automobiles, and personal help, used these models (Sarker 2021b).”
Machine learning predictive performance in road accident severity: A case study from Thailand
Mohamad, I., Jomnonkwao, S., & Ratanavaraha, V. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104833 · Cited by 29
AI awakens food packaging: Integrative advances, challenges, and future perspectives
Jia, X., Li, X., Alharbi, M. O., Ma, P., Ismail‐Fitry, M. R., Xia, S., Zhang, Y., Li, Y., & Wang, Q. L. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105466 · Cited by 24
Disease Prediction and Doctor Recommendation System using Machine Learning Approaches
Kumar, A. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.36234 · Cited by 24
Khan, M., Ahmad, I., Ahsan, M., Kano, M., & Çalışkan, H. (2022) · Fuel · doi:10.1016/j.fuel.2022.125563 · Cited by 24
Karthick, G. S., & Pankajavalli, P. B. (2022) · Theoretical Computer Science · doi:10.1016/j.tcs.2022.08.021 · Cited by 24
Usman, M., & Kopczewska, K. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191710967 · Cited by 24
A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data
Sarker, K. U., Saqib, M., Hasan, R., Mahmood, S., Hussain, S., Abbas, A., & Deraman, A. (2022) · Computers · doi:10.3390/computers11110158 · Cited by 27
“K-means, k-medoids, CLARA, and CLARANS are the commonly used partitioning clustering methods [113] and they are suitable for separate clusters with a predefined cluster number [110].”
MethodVidanaralage, A. J., Dharmaratne, A. T., & Haque, S. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100109 · Cited by 32
Ouali, L., Kabiri, L., Namous, M., Hssaisoune, M., Abdelrahman, K. S., Fnais, M. S., Kabiri, H., Hafyani, M. E., Oubaassine, H., Arioua, A., et al. (2023) · Sustainability · doi:10.3390/su15053874 · Cited by 26
Tominc, P., & Rožman, M. (2023) · Education Sciences · doi:10.3390/educsci13060580 · Cited by 30
Ulucan, M., Yıldırım, G., Alataş, B., & Alyamaç, K. E. (2023) · Structural Concrete · doi:10.1002/suco.202300138 · Cited by 24
“30 Most of these classification algorithms and models are black-box-based approaches.”
BackgroundYang, L., & Ding, N. (2023) · Safety Science · doi:10.1016/j.ssci.2023.106243 · Cited by 26
Evaluating global intelligence innovation: An index based on machine learning methods
Ma, X., Hao, Y., Li, X., Liu, J., & Qi, J. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122736 · Cited by 24
Aparicio-Ruiz, P., Barbadilla-Martín, E., Martín, J. G., & Nevado, J. (2023) · Buildings · doi:10.3390/buildings13092215 · Cited by 24
Exploration of Production Data for Predictive Maintenance of Industrial Equipment: A Case Study
Burmeister, N., Frederiksen, R. D., Høg, E. P., & Nielsen, P. (2023) · IEEE Access · doi:10.1109/access.2023.3315842 · Cited by 24
Katipoğlu, O. M. (2023) · Environmental Processes · doi:10.1007/s40710-023-00669-0 · Cited by 24
“XGBoost can be used to evaluate large datasets (Sarker 2021).”
Ullah, K. A., Rehman, F. A. U., Anwar, M., Faheem, M., & Riaz, N. (2023) · Health Science Reports · doi:10.1002/hsr2.1656 · Cited by 24
Torres, L. H. M., Ribeiro, B., & Arrais, J. P. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111268 · Cited by 26
The performance of artificial intelligence in prostate magnetic resonance imaging screening
Owida, H. A., Hassan, M. R., Ali, A. M., Alnaimat, F., Sharah, A. A., Abuowaida, S. F. A., & Alshdaifat, N. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i2.pp2234-2241 · Cited by 30
Digital marketing analytics: A review of strategies in the age of big data and AI
Adeleye, R. A., Awonuga, K. F., Asuzu, O. F., Ndubuisi, N. L., & Tula, S. T. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.2.0395 · Cited by 24
“This information informs reputation management efforts and helps in crafting appropriate responses (Mirza, et. al., 2019, Sarker, 2021, Zhong, et. al., 2021).”
