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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 .
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Items 901–1,000 of 1,013
Dia, I., Ahvar, E., & Lee, G. M. (2022) · Network · doi:10.3390/network2020015 · Cited by 20
“5, and Classification and Regression Trees (CART) are well known for DT algorithms [18,21].”
BackgroundMethodNational Food Intake Assessment: Technologies to Advance Traditional Methods
Moshfegh, A. J., Rhodes, D. G., & Martin, C. (2022) · Annual Review of Nutrition · doi:10.1146/annurev-nutr-062320-110636 · Cited by 20
Ketkar, Y., & Gawade, S. D. (2022) · Healthcare Analytics · doi:10.1016/j.health.2022.100117 · Cited by 20
Raya, D., Shreya, A., Kumar, A., Giri, S. K., Salem, D. R., Gnimpiéba, E. Z., Gadhamshetty, V. R., & Dhiman, S. S. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1008536 · Cited by 24
“ML can be divided into supervised and unsupervised methods (Sarker, 2021).”
BackgroundInfluentialShaban, M., Alateyah, A. I., Alsharekh, M. F., Alawad, M. O., BaQais, A., Kamel, M., Alsunaydih, F. N., El-Garaihy, W. H., & Salem, H. G. (2023) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp7020052 · Cited by 20
Muqeet, M., Malik, H., Panhwar, S., Khan, I. U., Hussain, F., Asghar, Z., Khatri, Z., & Mahar, R. B. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.124180 · Cited by 20
A Deep Learning Approach for Predicting Multiple Sclerosis
de Leon-Sanchez, E. R. P., Domínguez-Ramírez, O. A., Herrera-Navarro, A. M., Rodríguez‐Reséndiz, J., Paredes-Orta, C., & Mendiola-Santibañez, J. D. (2023) · Micromachines · doi:10.3390/mi14040749 · Cited by 20
A survey on air pollutant PM2.5 prediction using random forest model
Babu, S., & Thomas, B. (2023) · Environmental Health Engineering and Management · doi:10.34172/ehem.2023.18 · Cited by 22
Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition
Achenbach, P., Laux, S., Purdack, D., Müller, P. N., & Göbel, S. P. (2023) · Sensors · doi:10.3390/s23249847 · Cited by 20
“The application of data augmentation would, therefore, be more effective for dynamic gestures and would probably achieve a better effect on accuracy in the area of deep learning , as these methods perform significantly better with a large amount of data than the traditional ML methods [34].”
MethodPouyanfar, N., Ahmadi, M., Ayyoubzadeh, S. M., & Ghorbani-Bidkorpeh, F. (2023) · Materials Today Communications · doi:10.1016/j.mtcomm.2023.107938 · Cited by 20
Improving wind speed forecasting at Adama wind farm II in Ethiopia through deep learning algorithms
Chaka, M. D., Semie, A. G., Mekonnen, Y. S., Geffe, C. A., Kebede, H., Mersha, Y., Anose, F. A., & Benti, N. E. (2023) · Case Studies in Chemical and Environmental Engineering · doi:10.1016/j.cscee.2023.100594 · Cited by 21
Predicting the rotational dependence of line broadening using machine learning
Guest, E. R., Tennyson, J., & Yurchenko, S. N. (2024) · Journal of Molecular Spectroscopy · doi:10.1016/j.jms.2024.111901 · Cited by 20
Sherbini, A. E., Coroneos, S., Zidan, A., & Othman, M. (2024) · Seminars in Thrombosis and Hemostasis · doi:10.1055/s-0044-1785482 · Cited by 20
“23,24 Thrombosis risk in cancer patients can be predicted by ML algorithms through analyzing clinical and genetic data, thus predicting the likelihood of thrombotic events.”
BackgroundGholampoor, H., & Asadi, M. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19020066 · Cited by 20
“Having emerged as pivotal technologies in the fourth industrial revolution [17,18], these methods enable the development of sophisticated models that consider both the multifaceted roles of financial ratios and the interrelationships between them.”
BackgroundHasib, K. M., Mridha, M. F., Mehedi, M. H. K., Faruk, K. O., Muna, R. K., Iqbal, S., Islam, M. R., & Watanobe, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3431933 · Cited by 20
“As we have said earlier, our dataset is very small for training heavy machine learning [19] and deep learning [20] models, but we are trying to show a direction on how the language impairment problem of children can be solved.”
