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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]
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]
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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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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 801–900 of 1,013
Akande, O., Okolie, J. A., Kimera, R., & Ogbaga, C. C. (2025) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2025.100260 · Cited by 25
Machine Learning Descriptors for CO2 Capture Materials
Orhan, I. B., Zhao, Y., Babarao, R., Thornton, A. W., & Le, T. C. (2025) · Molecules · doi:10.3390/molecules30030650 · Cited by 23
Alkharisi, M. K., & Dahish, H. A. (2025) · Sustainability · doi:10.3390/su17072913 · Cited by 22
Anomaly detection using machine learning and adopted digital twin concepts in radio environments
Moharam, M. H., Hany, O., Hany, A., Mahmoud, A., Mohamed, M., & Saeed, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-02759-5 · Cited by 22
“While effective, supervised methods face challenges in detecting unseen anomaly types during operation, which limits their flexibility in real world scenarios 18,19 .”
Wen, Q., Qiu, L., Qiu, C., Che, K., Zeng, R., Wang, X., Cao, P., Xing, L., Yang, Z., & Yu, J. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.217881 · Cited by 24
Kareem, S. A., Ibrahim, M. A., Anaele, J. U., Olanrewaju, O. F., Aikulola, E. O., & Bodunrin, M. O. (2025) · Next Materials · doi:10.1016/j.nxmate.2025.100864 · Cited by 24
Zhan, Z., Zhang, M., He, X., Li, X., Wang, Z., Chen, X., Han, B., Hu, W., Meng, Q., & Li, H. L. (2025) · International Journal of Structural Integrity · doi:10.1108/ijsi-12-2024-0209 · Cited by 24
A systematic review of AI-driven business models for advancing Sustainable Development Goals
Khan, M. A., Rahman, A., Mahmud, F. U., Bishnu, K. K., Ahmed, M., Mridha, M. F., & Aung, Z. (2025) · Array · doi:10.1016/j.array.2025.100539 · Cited by 32
van der Laag, R., Rizzato, A., Bäck, T., & Fan, Y. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.152556 · Cited by 22
Bammou, Y., Benzougagh, B., Ouallali, A., Kader, S., Raougua, M., & Igmoullan, B. (2025) · DYSONA – Applied Science · doi:10.30493/das.2025.484839 · Cited by 27
Discriminant analysis classification of residential electricity smart meter data
Neale, A., Kummert, M., & Bernier, M. A. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111823 · Cited by 22
Arunachalam, A., Berriel, S. N., Feit, C., Kumar, U., Seal, S., Basu, K., & Banerjee, P. (2021) · Journal of Vacuum Science & Technology A Vacuum Surfaces and Films · doi:10.1116/6.0001482 · Cited by 22
Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles
Chen, T.-C., Alazzawi, F. J. I., Guerrero, J. W. G., Chetthamrongchai, P., Dorofeev, A. E., Ismael, A. M., Ahmed, A. A. A., Akhmadeev, R., Latipah, A. J., & Abualrejal, H. M. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/3693263 · Cited by 22
“In particular, reinforcement learning can be used to achieve enhanced energy management in Hybrid Energy Storage Systems (HESSs) [17, 18].”
BackgroundMethodGuo, W., Shi, Z., Zeng, T., He, Y., Cai, Z., & Zhang, J. (2022) · Talanta · doi:10.1016/j.talanta.2022.123261 · Cited by 22
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Patil, A., Iyer, S., & Pandya, R. J. (2022) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-6684-3921-0.ch003 · Cited by 22
Moraliyage, H., Mills, N., Rathnayake, P., Silva, D. D., & Jennings, A. (2022) · Conference on Human System Interactions · doi:10.1109/hsi55341.2022.9869498 · Cited by 22
“Recent advancements in deep learning as well as artificial intelligence open new pathways to build complex machine learning models with high accuracy to provide solutions for real-world applications[10].”
