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Machine Learning: Algorithms, Real-World Applications and Research Directions
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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.
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“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]
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“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]
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“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]
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“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]
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“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]
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“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]
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“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]
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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 101–200 of 1,013
Machine learning assisted advanced battery thermal management system: A state-of-the-art review
Li, A., Weng, J., Yuen, A. C. Y., Wang, W., Liu, H., Lee, E. W. M., Wang, J., Kook, S., & Yeoh, G. H. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.106688 · Cited by 123
Human Action Recognition Systems: A Review of the Trends and State-of-the-Art
Karim, M. F. A., Khalid, S., Aleryani, A., Khan, J., Ullah, I., & Ali, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3373199 · Cited by 117
“ML techniques such as Random Forest (RF), KNN, ANN, and SVM have been used for HAR [125], [126], [127].”
MethodOwusu-Adjei, M., Hayfron-Acquah, J. B., Twum, F., & Abdul-Salaam, G. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000290 · Cited by 116
Alsabt, R., Alkhaldi, W., Adenle, Y. A., & Alshuwaikhat, H. M. (2024) · Cleaner Waste Systems · doi:10.1016/j.clwas.2024.100158 · Cited by 116
Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots
Su, J., He, K., Li, Y., Tu, J., & Chen, X. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00906 · Cited by 121
Reviewing Federated Machine Learning and Its Use in Diseases Prediction
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114
Mohammed, M. A., Abdulhasan, M. J., Kumar, N. M., Abdulkareem, K. H., Mostafa, S. A., Maashi, M. S., Khalid, L. S., Abdulaali, H. S., & Chopra, S. S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11537-0 · Cited by 114
Machine learning applications for electrospun nanofibers: a review
Subeshan, B., Atayo, A., & Asmatulu, E. (2024) · Journal of Materials Science · doi:10.1007/s10853-024-09994-7 · Cited by 116
“Reinforcement learning is a specialized ML algorithm that centers around the acquisition of knowledge through the evaluation of consequences, whether in the form of rewards or pun-ishments, resulting from specific actions taken within a given environment [28].”
Parvin, N., Joo, S., Jung, J. H., & Mandal, T. K. (2025) · Nanomaterials · doi:10.3390/nano15120895 · Cited by 131
“Logistic regression, a fundamental method in medical statistics, remains popular for its simplicity and interpretability in predicting binary outcomes, such as disease presence or absence [99].”
Onciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113
“Highly cited studies on tactile sensing are already paving the way for these advancements, demonstrating how AI can simulate lifelike sensations in prosthetic hands [293].”
Artificial Intelligence‐Driven Nanoarchitectonics for Smart Targeted Drug Delivery
Bae, H., Ji, H., Konstantinov, K., Sluyter, R., Ariga, K., Kim, J. H., & Kim, J. H. (2025) · Advanced Materials · doi:10.1002/adma.202510239 · Cited by 125
Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships
Kyriazos, T. A., & Poga, M. (2024) · Encyclopedia · doi:10.3390/encyclopedia4040118 · Cited by 139
“Machine learning’s predictive power makes it a valuable tool for understanding and forecasting social phenomena [47,48].”
Dai, N., Lei, I. M., Li, Z., Li, Y., Fang, P., & Zhong, J. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.108041 · Cited by 111
Bibri, S. E., Huang, J., & Krogstie, J. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105516 · Cited by 135
Razzaq, K., & Shah, M. H. (2025) · Computers · doi:10.3390/computers14030093 · Cited by 131
“Supervised learning is one of the prominent categories of ML [42].”
Obaido, G., Mienye, I. D., Egbelowo, O. F., Emmanuel, I. D., Ogunleye, A., Ogbuokiri, B., Mienye, P., & Aruleba, K. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100576 · Cited by 120
Yousef, L. A., Yousef, H., & Rocha‐Meneses, L. (2023) · Energies · doi:10.3390/en16248057 · Cited by 123
A vision chip with complementary pathways for open-world sensing
Yang, Z., Wang, T., Lin, Y., Chen, Y., Zeng, H., Pei, J., Wang, J., Liu, X., Zhou, Y., Zhang, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07358-4 · Cited by 110
Sokol, K., Fackler, J., & Vogt, J. E. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01725-9 · Cited by 105
“Building these systems now is especially timely given the increased availability of high-quality, large-scale, real-life data as well as leaps in AI, opening this technology up for many real-world applications 1 .”