BackgroundRazavi-Termeh, S. V., Sadeghi‐Niaraki, A., Abba, S. I., Ali, F., & Choi, S.-M. (2024) · Applied Water Science · doi:10.1007/s13201-024-02301-4 · Cited by 20
“Machine learning algorithms offer several advantages, including better accuracy, faster processing speed, higher robustness, and lower computational costs than traditional methods (Dargan et al. 2020; Sarker et al. 2021; Farahani et al. 2022).”
Leaf Disease Detection using Image Processing
Poovidha, S., Thaiyalnayaki, S., & Sampath, A. K. (2023) · International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) · doi:10.1109/iccams60113.2023.10526087 · Cited by 20
Astuti, P. K., Hegedűs, B., Oleksa, A., Bagi, Z., & Kusza, S. (2024) · Insects · doi:10.3390/insects15060418 · Cited by 19
“Furthermore, deep learning (DL) is a subset of ML that originated from the artificial neural network (ANN) but has outperformed it and is capable of performing more complex operations [35].”
BackgroundAutomated, IMU-based spine angle estimation and IMU location identification for telerehabilitation
Pan, H., Wang, H., Li, D., Zhu, K., Gao, Y., Yin, R., & Shull, P. B. (2024) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-024-01366-1 · Cited by 19
“Its linear nature demands relatively modest computational resources [27, 28] and facilitates seam-less implementation and deployment on smartphones [29, 30].”
BackgroundPoorana Senthilkumar, S., R, W. B. N., Rajendran, R. K., & Karthik, S. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.395 · Cited by 19
“Supervised ML classification algorithms have various applications in our daily lives [8].”
BackgroundA Systematic Review of AI-Enabled Frameworks in Requirements Elicitation
Siddeshwar, V., Alwidian, S., & Makrehchi, M. (2024) · IEEE Access · doi:10.1109/access.2024.3475293 · Cited by 19
“The recursive splitting algorithm they use is greedy, which may not always lead to the globally optimal tree structure [111].”
BackgroundMethodInfluentialZhang, J., Gao, H., Liu, Y., & Wang, J. (2024) · JOM · doi:10.1007/s11837-024-06922-7 · Cited by 21
Kushwaha, R., & Palei, N. N. (2024) · Pharmaceutical Medicine · doi:10.1007/s40290-024-00537-8 · Cited by 19
“This enables the simultaneous prediction of numerous outputs [80, 81].”
Canché-Cab, L., San‐Pedro, L., Bassam, A., Rivero, M., & Soberanis, M. A. E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10962-5 · Cited by 19
Su, S., Ahn, T. K., & Yang, Y. X. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202408480 · Cited by 19
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Ejeofobiri, C. K., Victor-Igun, O. O., & Okoye, C. I. (2024) · Asian journal of mathematics and computer research · doi:10.56557/ajomcor/2024/v31i48971 · Cited by 29
Wu, M., Goh, K. W., Chaw, K. H., Koh, Y. S., Dares, M., Yeong, C. F., Su, E. L. M., Holderbaum, W., & Zhang, Y. (2024) · Frontiers in Mechanical Engineering · doi:10.3389/fmech.2024.1383202 · Cited by 19
“The model ’ s training involves setting speci fi c numbers for trees, depth, and maximum leaf size, allowing it to adeptly handle complex data structures (Biau and Scornet, 2016; Ahmad et al., 2018; Sarker, 2021).”
The digital future of farming: A bibliometric analysis of big data in smart farming research
Paudel, B., Riaz, S., Teng, S. W., Kolluri, R. R., & Sandhu, H. (2024) · Cleaner and Circular Bioeconomy · doi:10.1016/j.clcb.2024.100132 · Cited by 30
Manohar, G., Reddy, V., & Vaishali, V. (2025) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-025-02274-x · Cited by 19
Personalized smart immersive XR environments: a systematic literature review
Huang, N., Goswami, P., Sundstedt, V., Hu, Y. Q., & Cheddad, A. (2025) · The Visual Computer · doi:10.1007/s00371-025-03887-9 · Cited by 19
“It is the method of learning from data or experience [131] and auto-mates analytical model building.”
Şimşek, F. F. (2025) · Paddy and Water Environment · doi:10.1007/s10333-025-01024-9 · Cited by 25
“Deep learning (DL) is a crucial approach in artificial intelligence, as it can analyze large-scale data and predict appropriate outcomes in real-world applications (Sarker 2021).”