BackgroundA Review on Machine Learning & It’s Algorithms
Jain, N., & Kumar, R. (2022) · International Journal of Soft Computing and Engineering · doi:10.35940/ijsce.e3583.1112522 · Cited by 22
Song, J., Bang, S., Choi, N., & Kim, H. N. (2022) · Biomicrofluidics · doi:10.1063/5.0121476 · Cited by 25
Hashmi, U. S., Akbar, S., Adve, R., Moo, P. W., & Ding, J. B. (2022) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.12337 · Cited by 26
Wong, K. L., Chou, K. S., Tse, R. T., Tang, S.-K., & Pau, G. (2023) · Electronics · doi:10.3390/electronics12030657 · Cited by 22
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Afaq, Y., & Manocha, A. (2023) · Journal of Computer Information Systems · doi:10.1080/08874417.2023.2173330 · Cited by 23
“32,33 Blockchain technology secures deep learning algorithms from exter-nal attacks such as data tampering, model modification, and many more.”
BackgroundAchieving Reliability in Cloud Computing by a Novel Hybrid Approach
Shahid, M. A., Alam, M. M., & Su’ud, M. M. (2023) · Sensors · doi:10.3390/s23041965 · Cited by 22
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Binkhonain, M., & Zhao, L. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100457 · Cited by 29
Artificial Intelligence in Teleradiology
Lobo, M. D. (2023) · Advances in medical education, research, and ethics (AMERE) book series · doi:10.4018/978-1-6684-7164-7.ch004 · Cited by 23
Jornkokgoud, K., Baggio, T., Faysal, M. O., Bakiaj, R., Wongupparaj, P., Job, R., & Grecucci, A. (2023) · Social Neuroscience · doi:10.1080/17470919.2023.2242094 · Cited by 22
“Supervised machine learning (SML) has been increasingly used for neuroscientific research (Elhai & Montag, 2020) to predict class labels or continuous variables of interest (Hinton, 2011; Sarker, 2021).”
MethodDicu, T., Cucoș, A. L., Botoș, M. L., Burghele, B.-D., Florică, Ş., Baciu, C. L., Ștefan, B.-I., & Bălc, R. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.167024 · Cited by 22
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Zhao, J., Wang, P., Li, B., & Bai, C. (2023) · Applied Sciences · doi:10.3390/app131910567 · Cited by 22
Chatterjee, M., & Roy, K. (2023) · Environmental Science Processes & Impacts · doi:10.1039/d3em00445g · Cited by 21
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Malik, S., & Zaheer, S. (2023) · Pathology - Research and Practice · doi:10.1016/j.prp.2023.154989 · Cited by 22
Shakerimov, A., Alizadeh, T., & Varol, H. A. (2023) · IEEE Access · doi:10.1109/access.2023.3339568 · Cited by 25
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Niu, B., Shanshan, E., Wang, X., Xu, Z., & Qin, Y. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308502120 · Cited by 22
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Ouf, S. M., Mahmoud, K. T., & Abdel-Fattah, M. A. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00922-9 · Cited by 22
“In [44, 54], the authors identified several advantages of the XGBOOST.”
BackgroundResultExplainable Artificial Intelligence in Quantifying Breast Cancer Factors: Saudi Arabia Context
Alelyani, T., AlShammari, M. M., Almuhanna, A., & Asan, O. (2024) · Healthcare · doi:10.3390/healthcare12101025 · Cited by 27
“Machine Learning (ML) techniques have demonstrated their robustness in the development of detection algorithms [9].”
BackgroundArtificial intelligence: a new cutting-edge tool in spine surgery
Kalanchiam, G. P., Chandramohan, T., Raman, M., & Kalyanasundaram, H. (2024) · Asian Spine Journal · doi:10.31616/asj.2023.0382 · Cited by 22
“The fundamental principles of ML are based on intricate statistical and mathematical optimization using numerical data [5].”