BackgroundA technical perspective on integrating artificial intelligence to solid-state welding
Yaknesh, S., Rajamurugu, N., Babu, P. K., Subramaniyan, S., Khan, S. A., Saleel, C. A., Nur‐E‐Alam, M., & Soudagar, M. E. M. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-13524-9 · Cited by 24
“These AI techniques are chosen based on their distinct advantages and limitations, offering tailored solutions to specific SSW challenges [82–86].”
Smart Microbiomes: How AI Is Revolutionizing Personalized Medicine
Alexandrescu, L., Tofolean, I. T., Condur, L., Tofolean, D. E., Nicoara, A. D., Șerbănescu, L., Rusu, E. E., Twakor, A. N., Dumitru, E., Dumitru, A., et al. (2025) · Bioengineering · doi:10.3390/bioengineering12090944 · Cited by 28
Pacheco, F. A. L., Hermosilla, G., Bustamante, O. P., Villavicencio, G. E., Allende‐Cid, H., Palma, J., Valenzuela, P., García, J., Carpanetti, A., Minatogawa, V., et al. (2022) · Mathematics · doi:10.3390/math10234396 · Cited by 24
Al, S., Ciloglu, F. U., Akçay, A., & Koluman, A. (2023) · Meat Science · doi:10.1016/j.meatsci.2023.109421 · Cited by 23
Sara, S. S., Rahman, M. A., Rahman, R., & Talukder, A. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.01.092 · Cited by 26
Asri, A. K., Lee, H., Chen, Y., Wong, P.-Y., Hsu, C.-Y., Chen, P., Lung, S. C., Chen, Y., & Wu, C. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.170209 · Cited by 23
Spectral intelligent detection for aflatoxin B1 via contrastive learning based on Siamese network
Zhu, H., Zhao, Y., Gu, Q., Zhao, L., Yang, R., & Han, Z. (2024) · Food Chemistry · doi:10.1016/j.foodchem.2024.139171 · Cited by 23
Quantum Natural Language Processing: A Comprehensive Survey
Moudina, C. V., Fendji, J. L. E. K., Schöning, J., & Atemkeng, M. (2024) · IEEE Access · doi:10.1109/access.2024.3420707 · Cited by 25
“Classical machine learning techniques [55], [56], [57] empower machines to learn unknown functions from known inputs or data to predict the outputs of unknown inputs.”
BackgroundNechita, L., Nechita, A., Voipan, A. E., Voipan, D., Debita, M., Fulga, A., Fulga, I., & Mușat, C. L. (2024) · Diagnostics · doi:10.3390/diagnostics14171839 · Cited by 24
“This method is useful when acquiring a fully labeled dataset is impractical or expensive [8].”
BackgroundMethodInfluentialPredictive Analytics for Thyroid Cancer Recurrence: A Machine Learning Approach
Clark, E., Price, S., Lucena, T., Haberlein, B., Wahbeh, A., & Seetan, R. I. (2024) · Knowledge · doi:10.3390/knowledge4040029 · Cited by 23
“It proved effective in handling high-dimensional data and addressing class imbalances, which are common in DTC datasets, ultimately leading to better predictive performance [23].”
Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis
Zhang, Z., Cheng, H., Chen, M., Zhang, L., Cheng, Y., Geng, W., & Guan, J. (2024) · Foods · doi:10.3390/foods13233956 · Cited by 23
“Machine learning is a computational approach that involves the use of algorithms to analyze large datasets and build predictive models [13].”
Ansari, S. S., Ibrahim, S. M., & Hasan, S. D. (2024) · Journal of structural design and construction practice. · doi:10.1061/jsdccc.sceng-1496 · Cited by 24
A Machine and Deep Learning Framework for Robust Health Insurance Fraud Detection and Prevention
Pandya, S. (2023) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-14000u · Cited by 23
AI applications in HIV research: advances and future directions
Jin, R., & Zhang, L. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1541942 · Cited by 26
“Another common statistical model, logistic regression (LR), can be used for both classification and regression problems (Sarker, 2021).”