Contemporary applications of artificial intelligence and machine learning in echocardiography
Raissi-Dehkordi, N., Raissi-Dehkordi, N., & Xu, B. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00064-8 · Cited by 25
Ahmed, M., Hussain, S., Pirzado, F. A., Gárate-Escamilla, A. K., Amaya, I., Ochoa‐Ruiz, G., & Ortíz-Bayliss, J. C. (2025) · Applied Sciences · doi:10.3390/app15148056 · Cited by 19
Garg, P. K., Krishna, M., Kulkarni, P., Horne, D. A., Salgia, R., & Singhal, S. S. (2025) · Cancers · doi:10.3390/cancers17172799 · Cited by 29
“In regard to OC, ML is beginning to assist in early diagnosis, identifying secretive features of biomarkers and scanning them radio-diagnostically [111].”
Manohar, G., Kumar, A., Bhowmik, A., & Santhosh, A. J. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107793 · Cited by 19
Bettahi, A., Belouadha, F.-Z., & Harroud, H. (2025) · Algorithms · doi:10.3390/a18100662 · Cited by 38
“Beyond these areas, ML finds use in everything, from cybersecurity and smart city infrastructures to business intelligence and educational settings, strengthening its cross-sector relevance in the Fourth Industrial Revolution [3].”
Habibnia, S., Faraji, M., Alizadeh, M. H., Zadeh, M. M., Caire, R., Gharehpetian, G. B., & Guerrero, J. M. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116592 · Cited by 19
Mim, J. J., Mamun, A. A., Nayem, M. H., Mahmud, S., Nath, A., Rahman, S., Fidal, S. A., & Hossain, N. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.114485 · Cited by 20
Stress Classification based on Speech Analysis of MFCC Feature via Machine Learning
Hilmy, M. S. H., Asnawi, A. L., Jusoh, A. Z., Abdullah, K., Ibrahim, S. N., Ramli, H. A. M., & Azmin, N. F. M. (2021) · International Conference on Computer and Communication Engineering · doi:10.1109/iccce50029.2021.9467176 · Cited by 19
“Not only that, they can also be used for regression and classification problems [7].”
BackgroundPredictive Maintenance Framework for Cathodic Protection Systems Using Data Analytics
Rossouw, E., & Doorsamy, W. (2021) · Energies · doi:10.3390/en14185805 · Cited by 19
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Emeka, O. J., Nahazanan, H., Kalantar, B., Khuzaimah, Z., & Ojogbane, S. S. (2021) · Land · doi:10.3390/land10100995 · Cited by 19
Kordi, M., Dehghan, M. J., Shayesteh, A. A., & Azizi, A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100891 · Cited by 21
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Bahrebar, S., Homayoun, S., & Ambat, R. A. (2022) · Corrosion Science · doi:10.1016/j.corsci.2022.110500 · Cited by 21
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Saini, V., & Kumar, R. (2022) · New Journal of Chemistry · doi:10.1039/d2nj02513b · Cited by 19
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Shin, J., Lee, J., Ko, T., Lee, K. H., Choi, Y., & Kim, H. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12111899 · Cited by 19
Estimating Permeability in a Limestone Geothermal Reservoir from Nmr Laboratory Experiments
Catinat, M., Fleury, M., Brigaud, B., Antics, M., & Ungemach, P. (2022) · Geothermics · doi:10.1016/j.geothermics.2023.102707 · Cited by 19
Hughes, B. K., Wallis, R., & Bishop, C. L. (2023) · Cell and Tissue Research · doi:10.1007/s00441-023-03768-4 · Cited by 21
Teeth Reconstruction Using Artificial Intelligence: Trends, Perspectives, and Prospects
Chau, R. C. W., Thu, K. M., Hsung, T.-C., & Lam, W. Y. H. (2023) · Journal of the California Dental Association · doi:10.1080/19424396.2023.2199910 · Cited by 21
A scoping review on deep learning for next-generation RNA-Seq. data analysis
Pandey, D., & Perumal, P. O. (2023) · Functional & Integrative Genomics · doi:10.1007/s10142-023-01064-6 · Cited by 20
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BackgroundMethodPlant Disease Detection Using Ensemble Learning
Kondaveeti, H. K., Ujini, K. G., Pavankumar, B. V. V., Tarun, B. S., & Gopi, S. C. (2023) · International Conference on Computational Systems and Communication (ICCSC) · doi:10.1109/iccsc56913.2023.10142982 · Cited by 19
“There are several types of machine learning algorithms that can be used for plant disease detection, including supervised learning algorithms, unsupervised learning algorithms, and semi-supervised learning algorithms [1].”
