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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
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Items 601–700 of 1,013
Lee, Y., Won, J. H., Auh, Q., Noh, Y., & Lee, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-54120-x · Cited by 31
War, emotions, mental health, and artificial intelligence
Ćosić, K., Kopilaš, V., & Jovanović, T. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1394045 · Cited by 56
“Traditional and most common machine learning methods include: support vector machine (SVM), k-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Logistic Regression (LR; Sarker, 2021).”
MethodIbrahim, M. H., Badran, E. A., & Abdel-Rahman, M. H. (2024) · IEEE Access · doi:10.1109/access.2024.3466652 · Cited by 32
“Understanding the differences between these types of machine learning is crucial for selecting the most appropriate approach for a given problem or dataset; elaborating on practical considerations would benefit readers looking to apply these concepts in their work [68].”
BackgroundMadanchian, M. (2024) · Systems · doi:10.3390/systems12100415 · Cited by 37
Shiri, F. M., Yamaguchi, S., & Ahmadon, M. A. B. (2025) · Applied Sciences · doi:10.3390/app15062984 · Cited by 42
Silerio-Vázquez, F. D. J., Kakavandi, B., Tayyab, M., Baigenzhenov, O., Hosseini–Bandegharaei, A., & Proal-Nájera, J. B. (2025) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2025.216901 · Cited by 29
Alaraj, M., Radi, M., Alsisi, E. A., Majdalawieh, M., & Darwish, M. M. (2025) · Energies · doi:10.3390/en18174779 · Cited by 38
A review on AI Safety in highly automated driving
Wäschle, M., Thaler, F., Berres, A., Pölzlbauer, F., & Albers, A. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.952773 · Cited by 28
Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery
Kwofie, S. K., Adams, J., Broni, E., Enninful, K. S., Agoni, C., Soliman, M. E. S., & Wilson, M. D. (2023) · Pharmaceuticals · doi:10.3390/ph16030332 · Cited by 29
Ciuffreda, I., Casaccia, S., & Revel, G. M. (2023) · Sensors · doi:10.3390/s23156963 · Cited by 35
“The algorithm creates a tree-like model where each internal node represents a decision based on a feature, each branch represents the possible outcomes of that decision, and each leaf node represents the final predicted class or value [33].”
BackgroundMethodSolar‐Driven Hydrogen Evolution from Value‐Added Waste Treatment
Yu, S., Li, Y. L., Jiang, A., Chen, Y., Yuangang, D., Ye, J., & Zhou, Y. (2024) · Advanced Energy Materials · doi:10.1002/aenm.202304362 · Cited by 29
Neural Network Based Approach for Steady-State Stability Assessment of Power Systems
Badrudeen, T. U., Nwulu, N. I., & Gbadamosi, S. L. (2023) · Sustainability · doi:10.3390/su15021667 · Cited by 28
Echocardiographic artificial intelligence for pulmonary hypertension classification
Hirata, Y., Tsuji, T., Kotoku, J., Sata, M., & Kusunose, K. (2024) · Heart · doi:10.1136/heartjnl-2023-323320 · Cited by 28
Xiao, Y., Chen, Y., Huang, R., Jiang, F., Zhou, J., & Yang, T. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02214-5 · Cited by 34
“The emergence of machine learning (ML) algorithms presents an exciting opportunity to enhance DILI prediction models [8].”
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Artioli, P., Maci, A., & Magrì, A. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1375818 · Cited by 40
“Recent investigations have highlighted the widespread use of several clustering algorithm configurations for UEBA, denoting the need to examine in this direction (Landauer et al., 2020; Sarker, 2021).”