Anomaly detection in high-energy physics using a quantum autoencoder
Ngairangbam, V. S., Spannowsky, M., & Takeuchi, M. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.095004 · Cited by 108
A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers
Chen, Z., Zhang, J. M., Sarro, F., & Harman, M. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3583561 · Cited by 130
“We also choose the three ML models that are the most widely adopted in the real world [66].”
Mahmoud, D., Magolon, M., Boer, J., Elbestawi, M. A., & Mohammadi, M. G. (2021) · Applied Sciences · doi:10.3390/app112411910 · Cited by 114
“It is a powerful tool that can help increase the automation and optimize the performance of sophisticated systems, but is not recommended for straightforward problems [113].”
BackgroundA review on machine learning techniques for secured cyber-physical systems in smart grid networks
Hasan, M. K., Abdulkadir, R. A., Islam, S., Gadekallu, T. R., & Safie, N. (2024) · Energy Reports · doi:10.1016/j.egyr.2023.12.040 · Cited by 104
Salemme, V., Centonze, G., Avalle, L., Natalini, D., Piccolantonio, A., Arina, P., Morellato, A., Ala, U., Taverna, D., Turco, E., et al. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1170264 · Cited by 106
Janga, J. K., Reddy, K. R., & Raviteja, K. V. N. S. (2023) · Chemosphere · doi:10.1016/j.chemosphere.2023.140476 · Cited by 111
Simón, C., Revilla, E., & Sáenz, M. J. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114783 · Cited by 139
“One key factor concerns the quantity and quality of the data used for training it (Kaplan & Haenlein, 2019; Sarker, 2021; Vial et al., 2021).”
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Khatun, M. A., Memon, S. F., Eising, C., & Dhirani, L. L. (2023) · IEEE Access · doi:10.1109/access.2023.3346320 · Cited by 128
“As depicted in Figure 12, ML is traditionally classified into three categories: supervised, unsupervised, and reinforcement learning [223], [224].”
BackgroundYoussef, A. M., Pradhan, B., Dikshit, A., Al-Katheri, M. M., Matar, S. S., & Mahdi, A. M. (2022) · Bulletin of Engineering Geology and the Environment · doi:10.1007/s10064-022-02657-4 · Cited by 106
“New advanced techniques using machine learning methods (conventional machine learning (CML) and deep learning (DL)) have been tested to be used as robust and promising techniques in various fields (Li et al. 2015; Perol et al. 2018; Sarker 2021; Sharma et al. 2021).”
MethodZero-shot learning for requirements classification: An exploratory study
Alhoshan, W., Ferrari, A., & Zhao, L. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107202 · Cited by 122
Ruksakulpiwat, S., Thorngthip, S., Niyomyart, A., Benjasirisan, C., Phianhasin, L., Aldossary, H., Ahmed, B., & Samai, T. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s459946 · Cited by 98
A review on big data based on deep neural network approaches
Rithani, M., Kumar, R. P., & Doss, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10512-5 · Cited by 106
“In order to simulate problems involving time-dependent and sequential data, such as text generation, artificial intelligence, and stock market prediction, recurrent neural networks (RNN) are employed to analyse sequential data (Sarker 2021).”
MethodShirzad, M., Shaban, M., Mohammadzadeh, V., Rahdar, A., Fathi‐karkan, S., Hoseini, Z. S., Najafi, M., Kharaba, Z. J., & Aboudzadeh, M. A. (2025) · BioNanoScience · doi:10.1007/s12668-025-01980-w · Cited by 107
“This approach is frequently implemented in robotics and gaming [220].”
“Forest fire emissions: A contribution to global climate change”
Singh, S. (2022) · Frontiers in Forests and Global Change · doi:10.3389/ffgc.2022.925480 · Cited by 98
“…(ML) approaches have revolutionized scientific discovery across disciplines, including Earth informatics, image processingbased applications (Sarker, 2021), satellite imagery (Abburu and Golla, 2015), sensor signal processing (Hong et al., 2015), big data processing (Zheng et al., 2018),…”
BackgroundTalat, A., & Khan, A. U. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103491 · Cited by 96
Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques
Hector, I., & Panjanathan, R. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2016 · Cited by 142
“Moreover, deep learning, a subset of ML also can intelligently examine enormous data ( Sarker, 2021 ).”
BackgroundMethodInfluentialExploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand
Rather, M. A., Ahmad, I., Shah, A., Hajam, Y. A., Amin, A., Khursheed, S., Ahmad, I., & Rasool, S. (2024) · Food Chemistry X · doi:10.1016/j.fochx.2024.101309 · Cited by 94
“Both data mining and machine learning techniques are widely used in a variety of industries and applications, including finance, healthcare, marketing, and more (Sarker, 2021).”
BackgroundImproved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
Battineni, G., Hossain, M. A., Chintalapudi, N., Traini, E., Dhulipalla, V. R., Mariappan, R., & Amenta, F. (2021) · Diagnostics · doi:10.3390/diagnostics11112103 · Cited by 93
“This helps in quick decision making [8].”
Machine learning in sustainable ship design and operation: A review
Huang, L., Peña, B., Liu, Y., & Anderlini, E. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.112907 · Cited by 101
“…this paper, there are several other areas in the marine field that have been facilitated by ML such as underwater vehicles (Anderlini et al., 2020b, 2021), wave energy converters (Anderlini et al., 2018b; Benites-Munoz et al., 2020), condition monitoring and maintenance (Adeli and Kim, 2004; Kim…”
BackgroundSarker, I. H. (2023) · Security and Privacy · doi:10.1002/spy2.295 · Cited by 110
Self-citationReviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine
Zafar, I., Anwar, S., kanwal, F., Yousaf, W., Nisa, F. U., Kausar, T., Ain, Q. U., Unar, A., Kamal, M. A., Rashid, S., et al. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105263 · Cited by 104
A review on machine learning approaches for microalgae cultivation systems
Syed, T., Krujatz, F., Ihadjadene, Y., Mühlstädt, G., Hamedi, H., Mädler, J., & Urbas, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108248 · Cited by 92
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91
Machine Learning: Models, Challenges, and Research Directions
Khoei, T. T., & Kaabouch, N. (2023) · Future Internet · doi:10.3390/fi15100332 · Cited by 107
Yadav, S., Singh, A., Singhal, R., & Yadav, J. P. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.02.009 · Cited by 115
Quantum Computing: Navigating the Future of Computation, Challenges, and Technological Breakthroughs
Memon, Q. A., Ahmad, M. A., & PECHT, M. G. (2024) · Quantum Reports · doi:10.3390/quantum6040039 · Cited by 121
Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022)
Mohammadi, A., & Sheikholeslam, F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106959 · Cited by 93
AI‐organoid integrated systems for biomedical studies and applications
Maramraju, S., Kowalczewski, A. C., Kaza, A., Liu, X., Singaraju, J. P., Albert, M. V., Ma, Z., & Yang, H. (2024) · Bioengineering & Translational Medicine · doi:10.1002/btm2.10641 · Cited by 95
“65 Although DL demands substantial computational power, large datasets, and length-ier training periods compared with standard ML models, it automatically extracts features, eliminating the need for manual feature identification — particularly advantageous for processing unstructured data like…”
BackgroundReal-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques
Essahraui, S., Lamaakal, I., Hamly, I. E., Maleh, Y., Ouahbi, I., Makkaoui, K. E., Bouami, M. F., Pławiak, P., Alfarraj, O., & El‐Latif, A. A. A. (2025) · Sensors · doi:10.3390/s25030812 · Cited by 102
“The primary goal of this paper was to evaluate the effectiveness of various CV [20–22] and machine learning (ML) techniques [23–26] in detecting driver drowsiness through facial analysis.”
Khanam, M. M., Uddin, M. K., & Kazi, J. U. (2025) · Frontiers in Environmental Science · doi:10.3389/fenvs.2025.1573579 · Cited by 101
“Supervised and unsupervised learning are two essential ML classes widely used in microplastic research (Figure 2), each distinguished by their speci fi c methodologies and applications (Sarker, 2021b).”
Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review
Macarringue, L. S., Bolfe, É. L., & Pereira, P. R. M. (2022) · Journal of Geographic Information System · doi:10.4236/jgis.2022.141001 · Cited by 102
“k-nearest neighbour, Case-based reasoning) and eager (Decision Tree, Naive Bayes, Artificial Neural Networks) and mainly divided into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning [81] [82].”