BackgroundAltered habenular connectivity in chronic low back pain: An fMRI and machine learning study
Mao, C., Wu, Y., Yang, H. J., Qin, J., Song, Q. C., Zhang, B., Zhou, X. Q., Zhang, L., & Sun, H. H. (2023) · Human Brain Mapping · doi:10.1002/hbm.26389 · Cited by 17
“In addition, machine learning has been increasingly used in pain research (D'Antoni et al., 2022; Matsangidou et al., 2021) to automatically identify brain signatures of patients and make accurate classifications (Sarker, 2021).”
BackgroundMethodAn Analysis of Residual Financial Contagion in Romania’s Banking Market for Mortgage Loans
Ionescu, Ș., Chiriță, N., Nica, I., & Delcea, C. (2023) · Sustainability · doi:10.3390/su151512037 · Cited by 19
Obonna, U. O., Opara, F. K., Mbaocha, C. C., Obichere, J.-K. C., Akwukwaegbu, I. O., Amaefule, M. M., & Nwakanma, C. I. (2023) · Future Internet · doi:10.3390/fi15080280 · Cited by 19
Application of Machine Learning Based on Structured Medical Data in Gastroenterology
Kim, H., Gong, E. J., & Bang, C. S. (2023) · Biomimetics · doi:10.3390/biomimetics8070512 · Cited by 20
“Supervised learning begins with the objective of prediction, which involves known output or target values [12] The most common supervised tasks are “classi fi cation” which categorizes data, and “regression” which fi ts data [13].”
Prasanth, A., & Alqahtani, H. (2023) · IEEE International Conference on Engineering Technologies and Applied Sciences (ICETAS) · doi:10.1109/icetas59148.2023.10346531 · Cited by 19
Dehghan-Souraki, D., López-Gómez, D., Bladé, E., Tetto, A. L. D., & Ramos, M. S. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.105979 · Cited by 19
Laxmi, B., Devi, P. U. M., Thanjavur, N., & Viswanath, B. (2024) · Current Microbiology · doi:10.1007/s00284-024-03750-5 · Cited by 21
“Real-time analysis of experimental data by AI algorithms enables researchers to identify trends and generate actionable insights, facilitating informed decision-making and rapid iteration on experimental designs [55].”
El‐Sanabary, S., Kouta, H. K., Shaban, M., Alrumayh, A., Alateyah, A. I., Alsunaydih, F. N., Alawad, M. O., El-Taybany, Y., El‐Asfoury, M. S., & El-Garaihy, W. H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e33967 · Cited by 19
AI-Driven Innovations in Pediatric Dentistry: Enhancing Care and Improving Outcome
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“ML is a subset of AI that uses statistical models and algorithms to identify patterns within large datasets, enabling predictive analysis and decision-making based on the data [7,8].”
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Mondillo, G., Colosimo, S., Perrotta, A., Frattolillo, V., & Gicchino, M. F. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13216559 · Cited by 19
Subudhi, P., Shoba, S., Rahul Narasimhan, A., Kumar, B., & Punetha, D. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.236639 · Cited by 20
Graph representation learning for structural proteomics.
Fasoulis, R., Παλιούρας, Γ., & Kavraki, L. E. (2021) · PubMed Central · doi:10.1042/etls20210225 · Cited by 8
Introduction to machine learning
Saifuzzaman, M., & Ananna, T. N. (2024) · Advances in computers · doi:10.1016/bs.adcom.2024.06.005 · Cited by 19
Ashayeri, H., Sobhi, N., Pławiak, P., Pedrammehr, S., Alizadehsani, R., & Jafarizadeh, A. (2024) · Cancers · doi:10.3390/cancers16112138 · Cited by 21
“However, it can be categorized into four main types: supervised, unsupervised, semi-supervised, and reinforcement learning [5,6].”
BackgroundJin, K., Li, Y., Shen, J., Lin, H. L., Fan, M., & Shi, J. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110852 · Cited by 18
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You, J., Seok, H. S., Kim, S., & Shin, H. (2024) · Annals of Laboratory Medicine · doi:10.3343/alm.2024.0354 · Cited by 22
“A DT model represents decision rules based on data features in a tree structure and is a supervised learning model used primarily for classification [62, 68].”