Buwaider, A., El-Hajj, V. G., MacDowall, A., Gerdhem, P., Staartjes, V. E., Edström, E., & Terander, A. E. (2024) · The Spine Journal · doi:10.1016/j.spinee.2024.10.010 · Cited by 22
“These models are suitable for analyzing tabular data, as used in this study [27].”
Introduction to Machine Learning
Morr, C. E., Jammal, M., Ali‐Hassan, H., & El-Hallak, W. (2022) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-16990-8_1 · Cited by 22
Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials
Donmazov, S., Saruhan, E. N., Pekkan, K., & Pişkin, Ş. (2024) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-024-00737-y · Cited by 23
“Over the past two decades, ML models have found extensive application across various domains, including mathematics [13], science [14], engineering and medicine [15].”
Overview of Sustainable Water Treatment Using Microbial Fuel Cells and Microbial Desalination Cells
Farahani, H., Haghighi, M., Usefi, M. M. B., & Ghasemi, M. (2024) · Sustainability · doi:10.3390/su162310458 · Cited by 21
Sathiparan, N. (2025) · Sustainable Chemistry and Pharmacy · doi:10.1016/j.scp.2025.101915 · Cited by 21
Taye, E. A., Woubet, E. Y., Hailie, G. Y., Arage, F. G., Zerihun, T. E., Zegeye, A., Zeleke, T. C., & Kassaw, A. T. (2025) · BMC Public Health · doi:10.1186/s12889-025-22007-9 · Cited by 25
“Machine Learning algorithms are typically designed to make accurate predictions by learning from data rather making prior assumptions [40].”
Kömürcü, D., & Edis, E. (2025) · Buildings · doi:10.3390/buildings15081301 · Cited by 21
Enhancing friction stir-based techniques with machine learning: a comprehensive review
El-Zathry, N. E., Akinlabi, S. A., Woo, W. L., Patel, V. V., Mahamood, R. M., & Sabry, I. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adcff6 · Cited by 21
“These systems enhance inspection accuracy, reduce human error, and enable the rapid detection and classification of defects, thereby boosting productivity and improving product quality [123].”
Feng, Y., Jiang, M., Yang, L., Ren, W., Wang, H., Yang, G., Chen, Y., Zhou, L., Shi, H. F., Shao, P., et al. (2025) · Separation and Purification Technology · doi:10.1016/j.seppur.2025.133334 · Cited by 23
Emerging nanoparticle-based strategies for advanced cancer imaging and diagnosis
Kamyab, H., Khalili, E., Yuzir, A., Taheri, M. M., Zambrano, A. K., & Rajendran, S. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.126046 · Cited by 24
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Rashed, B. M., & Popescu, N. (2021) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb52898.2021.9657673 · Cited by 21
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Ropelewska, E. (2022) · Agronomy · doi:10.3390/agronomy12040762 · Cited by 24
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Husen, A., Chaudary, M. H., & Ahmad, F. (2022) · ACM Computing Surveys · doi:10.1145/3524106 · Cited by 22
Lazzarini, N., Filippoupolitis, A., Manzione, P., & Eleftherohorinou, H. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271227 · Cited by 20
“Different techniques and algorithms exist within the machine learning field: from decision trees where predictions are made following a nested set of tests on attributes, to logistic regression that calculates the probability of events as a linear combination of input features, to boosted algorithms that combine many…”
BackgroundInfluentialElectrochemical sensors coupled with machine learning for food safety and quality inspection
Magarelli, G., Freire, A. M., & Silva, L. P. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-95988-9.00001-1 · Cited by 22
Alhalabi, W., Al‐Rasheed, A., Manoharan, H., Alabdulkareem, E., Alduailij, M. A., Alduailij, M. A., & Selvarajan, S. (2023) · Electronics · doi:10.3390/electronics12030747 · Cited by 21
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Thangaraja, M., Vajravelu, S. K., Kaliyaperumal, V., & Kalaivanan, D. (2023) · Optik · doi:10.1016/j.ijleo.2023.170691 · Cited by 21
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Zhou, J., You, D., Bai, J., Chen, X., Wu, Y., Wang, Z., Tang, Y., Zhao, Y., & Feng, G. (2023) · Cardiovascular Innovations and Applications · doi:10.15212/cvia.2023.0011 · Cited by 25