BackgroundAn Insight of Deep Learning Based Demand Forecasting in Smart Grids
Aguiar-Pérez, J. M., & Pérez-Juárez, M. A. (2023) · Sensors · doi:10.3390/s23031467 · Cited by 95
Kausik, A. K., Rashid, A. B., Baki, R. F., & Maktum, M. M. J. (2025) · Array · doi:10.1016/j.array.2025.100393 · Cited by 112
The principles of pedestrian route choice
Tong, Y., & Bode, N. W. F. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0061 · Cited by 106
A review of distributed acoustic sensing applications for railroad condition monitoring
Rahman, M. A., Taheri, H., Dababneh, F., Karganroudi, S. S., & Arhamnamazi, S. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110983 · Cited by 88
Ofori-Boateng, R., Aceves‐Martins, M., Wiratunga, N., & Moreno‐García, C. F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10844-w · Cited by 123
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Zhao, G., Li, Y., & Xu, Q. (2022) · International Journal of Network Dynamics and Intelligence · doi:10.53941/ijndi0101006 · Cited by 100
Theory, properties and engineering of 2D magnetic materials
Xing, S., Zhou, J., Zhang, X., Elliott, S. R., & Sun, Z. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.101036 · Cited by 85
Development of a neural network-based energy management system for a plug-in hybrid electric vehicle
Millo, F., Rolando, L., Tresca, L., & Pulvirenti, L. (2022) · Transportation Engineering · doi:10.1016/j.treng.2022.100156 · Cited by 93
Artsi, Y., Sorin, V., Glicksberg, B. S., Korfiatis, P. D., Nadkarni, G. N., & Klang, E. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1659134 · Cited by 113
“LLMs show promise in real-world clinical workflows (16), with the potential to enhance many fields in clinical care.”
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Zaben, M. M., Worku, M. Y., Hassan, M. A. E., & Abido, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3360330 · Cited by 94
“The main goal is to determine the actions to be taken in a given environment such that maximized the cumulative reward [49], [52].”
BackgroundInfluentialAl‐Mudhafar, W. J., Abbas, M. A., & Wood, D. A. (2022) · Marine and Petroleum Geology · doi:10.1016/j.marpetgeo.2022.105886 · Cited by 83
Kim, D., Song, Y., Kim, S., Lee, S., Wu, Y., Shin, J., & Lee, D. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122343 · Cited by 99
Ferro, M., Crocetto, F., Barone, B., Giudice, F. D., Maggi, M., Lucarelli, G. T., Busetto, G. M., Riccardo Autorino, B., Marchioni, M., Cantiello, F., et al. (2023) · Therapeutic Advances in Urology · doi:10.1177/17562872231164803 · Cited by 83
Biju, A. V. N., Thomas, A. S., & Thasneem, J. (2023) · Quality & Quantity · doi:10.1007/s11135-023-01673-0 · Cited by 105
“These algorithms independently detect complex patterns within the available data and uncover befitting combinations among variables to model accurate and precise predictions (Sarker 2021).”
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Artificial Intelligence in the Smart City — A Literature Review
Szpilko, D., Naharro, F. J., Lăzăroiu, G., Nica, E., & Gallegos, A. D. L. T. (2023) · Engineering Management in Production and Services · doi:10.2478/emj-2023-0028 · Cited by 110
Dangi, R. R., Sharma, A., & Vageriya, V. (2024) · Public Health Nursing · doi:10.1111/phn.13500 · Cited by 100
“DL, a specialized area within ML, utilizes neural networks with multiple layers to process and interpret large datasets, enabling the system to learn patterns and make complex decisions autonomously (Sarker 2021) (Abdusalomov, Mukhiddinov, and Whangbo 2023).”
Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete
Ali, A. A., Riaz, R. D., Malik, U. J., Abbas, S. B., Usman, M., Shah, M. U., Kim, I., Hanif, A., & Faizan, M. (2023) · Materials · doi:10.3390/ma16114149 · Cited by 82
Artificial intelligence in nanotechnology for treatment of diseases
Heydari, S., Masoumi, N., Esmaeeli, E., Ayyoubzadeh, S. M., Ghorbani‐Bidkorbeh, F., & Ahmadi, M. (2024) · Journal of drug targeting · doi:10.1080/1061186x.2024.2393417 · Cited by 86
Dhal, S. B., & Kar, D. (2024) · Forecasting · doi:10.3390/forecast6040046 · Cited by 96
Use of machine learning in osteoarthritis research: a systematic literature review
Binvignat, M., Pedoia, V., Butte, A. J., Louati, K., Klatzmann, D., Berenbaum, F., Mariotti‐Ferrandiz, E., & Sellam, J. (2022) · RMD Open · doi:10.1136/rmdopen-2021-001998 · Cited by 83
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
Karthick, K., K, A. S., Samikannu, R., Kuppusamy, R., Teekaraman, Y., & Thelkar, A. R. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/6517716 · Cited by 80
RetractedA Survey on Semantic Communications for Intelligent Wireless Networks
Iyer, S., Khanai, R., Torse, D. A., Pandya, R. J., Rabie, K. M., Pai, K., Khan, W. U., & Fadlullah, Z. M. (2022) · Wireless Personal Communications · doi:10.1007/s11277-022-10111-7 · Cited by 88
“In specific, the application of supervised learning, in which the testing and learning phases have a clear distinction, to any communication network provides immense opportunity for training the learner through aggregated models which are stochastic in nature via a simulator which can generate the input data, the chan…”
BackgroundAn Overview of Supervised Machine Learning Algorithm
Gupta, V., Mishra, V. K., Singhal, P., & Kumar, A. (2022) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart55829.2022.10047618 · Cited by 80
“Sarker, I.”
Martínez-Comesaña, M., Rigueira, X., Larrañaga, A., Martínez, J. M., Ocarranza-Prado, I., & Kreibel, D. (2023) · Revista de Psicodidáctica (English ed ) · doi:10.1016/j.psicoe.2023.06.002 · Cited by 126
Accelerating 3D printing of pharmaceutical products using machine learning
Ong, J. J., Muñiz, B., Gaisford, S., Cabalar, P., Basit, A. W., Pérez, G., & Goyanes, Á. (2022) · International Journal of Pharmaceutics X · doi:10.1016/j.ijpx.2022.100120 · Cited by 79
“ML has garnered considerable interests and accolades in recent years owing to its success in affording actionable insights across disciplines that humans and conventional strategies struggle or fail to provide (Kourou et al., 2015; Libbrecht and Noble, 2015; Sarker, 2021).”
BackgroundŞengönül, E., Samet, R., Al-Haija, Q. S. A., Alqahtani, A., Alturki, B., & Alsulami, A. A. (2023) · Applied Sciences · doi:10.3390/app13084956 · Cited by 79
A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine Learning
Mehmood, I., Shahid, S., Hussain, H., Khan, I., Ahmad, S., Rahman, S., Ullah, N., & Huda, S. (2023) · IEEE Access · doi:10.1109/access.2023.3287326 · Cited by 79
Chen, Q., Wang, J., Yao, F., Zhang, W., Qi, X., Gao, X., Liu, Y., Wang, J., Zou, M., & Liang, P. (2023) · Microchimica Acta · doi:10.1007/s00604-023-06044-y · Cited by 81
“Detection combined with AI algorithm technology can more effectively extract valuable information from spectral data [117].”
BackgroundKumar, S., Kumar, H., Kumar, G., Singh, S. P., Bijalwan, A., & Diwakar, M. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01192-w · Cited by 100
“Some of the algorithms developed for binary classification can also address multiclass concerns [105].”
BackgroundMethodInfluentialAlashhab, A. A., Zahid, M. S. M., Azim, M. A., Daha, M. Y., Isyaku, B., & Ali, S. (2022) · Symmetry · doi:10.3390/sym14081563 · Cited by 91
Martínez-Comesaña, M., Rigueira, X., Larrañaga, A., Martínez, J. M., Ocarranza-Prado, I., & Kreibel, D. (2023) · Revista de Psicodidáctica · doi:10.1016/j.psicod.2023.06.001 · Cited by 122
Hakami, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-59958-9 · Cited by 78
A Scoping Review of Artificial Intelligence for Precision Nutrition
Wu, X., Oniani, D., Shao, Z., Arciero, P. J., Sivarajkumar, S., Hilsman, J., Mohr, A. E., Ibe, S., Moharir, M., Li, L.-J., et al. (2025) · Advances in Nutrition · doi:10.1016/j.advnut.2025.100398 · Cited by 89
“Clustering is an unsupervised machine learning technique that groups a set of objects into clusters, where objects within the same cluster share greater similarity with each other than with those in different clusters [78].”