AI-Enhanced Prediction Tools and Sensor Integration in Advanced Analytical Chemistry Techniques
Kumar, M., Nandi, A., Yadav, R. L., Gupta, G. D., & Sharma, K. (2025) · Current Analytical Chemistry · doi:10.2174/0115734110373957250516113853 · Cited by 24
Ülkir, O. (2025) · Measurement · doi:10.1016/j.measurement.2025.118089 · Cited by 24
Sharma, D., & Singh, K. (2025) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-025-04494-5 · Cited by 23
“…anal - ysis for predicting the numerical outcomes, clustering for grouping similar data together, dimensionality reduction for simplifying data and retaining essential information, anomaly detection for spotting the outliers and Reinforcement learning to make sequential decisions (Sarker 2021).”
Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma
Morello, G., Cognata, V. L., Guarnaccia, M., Gentile, G., & Cavallaro, S. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199362 · Cited by 24
“Indeed, the field of AI encompasses a range of algorithms and techniques that enable the extraction and processing of high-dimensional data (clinical, biochemical, omics data and medical imaging) as well as their storage, integration, analysis and interpretation [21–23] (Figure 2).”
Sarathkumar, E., & Jayasree, R. S. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118478 · Cited by 26
The Role of Artificial Intelligence in Biomaterials Science: A Review
Martelli, A., Bellucci, D., & Cannillo, V. (2025) · Polymers · doi:10.3390/polym17192668 · Cited by 24
“The aim is to improve prediction accuracy beyond what can be achieved using only labelled data [26].”
InfluentialA Simulation and Machine Learning Informed Diagnosis of the Severe Accidents
Song, J., & Ha, K.-S. (2022) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2022.111881 · Cited by 24
Wimalarathna, H., Veeranna, S. A., Allan, C., Agrawal, S. K., Samarabandu, J. K., Ladak, H. M., & Allen, P. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107118 · Cited by 23
Vrdoljak, J., Boban, Z., Barić, D., Šegvić, D., Kumrić, M., Avirović, M., Balja, M. P., Periša, M. M., Tomasović, Č., Tomić, S., et al. (2023) · Cancers · doi:10.3390/cancers15030634 · Cited by 28
The power of deep learning for intelligent tumor classification systems: A review
Chandni, Sachdeva, M., & Kushwaha, A. K. S. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108586 · Cited by 24
Review of condition monitoring methods for capacitors used in power converters
Nathan, L. P. A., Hemamalini, R. R., Jeremiah, R. J. R., & Partheeban, P. (2023) · Microelectronics Reliability · doi:10.1016/j.microrel.2023.115003 · Cited by 25
Xie, F., Ning, Y., Liu, M., Li, S., Saffari, S. E., Yuan, H., Volovici, V., Ting, D. S. W., Goldstein, B. A., Ong, M. E. H., et al. (2023) · STAR Protocols · doi:10.1016/j.xpro.2023.102302 · Cited by 23
Jeyanthi, D. V., & Indrani, B. (2023) · Soft Computing · doi:10.1007/s00500-023-08536-8 · Cited by 23
“Machine learning approaches take into account the ideal model to handle the massive nonstop volume of data provided by IoT devices (Sarker 2021).”
RetractedApplication of Machine Learning in Dementia Diagnosis: A Systematic Literature Review
Kantayeva, G., Lima, J., & Pereira, A. I. (2022) · Heliyon · doi:10.1016/j.heliyon.2023.e21626 · Cited by 27
“ML techniques are based on learning from experience, improving its accuracy by imitating human thinking processes [7].”
BackgroundMachine learning for hospital readmission prediction in pediatric population
da Silva, N. C., Albertini, M. K., Backes, A. R., & Pena, G. D. G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107980 · Cited by 23
Explaining Siamese networks in few-shot learning
Fedele, A., Guidotti, R., & Pedreschi, D. (2024) · Machine Learning · doi:10.1007/s10994-024-06529-8 · Cited by 23
“Machine learning (ML) models are commonly used in AI systems because of their success in various fields such as image processing, time series analysis, and audio signal processing (Sarker, 2021; Purwins et al., 2019).”