BackgroundAlabdali, M. A., Khan, S. A., Yaqub, M. Z., & Alshahrani, M. D. (2024) · Sustainability · doi:10.3390/su16114854 · Cited by 38
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Liaras, E., Nerantzidis, M., & Alexandridis, A. (2024) · Review of Quantitative Finance and Accounting · doi:10.1007/s11156-024-01306-z · Cited by 34
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A Review on the Application of Machine Learning in Gamma Spectroscopy: Challenges and Opportunities
Zehtabvar, M., Taghandiki, K., Madani, N., Sardari, D., & Bashiri, B. (2024) · Spectroscopy Journal · doi:10.3390/spectroscj2030008 · Cited by 27
Recent Advancements in Pharmaceutical 3D Printing Industry
Kulkarni, V. R., Saha, T., Giri, B. R., Lu, A., Das, S. C., & Maniruzzaman, M. (2024) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2024.106072 · Cited by 33
A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions
Al-Samahi, S., Zeiada, W., Al-Khateeb, G. G., Hamad, K., & Alnaqbi, A. J. (2024) · Infrastructures · doi:10.3390/infrastructures9100167 · Cited by 28
Kalan, R. S., & Dulger, I. (2024) · IEEE Access · doi:10.1109/access.2024.3491613 · Cited by 30
Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches
Patne, A. Y., Dhulipala, S. M., Lawless, W., Prakash, S., Mohapatra, S. S., & Mohapatra, S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252212233 · Cited by 32
“In short, ML encompasses the study and training of various types of algorithms that use input from datasets to predict an output independently [2].”
Jang, J.-W., Jo, J., Kim, J., Lee, S., Lee, T., & Yoo, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109704 · Cited by 29
Enhancing Customer Segmentation with Unsupervised Deep Learning
Madanchian, M., Farhaoui, Y., & Taherdoost, H. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-88304-0_1 · Cited by 28
Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review
Du, X., Gao, S., & Yang, G. (2025) · Gases · doi:10.3390/gases5020009 · Cited by 30
“There are various ML algorithms, each suited for different types of tasks [11,26,27].”
Alahmer, A. I., Turner, T., Al‐Dahidi, S., & Alrbai, M. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117464 · Cited by 28
Dada, B. A., Nwulu, N. I., & Olukanmi, S. O. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101136 · Cited by 28
Li, Z., & Xiao, Y. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07669-8 · Cited by 38
“A robust theoretical foundation is essential: understanding core concepts such as machine learning, neural networks, and data science is crucial for interpreting AI’s role in data analysis and clinical insights [96, 104, 107].”
Choi, J. W., Hidayat, M. S., Cho, S. B., Hwang, W.-H., Lee, H., Cho, B., Kim, M. S., Baek, I., & Kim, G. (2025) · Plants · doi:10.3390/plants14182841 · Cited by 39
“ML techniques have recently been used to predict crop yields under abnormal climate [19].”
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Al-Badri, A. H., Ismail, N. A., Al-Dulaimi, K., Rehman, A. U., Abunadi, I., & Bahaj, S. A. (2022) · IEEE Access · doi:10.1109/access.2022.3200603 · Cited by 33
“Deep Learning (DL)-based approaches are an extension of ML approaches to achieve encouraging results with real-world data using self-extracting features [13].”
BackgroundSingh, J., Sandhu, J. K., & Kumar, Y. (2024) · Service Oriented Computing and Applications · doi:10.1007/s11761-023-00382-8 · Cited by 31
“Likewise,inthecaseofunsupervised learning, the models are being trained with the unlabeled set of data which is used to obtain compact descriptions of the data and does not include any response variable as an output [8].”
Algorithmic management in scientific research
Koehler, M., & Sauermann, H. (2024) · Research Policy · doi:10.1016/j.respol.2024.104985 · Cited by 35
Integrating Prediction/Estimation and Optimization with Applications in Operations Management
Qi, M., & Shen, Z. M. (2022) · doi:10.1287/educ.2022.0249 · Cited by 32
Exploring Hyperparameter Usage and Tuning in Machine Learning Research
Simon, S., Kolyada, N., Akiki, C., Potthast, M., Stein, B., & Siegmund, N. (2023) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1109/cain58948.2023.00016 · Cited by 27
“The selection of these methods and their configuration settings strongly depend on the modality, domain and available training data [10], [11].”