Rane, N. L., Choudhary, S., & Rane, J. (2024) · doi:10.61577/scpnmia.2024.100006 · Cited by 95
Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management
Rico-Chávez, A. K., Franco, J. A., Fernandez-Jaramillo, A. A., Contreras-Medina, L. M., Guevara-González, R. G., & Hernández-Escobedo, Q. (2022) · Plants · doi:10.3390/plants11070970 · Cited by 83
Radzi, P. N. L. M., Akhter, M. N., Mekhilef, S., & Shah, N. M. (2023) · Sustainability · doi:10.3390/su15042942 · Cited by 77
A Survey on BERT and Its Applications
Aftan, S., & Shah, H. (2023) · Learning and Technology Conference (L&T) · doi:10.1109/lt58159.2023.10092289 · Cited by 90
“The best way to use automated technologies for many populations and industrial requirements is to save time and money and obtain more accurate results than typical systems[1].”
MethodThe Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review
Suriyaamporn, P., Pamornpathomkul, B., Patrojanasophon, P., Ngawhirunpat, T., Rojanarata, T., & Opanasopit, P. (2024) · AAPS PharmSciTech · doi:10.1208/s12249-024-02901-y · Cited by 86
“However, when dealing with datasets that contain substantial noise or overlapping target classes, SVM's performance may be suboptimal [71].”
InfluentialOrabi, M. M., Emam, O. E., & Fahmy, H. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01099-5 · Cited by 77
“…on a specific process cycle including collecting reliable data where the machine learning model can find the correct pattern, data preprocessing, training model, evaluating the model by testing it using unknown data then hyperpa-rameter tuning and finally deploy the model to make a prediction [8].”
Samad, A., Ajmal, A., Mahmood, A., Khurshid, B., Li, P., Jan, S. M., Rehman, A. U., He, P., Abdalla, A. N., Umair, M., et al. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1060076 · Cited by 76
“When the dataset contains additional noise, such as overlapping target classes, SVM does not perform effectively (Sarker, 2021b).”
MethodIssaiy, M., Zarei, D., & Saghazadeh, A. (2023) · World Journal of Emergency Surgery · doi:10.1186/s13017-023-00527-2 · Cited by 78
“These techniques enhance diagnostic precision by learning from data and experience [9, 10].”
BackgroundHassan, A., Ahmad, S. G., Munir, E. U., Khan, I., & Ramzan, N. (2024) · Scientific Reports · doi:10.1038/s41598-024-61665-4 · Cited by 86
RetractedComputational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks
Mashhadimoslem, H., Abdol, M. A., Karimi, P., Zanganeh, K. E., Shafeen, A., Elkamel, A., & Kamkar, M. (2024) · ACS Nano · doi:10.1021/acsnano.3c13001 · Cited by 80
Progress of machine learning-based biosensors for the monitoring of food safety: A review
Hassan, M. M., Xu, Y., Sayada, J., Zareef, M., Shoaib, M., Chen, X., Li, H., & Chen, Q. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.116782 · Cited by 76
Secure IoT Healthcare Architecture with Deep Learning‐Based Access Control System
Thilagam, K., Beno, A., Lakshmi, M. V., Wilfred, C. B., George, S. M., Karthikeyan, M., Vijayakumar, P., Ramesh, C., & Karunakaran, P. (2022) · Journal of Nanomaterials · doi:10.1155/2022/2638613 · Cited by 75
Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches
Ahamad, M. M., Aktar, S., Uddin, M. J., Rahman, T., Alyami, S. A., Al-Ashhab, S., Akhdar, H., Azad, A., & Moni, M. A. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12081211 · Cited by 85
“The usage of machine learning models is a broadly recognized mechanism for showing disease-related factors as distinguishing markers in predictive patient diagnostics [25,26].”
BackgroundAslam, B., Maqsoom, A., Cheema, A. H., Ullah, F., Alharbi, A., & Imran, M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3221430 · Cited by 75
“Such AI algorithms are gaining popularity because of their nonlinear structures, capacity to forecast complicated events, ability to handle large datasets, and lack of sensitivity to missing data [16].”
BackgroundArchitectural spatial layout planning using artificial intelligence
Ko, J., Ennemoser, B., Yoo, W.-J., Yan, W., & Clayton, M. J. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105019 · Cited by 83
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
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