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Thacker, J. C. R., Bray, D. J., Warren, P. B., & Anderson, R. L. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c08232 · Cited by 23
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Xie, S., & Zhang, J. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120682 · Cited by 21
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Wang, X., Wen, H., Hu, T., & Wang, B. (2023) · Physics of Fluids · doi:10.1063/5.0154979 · Cited by 22
Liu, Q., Zhang, L., Ren, G., & Zou, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107466 · Cited by 22
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“…recently released nanoparticle-based Proteograph platform (SEER), in combination with advances in MS instrumentation and protein tagging methods [107], enable increased sample multiplexing and a deeper dive into the proteome, thereby increasing throughput and reproducibility and, most…”
BackgroundA novel plant disease diagnosis framework by integrating semi-supervised and ensemble learning
Sharma, P., & Sharma, A. (2023) · Journal of Plant Diseases and Protection · doi:10.1007/s41348-023-00803-y · Cited by 21
“For this reason, many investigations have turned their attention to traditional machine learning (ML) applications to automate plant disease diagnosis (Sarker 2021a, b).”
Dorosan, M., Dailisan, D. N., Valenzuela, J. F., & Monterola, C. P. (2023) · Cities · doi:10.1016/j.cities.2023.104587 · Cited by 21
Nazemian, A., Boulougouris, E. K., & Aung, M. Z. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12020216 · Cited by 21
“Therefore, a new model for such diversity with appropriate generalization to new predictions is desired in this field, leading to data mining approaches [3].”
BackgroundNajafi, B., Najafi, A., & Farahmandian, A. (2024) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3324542 · Cited by 21
“Machine learning and deep learning algorithms lead to the discovery of knowledge from data in less time and less cost [41], [42], and it can be said that they are used in most sciences [43], [44], [45], [46], [47], which can be a good reason for using machine learning and deep learning algorithms…”
BackgroundArtificial intelligence in routine blood tests
Santos-Silva, M. A., Sousa, N., & Sousa, J. C. (2024) · Frontiers in Medical Engineering · doi:10.3389/fmede.2024.1369265 · Cited by 24
Wang, H., Tang, Z., Tan, S. H., Wang, J., Liu, Y., Fang, H., Xia, C., & Wang, Z. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3639212 · Cited by 21
“The success of a supervised learning model is heavily dependent on having a suitable representation of the problem domain that can encode the essential information needed for the task at hand, such as vulnerability detection in our case [72].”
BackgroundSynergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances
Jakšić, Z. (2024) · Photonics · doi:10.3390/photonics11050442 · Cited by 21
Karthick, K., Aruna, S. K., Dharmaprakash, R., & Ravindiran, G. (2024) · Earth Science Informatics · doi:10.1007/s12145-024-01382-8 · Cited by 22
“In many real-world problems, the relationships between variables and the target outcome are complex and non-linear (Sarker 2021).”
Ramya, S., Srinath, S., & Tuppad, P. (2024) · Case Studies in Chemical and Environmental Engineering · doi:10.1016/j.cscee.2024.100822 · Cited by 23
Höfler, L. (2024) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2024.136528 · Cited by 24
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Singh, B., & Kaunert, C. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-5380-6.ch014 · Cited by 22
Yeganegi, M., Danaei, M., Azizi, S., Jayervand, F., Bahrami, R., Dastgheib, S. A., Rashnavadi, H., Masoudi, A., Shiri, A., Aghili, K., et al. (2025) · Frontiers in Pediatrics · doi:10.3389/fped.2025.1514447 · Cited by 26
“In contrast, DL utilizes neural networks, including convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data analysis, to handle complex data types (1, 31).”