BackgroundHu, Z., Zhang, L., Shen, Q., Chen, X., Wang, W., & Li, K. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.08.049 · Cited by 31
Gashkarimov, V. R., Ильдаровна, С. Р., Efremov, I. S., & Asadullin, A. R. (2023) · Consortium Psychiatricum · doi:10.17816/cp11030 · Cited by 36
“One of the advantages of using machine learning techniques is the ability to process large amounts of data, as well as the ability to analyze information that is diverse in nature [17]; e.”
BackgroundMaghami, M., Sattari, S. A., Tahmasbi, M., Panahi, P., Mozafari, J., & Shirbandi, K. (2023) · BioMedical Engineering OnLine · doi:10.1186/s12938-023-01172-1 · Cited by 34
“The CT-Scan, one of the most well-known imaging modalities, and has seen considerable break-throughs in ML and its application [15, 16].”
BackgroundWu, D., Xu, Y., Xu, F. L., Shao, M., & Huang, M. (2024) · Vibrational Spectroscopy · doi:10.1016/j.vibspec.2024.103672 · Cited by 27
Lu, H., Cantero‐Chinchilla, S., Yang, X. P. O., Gryllias, K., & Chronopoulos, D. (2024) · Composite Structures · doi:10.1016/j.compstruct.2024.118087 · Cited by 27
An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0
Damilos, S., Saliakas, S., Karasavvas, D., & Koumoulos, E. P. (2024) · Applied Sciences · doi:10.3390/app14104207 · Cited by 33
Robust detection of ductile fracture by acoustic emission data-driven unsupervised learning
Kwon, J.-H., Nguyen‐Nhat, T., Tran, M. T., Lee, H. W., Joo, H. S., Rhee, K., Park, S.-S., Kim, D. W., Jeong, Y.-G., & Kim, D.-K. (2024) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2024.109420 · Cited by 27
Environmental impact assessment of ocean energy converters using quantum machine learning
Rezaei, T., & Javadi, A. A. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.121275 · Cited by 27
Suriyaamporn, P., Pamornpathomkul, B., Wongprayoon, P., Rojanarata, T., Ngawhirunpat, T., & Opanasopit, P. (2024) · Pharmacia · doi:10.3897/pharmacia.71.e123549 · Cited by 27
A Review of Hydraulic Cylinder Faults, Diagnostics, and Prognostics
Kumar, P., Park, S., Zhang, Y., Jo, S.-H., Kim, H. S., & Kim, T. (2024) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-024-00639-3 · Cited by 27
“These ML requires features as input for efficient decision making [98, 99].”
Naeem, G., Asif, M., & Khalid, M. (2024) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2024.100779 · Cited by 32
A hybrid deep learning model for UWB radar-based human activity recognition
Khan, I., Guerrieri, A., Serra, E., & Spezzano, G. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101458 · Cited by 27
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Pons, J. V., Comerma-Montells, A., Escobet, T., Castaño, A. D. D., & Tarrés-Puertas, M. I. (2024) · Applied Sciences · doi:10.3390/app15010129 · Cited by 27
Karger, E., Rothweiler, A., Brée, T., & Ahlemann, F. (2025) · Urban Science · doi:10.3390/urbansci9040132 · Cited by 30
Puniya, B. L. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169181 · Cited by 30
Liang, Y., Li, D., Deng, D., Chu, C. H., Mei, M. L., Li, Y., Yu, N. H., He, J., & Cheng, L. (2025) · International Dental Journal · doi:10.1016/j.identj.2025.04.007 · Cited by 27
Atias, D., Ashri, S., Goldbourt, U., Benyamini, Y., Gilad-Bachrach, R., Hasin, T., Gerber, Y., & Obolski, U. (2025) · Annals of Epidemiology · doi:10.1016/j.annepidem.2025.07.024 · Cited by 30
Design Space Exploration of Hybrid Quantum–Classical Neural Networks
Kashif, M., & Al-Kuwari, S. M. (2021) · Electronics · doi:10.3390/electronics10232980 · Cited by 28
Data-driven causal inference of process-structure relationships in nanocatalysis
Ting, J. Y. C., & Barnard, A. S. (2022) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2022.100818 · Cited by 29
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Endut, N., Hamzah, W. M. A. F. W., Ismail, I., Yusof, M. K., Baker, Y. A., & Yusoff, H. (2022) · TEM Journal · doi:10.18421/tem112-20 · Cited by 27
Coronado-Blázquez, J. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1950 · Cited by 27
Zabala, I. D., Castañeda, L., Marín, V. I., & Turró, C. (2022) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-022-00359-1 · Cited by 27
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BackgroundZhang, H., & Han, Y. (2022) · Sensors · doi:10.3390/s22228977 · Cited by 27
“Its application covers all fields of artificial intelligence [9–11].”