Machine learning for antidepressant treatment selection in depression
Arnold, P. I. M., Janzing, J., & Hommersom, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104068 · Cited by 24
Pore, S., & Roy, K. (2024) · Digital Discovery · doi:10.1039/d4dd00082j · Cited by 24
Kazanskiy, N. L., Khonina, S. N., Oseledets, I. V., Nikonorov, A. V., & Butt, M. A. (2024) · Technologies · doi:10.3390/technologies12090143 · Cited by 24
“…and optimizations, thereby boosting R&D efficiency [163–165]. signi fi cantly cut down the time required for conventional electromagnetic fi eld simulations and optimizations, thereby boosting R&D e ffi ciency [163 One of the primary challenges in AI-enhanced MS design is the size of the dataset.”
Maniruzzaman, M., Islam, M. M., Rahman, M. J., Hasan, M. A. M., & Shin, J. (2021) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2021.102263 · Cited by 32
DRL-Based Distributed Task Offloading Framework in Edge-Cloud Environment
Nashaat, H., Hashem, W., Rizk, R. Y., & Attia, R. M. (2024) · IEEE Access · doi:10.1109/access.2024.3371993 · Cited by 23
“Deep Reinforcement Learning (DRL) is a subfield of Machine Learning (ML) [10] methodology that combines the Reinforcement Learning (RL) and Deep Neural Network (DNN) to facilitate and obtain the optimal offloading decision-making in volumes of communication and computation.”
MethodMachine learning and deep learning techniques for poultry tasks management: a review
Subramani, T., Jeganathan, V., & Balasubramanian, S. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18951-0 · Cited by 22
“To avoid the disadvantage of the knowledge-based approach, the cogitation of Machine Learning (ML) and Deep Learning (DL) was introduced to AI systems [5].”
Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?
Parsa, S., Somani, S. S., Dudum, R., Jain, S. S., & Rodríguez, F. (2024) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01210-w · Cited by 22
“The non-linear separation and classification of data is an advantage over traditional statistical analysis methods [34].”
Automatic Classification of Cardiac Arrhythmias Using Deep Learning Techniques: A Systematic Review
Vásquez-Iturralde, F., Flores-Calero, M. J., Grijalva, F., & Rosales, A. (2024) · IEEE Access · doi:10.1109/access.2024.3408282 · Cited by 23
“Therefore, tools and techniques for data management are urgently needed to generate useful insights from data in a timely and intelligent manner, upon which real-world applications are based [129].”
BackgroundCastro, C., Leiva, V., Garrido, D., Huerta, M., & Minatogawa, V. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108321 · Cited by 23
Maniscalco, M., Candia, C., Fuschillo, S., Ambrosino, P., Paris, D., & Motta, A. (2024) · Journal of Breath Research · doi:10.1088/1752-7163/ad7a9a · Cited by 22
“Unsupervised learning algorithms include clustering algorithms like k-means and dimensionality reduction techniques like principal component analysis [147, 151].”
InfluentialJose, P., Hariharan, S., Vimaladevi, M., Sujaudeen, N., Krishnamoorthy, M., & Cherukuri, A. K. (2024) · IET Quantum Communication · doi:10.1049/qtc2.12113 · Cited by 25
Mohammadi, I., Firouzabadi, S. R., Hosseinpour, M., Akhlaghpasand, M., Hajikarimloo, B., Nejad, S. Z., & Nia, P. S. (2024) · BMC Cardiovascular Disorders · doi:10.1186/s12872-024-04336-6 · Cited by 29
Stefanovska, E., & Pepelnjak, T. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103103 · Cited by 27
Metadata
- Authors
- Sarker, I. H.
- Affiliations
- Chittagong University of Engineering & Technology (BD)
- Corresponding author
- Sarker, Iqbal H.
- ORCID
- Sarker, Iqbal H.
- Venue
- SN Computer Science
- Published
- 2021-03-22
- Volume
- 2
- Issue
- 3
- Pages
- 160-160
- ISSN
- 2661-8907 (ISSN-L)
- 2662-995X
- PMC
- PMC7983091
- OpenAlex
- W3135028703
- Cited by
- 5,583
- References
- 153
- Citations by year
- 2026: 867 · 2025: 1685 · 2024: 1511 · 2023: 1008 · 2022: 416 · 2021: 96
- FWCI
- 54.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- SN Computer Science (publishedVersion)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- machine learning algorithms, supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, deep learning, Industry 4.0, Internet of Things data, cybersecurity, smart cities, medical applications, e-commerce, agricultural applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