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Faust, O., Salvi, M., Barua, P. D., Chakraborty, S., Molinari, F., & Acharya, U. R. (2025) · Sensors · doi:10.3390/s25010205 · Cited by 21
“Performance measures play a crucial role in providing feedback to guide the model creation process [1,37].”
Castillo, J. L. U., Celestino, A. E. M., Cruz, D. A. M., Tuxpan-Vargas, J., Ramos‐Leal, J. A., & Morán‐Ramírez, J. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1479855 · Cited by 24
Πετμεζάς, Γ., Papageorgiou, V. Ε., Vassilikos, V. P., Pagourelias, E. D., Tachmatzidis, D., Tsaklidis, G. M., Katsaggelos, A. K., & Maglaveras, N. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104800 · Cited by 18
Use of Artificial Intelligence in Difficult Airway Assessment: The Current State of Knowledge
Wilk, M., Pikiewicz, W., Florczak, K., & Jakóbczak, D. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14051602 · Cited by 19
“Another type is unsupervised learning, which deals with data that has no labels and aims to uncover hidden patterns or relationships [4].”
Zou, Z., Tang, F., Qiao, L., Wang, S., & Zhang, H. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1528696 · Cited by 20
“Optimizing features, especially those highly correlated with drug susceptibility phenotypes, can significantly improve model performance (Sarker, 2021).”
Zhang, Y., Zhang, S., & Dinavahi, V. (2025) · eTransportation · doi:10.1016/j.etran.2025.100417 · Cited by 20
Stylos, N., Okumus, F., & Onder, I. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105205 · Cited by 18
Abreu-Dias, R., Santos-Gago, J. M., Rodríguez, F. M., & Sabucedo, L. M. Á. (2025) · Technologies · doi:10.3390/technologies13050187 · Cited by 18
“Among classical ML methods, the most commonly used are [41] random forest (RF), a classification or regression model that aggregates the predictions of multiple random decision trees using majority voting or averaging to produce the final outcome, support vector machine (SVM), a prediction model…”
Silva, M. A., Hamilton, E. J., Russell, D., Game, F. L., Wang, S. C., Baptista, S., & Monteiro‐Soares, M. (2025) · Journal of Medical Internet Research · doi:10.2196/69408 · Cited by 24
“ML algorithms—which include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning [13,14]—can use data from several sources, capture complex patterns, and thus may perform better than traditional models [15,16], especially in settings with high variability.”
Shaik, T., Tao, X., Li, L., Xie, H., Dai, H., Zhao, F., & Yong, J. (2025) · Brain Informatics · doi:10.1186/s40708-025-00262-1 · Cited by 18
“Reinforcement Learning (RL) addresses these gaps by enabling systems to learn through interaction with their environment [23].”
Alzahrani, R. M., Sikkandar, M. Y., Begum, S. S., Babetat, A. F. S., Alhashim, M. M., Alduraywish, A. A., Prakash, N. B., & Eddie, Y. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-11218-0 · Cited by 22
Shi, Y., Sun, M., Shi, H., Liang, Z., Qiao, B., Zhao, S., Pu, X., & Song, D. (2025) · 中国科学通报：英文版 · doi:10.1016/j.scib.2025.07.007 · Cited by 20
Calderone, A., Latella, D., Fauci, E. L., Puleo, R., Sergi, A., Francesco, M. D., Mauro, M., Foti, A., Salemi, L., & Calabrò, R. S. (2025) · Biomedicines · doi:10.3390/biomedicines13092118 · Cited by 23
“It also enables a hyper-drive of patient-centered ‘personalized’ care and tailoring and de-risking of business models in healthcare systems with new evidential bases for decisions, outcomes, and values [113].”
Ajin, R. S., Costache, R. D., Bărbulescu, A., Fanti, R., & Segoni, S. (2025) · Water · doi:10.3390/w17142041 · Cited by 20
Advancement of machine learning algorithms in biosensors.
Chin, W.-J., Lim, W., Khor, S. M., Ramakrishnan, N., Chee, P.-S., & Goh, C.-H. (2025) · Clinica chimica acta; international journal of clinical chemistry · doi:10.1016/j.cca.2025.120677 · Cited by 20
Artificial Intelligence-Driven Materials Design for Next-Generation Sustainable Energy Technologies
Fawzy, S. M., Ali, M. K. M., & Allam, N. K. (2026) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.6c01084 · Cited by 18
In silico QSAR modeling to predict the safe use of antibiotics during pregnancy
Çeli̇k, F. K., & Karaduman, G. (2022) · Drug and Chemical Toxicology · doi:10.1080/01480545.2022.2113888 · Cited by 19
“Machine learning utilizes different algorithms that take and evaluate input data to predict output values (Sarker 2021).”