BackgroundHossain, M. D., Scott, S. H., Cluff, T., & Dukelow, S. P. (2023) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-023-01140-9 · Cited by 32
Ouf, N. S. (2023) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2023.03.002 · Cited by 27
Tulla, P. S., Kumar, P., Vishwakarma, D. K., Kumar, R., Kuriqi, A., Kushwaha, N. L., Rajput, J., Srivastava, A., Pham, Q. B., Panda, K. C., et al. (2024) · Theoretical and Applied Climatology · doi:10.1007/s00704-024-04862-5 · Cited by 27
“…data has indeed lately been discovered to be beneficial in domains such as hydrology (Abrahart and See 2007; Hadi et al. 2019), fluvial hydraulics (Gleason 2015; Hartnett and Nash 2017), traffic engineering (Sarker 2021), and soil analysis (Besalatpour et al. 2012; Venkatesh and Bind 2022).”
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Tiwari, P., Chaudhary, S., Majhi, D., & Mukherjee, B. (2023) · Iberoamerican Journal of Science Measurement and Communication · doi:10.47909/ijsmc.36 · Cited by 26
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Durak, A., & Bulut, V. (2023) · Computer Applications in Engineering Education · doi:10.1002/cae.22679 · Cited by 32
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BackgroundMethodInfluentialRetracted: Machine Learning Techniques for Analysing Security Practises in Electronic Health Records
Saraswat, B. K., Saxena, A. K., & Vashist, P. C. (2023) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs59847.2023.10390093 · Cited by 26
“Up-to-date management is the primary and most crucial security degree [4].”
BackgroundRetractedA Future Direction of Machine Learning for Building Energy Management: Interpretable Models
Gugliermetti, L., Cumo, F., & Agostinelli, S. (2024) · Energies · doi:10.3390/en17030700 · Cited by 26
Revolutionizing physics: a comprehensive survey of machine learning applications
Suresh, R., Bishnoi, H., Kuklin, A. V., Parikh, A., Моlokeev, М. S., Harinarayanan, R., Gharat, S., & Hiba, P. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1322162 · Cited by 31
“Most problems can be mainly divided into classi fi cation analysis, regression analysis, data clustering, association rule learning, feature engineering for dimensionality reduction, as well as deep learning methods [64].”
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Abatan, A., Adeyinka, M. A., Sodiya, E. O., Jacks, B. S., Ugwuanyi, E. D., Daraojimba, O. H., & Lottu, O. A. (2024) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2024.11.1.0288 · Cited by 26
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BackgroundKlinavičius, T., Khinevich, N., Tamulevičienė, A., Vidal, L., Tamulevičius, S., & Tamulevičius, T. (2024) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.4c02459 · Cited by 28
“As a data-driven approach, an ML algorithm can learn the underlying patterns in the data and approximate them effectively, circumventing computational or analytical difficulties 54,55 .”