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Chang, M. C., Kim, J. K., Park, D., Kim, J. H., Kim, C. R., & Choo, Y. J. (2023) · Healthcare · doi:10.3390/healthcare11192687 · Cited by 21
Siachos, N., Lennox, M., Anagnostopoulos, A., Griffiths, B. E., Neary, J. M., Smith, R. F., & Oikonomou, G. (2023) · Journal of Dairy Science · doi:10.3168/jds.2023-23894 · Cited by 23
“%) κ w S.E. 2 standard error of κ w 3 confidence interval of κ w from experience and improve without necessarily being programmed to (Sarker, 2021).”
BackgroundHoxha, V. (2024) · International Journal of Housing Markets and Analysis · doi:10.1108/ijhma-09-2023-0120 · Cited by 20
Palaniappan, V., Ishak, I., Ibrahim, H., Sidi, F., & Zukarnain, Z. A. (2024) · IEEE Access · doi:10.1109/access.2024.3418458 · Cited by 22
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Ghaffari, M., Aliahmadi, A., Khalkhali, A., Zakery, A., Daim, T. U., & Zamani, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102664 · Cited by 21
Machine Learning Application in Prediction of Scour Around Bridge Piers: A Comprehensive Review
Rahman, F., & Chavan, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10167-7 · Cited by 22
Bridging odorants and olfactory perception through machine learning: A review
Risheng, Z., Ji, Z., Wang, S., & Chen, H. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104700 · Cited by 21
Isaac, K. S., Combe, M., Potter, G., & Sokolenko, S. (2024) · Current Research in Food Science · doi:10.1016/j.crfs.2024.100842 · Cited by 22
“In contrast, RF models are more simplistic, built upon an ensemble of decision trees applied to user-defined features (Sarker, 2021).”
Preventing algorithm aversion: People are willing to use algorithms with a learning label
Chacon, A., Kausel, E. E., Reyes, T. H., & Trautmann, S. T. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.115032 · Cited by 21
Artificial intelligence and digital tools for design and execution of cardiovascular clinical trials
Hu, J., Power, J. R., Zannad, F. M., & Lam, C. S. P. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae794 · Cited by 27
Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning
Tavakoli, H., Rostami, R., Shalbaf, R., & Nazem‐Zadeh, M. (2024) · Brain and Behavior · doi:10.1002/brb3.70219 · Cited by 20
Artificial intelligence and human microbiome: A brief narrative review
Fonseca, D. C., Fernandes, G. D. R., & Waitzberg, D. L. (2025) · Clinical Nutrition Open Science · doi:10.1016/j.nutos.2024.12.009 · Cited by 23
Lázaro, F. L., Madeira, T., Melício, R., Valério, D., & Santos, L. F. F. M. (2025) · Aerospace · doi:10.3390/aerospace12020106 · Cited by 20
Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics
Brittin, N. J., Anderson, J. M., Braun, D. R., Rajski, S. R., Currie, C. R., & Bugni, T. S. (2025) · Journal of Natural Products · doi:10.1021/acs.jnatprod.4c01123 · Cited by 22
Kumar, S., & Kumar, R. (2025) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-025-10757-x · Cited by 24
Machine learning for screw design in single‐screw extrusion
Polychronopoulos, N. D., Moustris, K. P., Karakasidis, T. E., Sikora, J. W., Krasinskyi, V., Sarris, I. E., & Vlachopoulos, J. (2025) · Polymer Engineering and Science · doi:10.1002/pen.27170 · Cited by 20
Real-Time Detection of Forest Fires Using FireNet-CNN and Explainable AI Techniques
Alam, G. M. I., Tasnia, N., Biswas, T., Hossen, M. J., Tanim, S. A., & Miah, M. S. U. (2025) · IEEE Access · doi:10.1109/access.2025.3552352 · Cited by 20
“ML and DL models have undergone various strategies enabling models to generalize better, reduce over-fitting and improving the general performance [70], [71].”