Petrič, G., Cugmas, M., Petrič, R., & Atanasova, S. (2023) · Digital Health · doi:10.1177/20552076231155681 · Cited by 18
“Online support groups are intended for the exchange of advice and experiences among patients with similar symptoms and diseases and are usually somewhat moderated, though mostly in terms of interactive moderation, which is limited to facilitating and managing discussions and not correcting content.(50) Lastly, sociali…”
BackgroundSpatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine Learning Algorithms
Vivek, M., & Prathap, B. R. (2023) · SN Computer Science · doi:10.1007/s42979-023-01816-y · Cited by 18
“Sarker describes machine learning methods that can boost an application's intelligence and capabilities [12].”
BackgroundEl-Garaihy, W. H., Alateyah, A. I., Shaban, M., Alsharekh, M. F., Alsunaydih, F. N., El‐Sanabary, S., Kouta, H. K., El-Taybany, Y., & Salem, H. G. (2023) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp7040148 · Cited by 18
Behera, D., Kumar, V. A., Rao, J. P., Padal, S. B., Ayyappan, N., & Reddy, C. S. (2023) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-023-01740-x · Cited by 18
“One of the nonparametric techniques that has been deemed the most reliable and robust is random forest (RF) (Degenhardt et al., 2019; Rodriguez-Galiano et al., 2012; Sarker, 2021).”
Ershadi, A., Finkel, M., Susset, B., & Grathwohl, P. (2023) · Waste Management · doi:10.1016/j.wasman.2023.09.001 · Cited by 18
A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution
Long, N. K., Malaney, R., & Grant, K. J. (2023) · Information · doi:10.3390/info14100553 · Cited by 18
Khan, M. J., Singh, P. P., Pradhan, B., Alamri, A. M., & Lee, C.-W. (2023) · Sensors · doi:10.3390/s23218783 · Cited by 20
Jáuregui-Velarde, R., Andrade-Arenas, L., Molina-Velarde, P., & Yactayo-Arias, C. (2024) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v14i1.pp1079-1090 · Cited by 20
“Within this domain, various types of ML algorithms are present, encompassing supervised, unsupervised, semi-supervised, and reinforcement learning [8].”
BackgroundKim, D., Choi, H.-S., Lee, D., Kim, M., Kim, Y., Han, S., Heo, Y., Park, J.-H., & Park, J. (2023) · JMIR Formative Research · doi:10.2196/45202 · Cited by 18
“Machine learning algorithms have emerged as a promising approach for improving decision-making on specific questions related to rich multidimensional data and for medical research and clinical care [10-12].”
BackgroundMachine Learning and Deep Learning for Big Data Analysis
Pradhan, T., Nimkar, P., & Jhajharia, K. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-0413-6.ch008 · Cited by 18
Yang, H., Lu, S., & Yang, L. (2024) · Systematic Reviews · doi:10.1186/s13643-024-02449-9 · Cited by 20
“In addition, the selection of a model should also take into account factors such as the sample size, the nature of data, and the purpose of model construction [46].”
BackgroundPiven, A. O., Darmoroz, D. D., Скорб, Е. В., & Orlova, T. (2024) · Soft Matter · doi:10.1039/d3sm01634j · Cited by 18
Wu, Q. F., & Dai, J. (2024) · Journal of Bone and Mineral Research · doi:10.1093/jbmr/zjae025 · Cited by 18
Integration of IoT and Edge Computing in Industrial Systems
Yazdi, M. (2024) · Springer series in reliability engineering · doi:10.1007/978-3-031-53514-7_7 · Cited by 18
Leniak, A., Pietruś, W., & Kurczab, R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c02039 · Cited by 18
Design of Modified Polymer Membranes Using Machine Learning
Glaß, S., Schmidt, M., Merten, P., Latif, A. A., Fischer, K., Schulze, A., Friederich, P., & Filiz, V. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c18805 · Cited by 20
Menon, V., & Kolathayar, S. (2024) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-024-00417-3 · Cited by 18
“ML is a fundamental building block in pursuing artificial intelligence (AI) (Sarker 2021).”
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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