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“In the field of machine learning, the performance of algorithms heavily depends on the quality and characteristics of the data being used [1].”
BackgroundUsing Social Media Analysis to Improve E-commerce Marketing Strategies
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Taherdoost, H. (2024) · Information · doi:10.3390/info15080491 · Cited by 26
An intriguing future is approaching: Artificial intelligence meets molecularly imprinted polymers
Karasu, T., Çalışır, F., Pişkin, S., Özgür, E., Armutçu, C., Çorman, M. E., & Uzun, L. (2024) · Journal of Pharmaceutical and Biomedical Analysis Open · doi:10.1016/j.jpbao.2024.100041 · Cited by 26
Tiruneh, S. A., Rolnik, D. L., Teede, H. J., & Enticott, J. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105645 · Cited by 31
AI-driven photonics: Unleashing the power of AI to disrupt the future of photonics
Mahmoud, M. G., Hares, A. S., Hameed, M. F. O., El-Azab, M. S., & Obayya, S. S. A. (2024) · APL Photonics · doi:10.1063/5.0220766 · Cited by 29
Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis
Fayaz, S., Babu, L., Paridayal, L., Vasantha, M., Paramasivam, P., Karuppasamy, S., & Chinnaiyan, P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309151 · Cited by 26
Ji, H., Xu, Y., & Teng, G. (2024) · Poultry Science · doi:10.1016/j.psj.2024.104458 · Cited by 26
Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review
Almeida, G., Correia, F. B., Borges, A. R., & Bernardino, J. (2024) · Applied Sciences · doi:10.3390/app142210523 · Cited by 33
“ML algorithms have become pivotal tools in various fields [1], including health-care [2,3].”
Kousheh, S., & Lin, M. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104940 · Cited by 26
Artificial intelligence and its application in clinical microbiology
Mairi, A., Hamza, L., & Touati, A. (2025) · Expert Review of Anti-infective Therapy · doi:10.1080/14787210.2025.2484284 · Cited by 36
“Manifold learning techniques, such as t-SNE, aim to capture the underlying low-dimensional structure of high-dimensional data for better visualization [35,39].”
Machine Learning for Organic Fluorescent Materials
Zhong, J., Zhu, W., Shen, S., Zhou, N., Xi, M., Du, K., Wang, D., & Tang, B. Z. (2025) · Aggregate · doi:10.1002/agt2.70089 · Cited by 26
Catalysis meets machine learning: a guide to data-driven discovery and design
Casillo, E., Scattolin, T., & Nolan, S. P. (2025) · Chemical Communications · doi:10.1039/d5cc05274b · Cited by 35
An overview of the applications of Artificial Intelligence in Cybersecurity
Akhtar, M., & Feng, T. (2021) · EAI Endorsed Transactions on Creative Technologies · doi:10.4108/eai.23-11-2021.172218 · Cited by 26
“One of the greatest challenges in AI application in cybersecurity is the collection, management, and processing of unquantified data (structured, semistructured, unstructured, or meta-data) especially when dealing the real-world cybersecurity problems (Sarker, 2021).”
BackgroundPrivacy-Preserving and Explainable AI in Industrial Applications
Ogrezeanu, I. A., Vizitiu, A., Ciusdel, C. F., Puiu, A., Coman, S., Boldișor, C., Itu, A., Demeter, R., Moldoveanu, F. D., Suciu, C., et al. (2022) · Applied Sciences · doi:10.3390/app12136395 · Cited by 30
The Quality of Drinkable Water using Machine Learning Techniques
Pal, O. K. (2022) · International Journal of Advanced Engineering Research and Science · doi:10.22161/ijaers.96.2 · Cited by 26
Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms
Mao, Y. X., Wang, L., Chen, C., Yang, Z. Y., & Wang, J. (2023) · Laser & Photonics Review · doi:10.1002/lpor.202200357 · Cited by 28
Use of machine learning in the field of prosthetics and orthotics: A systematic narrative review
Choo, Y. J., & Chang, M. C. (2023) · Prosthetics and Orthotics International · doi:10.1097/pxr.0000000000000199 · Cited by 26
“47 Huang et al 16 also developed a training model by applying the Lagrangian function to solve an issue related to optimization of the SVM.”