Exploring the intersection of mechanobiology and artificial intelligence
Oria, R., Jain, K., & Weaver, V. M. (2025) · npj Biological Physics and Mechanics. · doi:10.1038/s44341-025-00010-w · Cited by 23
Applications of machine learning in potentially toxic elemental contamination in soils: A review
Li, Y., Bao, X., Wang, T., He, Y., Liu, X., Li, Y., Ren, S., Wang, E., & Guo, G. (2025) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2025.118110 · Cited by 20
Kong, G., Peng, J., & Corcoran, J. (2025) · Computational Urban Science · doi:10.1007/s43762-025-00178-w · Cited by 22
“Importantly, they enable models to self-adjust as new data becomes available, improving efficiency and scalability across diverse geographic and climatic conditions (Adithya et al., 2023; Sarker, 2021; Yogi et al., 2024).”
Advances in fault detection techniques for automated manufacturing systems in industry 4.0
Ahmed, Y. S., Abubakar, A. A., Arif, A. F. M., & Al-Badour, F. (2025) · Frontiers in Mechanical Engineering · doi:10.3389/fmech.2025.1564846 · Cited by 23
“Alternatively, a perceptron is a network containing only one hidden layer (Sarker, 2021).”
InfluentialNanou, E., Pliatsika, N., Stefas, D., Polygenis, D., & Couris, S. (2025) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2025.107650 · Cited by 20
Accelerating tribological material discovery: A machine learning framework with predictive insights
Zahoor, A., Rahman, A., & Mursaleen, M. (2025) · Tribology International · doi:10.1016/j.triboint.2025.110862 · Cited by 20
Automated design and optimization of concrete beams reinforced with stainless steel
Rabi, M., Abarkan, I., Sarfarazi, S., Ferreira, F. P. V., & Alkherret, A. J. (2025) · Structural Concrete · doi:10.1002/suco.70332 · Cited by 20
Machine learning applications in welding processes: Progresses and opportunities
Geng, P., Xia, Y.-J., Dong, Z., Men, B., Pan, B., Shao, C., Li, Y., & Li, J. (2025) · International Journal of Machine Tools and Manufacture · doi:10.1016/j.ijmachtools.2025.104344 · Cited by 20
Artificial Intelligence-Driven Development and Characterization of Nanomedicine
Okafor, N. I., Igbokwe, N. N., Onohuean, H., Faya, M. A., & Choonara, Y. E. (2026) · BioNanoScience · doi:10.1007/s12668-026-02476-x · Cited by 26
Gyamerah, S. A. (2021) · Complexity · doi:10.1155/2021/1767708 · Cited by 20
“But, the performance of machine learning models is highly dependent on the nature and characteristics of data [16] which makes it difficult for machine learning models to deeply mine the inner characteristics of the Bitcoin series.”
BackgroundMachine learning techniques with ANOVA for the prediction of breast cancer
(2022) · International Journal of Advanced Technology and Engineering Exploration · doi:10.19101/ijatee.2021.874555 · Cited by 20
A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors
Islam, U. I., Haque, E., Alsalman, D., Islam, M. N., Moni, M. A., & Sarker, I. H. (2022) · Annals of Data Science · doi:10.1007/s40745-022-00381-0 · Cited by 22
“Pearson’s chi-squared test [23] of independence is a non-parametric method that finds the relationship between two categorical variables based on frequency.”
MethodInfluentialSelf-citationPrediction of COVID-19 disease severity using machine learning techniques
Ahmed, A. H., Al-Hamadani, M. N. A., & Satam, I. A. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i2.3272 · Cited by 22
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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