BackgroundMethodDeLuca, N. M., Mullikin, A., Brumm, P., Rappold, A. G., & Hubal, E. A. C. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c03670 · Cited by 30
Bahadur, F. T., Shah, S. R., & Nidamanuri, . R. (2023) · Environmental Monitoring and Assessment · doi:10.1007/s10661-023-12001-2 · Cited by 29
“ANNs consist of networks of connected units or nodes called artificial neurons (Sarker 2021a).”
Saleh, M. A., Rasel, H. M., & Ray, B. (2024) · Green Technologies and Sustainability · doi:10.1016/j.grets.2024.100104 · Cited by 26
An empiric validation of linguistic features in machine learning models for fake news detection
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From beasts to bytes: Revolutionizing zoological research with artificial intelligence
Zhang, Y., 重庆师范大学生命科学学院重庆市媒介昆虫重点实验室, 重., Luo, Z., Sun, Y., Liu, J., Chen, Z., 重庆师范大学数学科学学院, 重. 4., & 重庆师范大学重庆国家应用数学研究中心, 重. 4. (2023) · 动物学研究 · doi:10.24272/j.issn.2095-8137.2023.263 · Cited by 25
“Machine learning attempts to utilize experience, usually in the form of data, to improve model performance and the process of learning (Sarker, 2021).”
BackgroundRaissi-Dehkordi, N., Raissi-Dehkordi, N., Toudeshki, K. K., & Farjoo, M. H. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.11.003 · Cited by 26
“Supervised learning, the main type of ML used in LQTS interpretation, involves us-ing labeled data for tasks like classi fi cation, 12 and encompasses a wide range of algorithms, such as neural networks.”
High spatial and temporal resolution multi-source anthropogenic heat estimation for China
Qian, J., Zhang, L., Schlink, U., Meng, Q., Liu, X., & Janscó, T. (2024) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2024.107451 · Cited by 25
Machine Learning for Unmanned Aerial Vehicles Navigation: An Overview
Fagundes-Junior, L. A., de Carvalho, K. B., Ferreira, R., & Brandao, A. S. (2024) · SN Computer Science · doi:10.1007/s42979-023-02592-5 · Cited by 30
“The main ANN structure are: Multi-Layer Percep-trons (MLPs), Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) [75].”
A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
Samborska-Goik, K., & Pogrzeba, M. (2024) · Applied Sciences · doi:10.3390/app14093675 · Cited by 33
Tendikov, N., Rzayeva, L., Saoud, B., Shayea, I. A. M., Azmi, M. H., Myrzatay, A., & Alnakhli, M. A. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102254 · Cited by 25
Robertshaw, H. H., Karstensen, L., Jackson, B. M., Granados, A., & Booth, T. C. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03208-w · Cited by 25
“AI, and in particular, machine learning (ML), has accelerated in recent years in its applications for data analysis and learning [18], with many areas of healthcare already making use of this technology for disease prediction and diagnosis [19, 20].”
Bammou, Y., Benzougagh, B., Igmoullan, B., Kader, S., Ouallali, A., Spalevıć, V., Sestraș, P., & Kuriqi, A. (2024) · Environmental Earth Sciences · doi:10.1007/s12665-024-11741-9 · Cited by 25
“P t : total amount of all pixels The determining factors Jenks' natural break approach was utilized to analyze the maps and classify LS, FS, and GES into subclasses (Sarker 2021).”
Discovery of novel materials through machine learning
Akinpelu, A., Bhullar, M., & Yao, Y. (2024) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ad6bdb · Cited by 29
“The choice of algorithm significantly impacts the system’s ability to make accurate predictions and generalize well to new data [43, 44].”
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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