The DOI matches its Crossref record; 151 of the 153 references it cites were found in a registry or an index.
Machine Learning: Algorithms, Real-World Applications and Research Directions
Open accessPublished version of a preprint
Abstract
In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The regularization techniques such as Ridge, Lasso, Elastic-Net, etc. [85, 105] can be used to optimize the linear regression model.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Besides, support vector regression, decision tree regression, random forest regression techniques [85, 105] can be used for building effective regression models depending on the problem type, e.g., non-linear tasks.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Advanced analytics of these data using machine learning techniques [105] can facilitate the efficient management of urban areas including real-time management, e.g., traffic flow management, evidence-based planning decisions which pertain to the longer-term strategic role of forecasting for urban planning, e.g., crime prevention, public safety, and security, or framing the future, e.g.,”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Advanced analytics of rural data including machine learning [105] modeling can facilitate providing new opportunities for them to build insights and capacity to meet current needs and prepare for their futures.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“These deep learning methods can perform well, particularly, when learning from large-scale datasets [105, 140].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
The cited source says what the claim attributes to it.
In the citing paper
“Such advanced analytics and decision-making based on machine learning techniques [105], a major part of artificial intelligence (AI) [102] can also play a significant role in the Fourth Industrial Revolution (Industry 4.0) due to its learning capability for smart computing as well as automation [121].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
The cited source says what the claim attributes to it.
In the citing paper
“The data can be structured, semi-structured, or unstructured, which increases day by day [105].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1×
“…based on CNN can be used in the field, such as AlexNet [60], Xception [24], In-Preprints ception [118], Visual Geometry Group (VGG) [44], ResNet [45], etc. – LSTM-RNN: Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the area of deep learning…”
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
(1967) · Cited by 23,719
Research works printed but not linked: 1
- Baldi P. Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, 2012; 37–49 .
Cited by
Sorted by citations
Items 201–300 of 1,013
Artificial Intelligence in Drug Discovery and Development
Mak, K., Wong, Y.-H., & Pichika, M. R. (2023) · Springer eBooks · doi:10.1007/978-3-030-73317-9_92-1 · Cited by 81
Yiğitcanlar, T., Senadheera, S., Marasinghe, R., Bibri, S. E., Sanchez, T. W., Cugurullo, F., & Sieber, R. E. (2024) · Cities · doi:10.1016/j.cities.2024.105151 · Cited by 106
Ahmed, W. M., Wani, M. A., Pławiak, P., Meshoul, S., Mahmoud, A., & Hammad, M. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-12353-4 · Cited by 75
“Additionally, some studies focus on isolated aspects of student performance, failing to provide a comprehensive analysis that accounts for various influential factors 6 .”
Artificial intelligence in salivary biomarker discovery and validation for oral diseases
Adeoye, J., & Su, Y. (2023) · Oral Diseases · doi:10.1111/odi.14641 · Cited by 85
Machine learning based recommender system for e-commerce
Loukili, M., Messaoudi, F., & Ghazi, M. E. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i4.pp1803-1811 · Cited by 74
“An association rule can be defined as a truth table that results from the combination of two or more features [33].”
Artificial intelligence and water quality: From drinking water to wastewater
Pérez-Beltrán, C. H., Robles, A. D., Rodríguez, N. A., Ortega-Gavilán, F., & Jiménez-Carvelo, A. M. (2024) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2024.117597 · Cited by 74
Revolutionizing CRISPR technology with artificial intelligence
Kim, M., Go, M.-J., Kang, S.-H., Jeong, S.-H., & Lim, K. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01462-9 · Cited by 87
“A subset of AI, ML, involves algorithms that learn from data to identify patterns and make predictions or decisions 23 .”
Islam, M. S., Islam, M. T., Sarker, S., Jame, H. A., Nishat, S. S., Jani, M. R., Rauf, A., Ahsan, S., Shorowordi, K. M., Efstathiadis, H., et al. (2022) · ACS Omega · doi:10.1021/acsomega.2c01076 · Cited by 76
Predictive Analytics in Business Analytics: Decision Tree
Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2021) · Advances in Decision Sciences · doi:10.47654/v26y2022i1p1-30 · Cited by 73
Ni, X., Hu, W., Fan, Q., Cui, Y., & Qi, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121303 · Cited by 73
Blanco, K., Salcidua, S., Orellana, P., Sauma-Pérez, T., León, T., Steinmetz, L. C. L., Ibañez, A., Duran-Aniotz, C., & de la Cruz, R. (2023) · Alzheimer's Research & Therapy · doi:10.1186/s13195-023-01304-8 · Cited by 73
“There are some variations of how to define the types of ML algorithms but commonly they can be divided into categories according to their purpose and the main categories are the following: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning [66].”
Gangadevi, E., Shobarani, R., Dhanaraj, R. K., & Nayyar, A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09295-1 · Cited by 73
Hatmal, M. M., Al-Hatamleh, M. A. I., Olaimat, A. N., Mohamud, R., Fawaz, M. A., Kateeb, E. T., Alkhairy, O. K., Tayyem, R., Lounis, M., Al‐Raeei, M., et al. (2022) · Vaccines · doi:10.3390/vaccines10030366 · Cited by 73
Meena, J., & Hasija, Y. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105505 · Cited by 72
Navidpour, A. H., Hosseinzadeh, A., Huang, Z., Li, D., & Zhou, J. L. (2022) · Catalysis Reviews · doi:10.1080/01614940.2022.2082650 · Cited by 71
“Like neural networks that employ gradient descent for weight optimization, the loss function is minimized by the gradient in GBM. [57] The learning process fits new models to yield a more accurate prediction of the response in GBMs. Maximizing the correlation between the newly constructed…”
BackgroundMethodInfluentialMethods, progresses, and opportunities of materials informatics
Li, C., & Zheng, K. (2023) · InfoMat · doi:10.1002/inf2.12425 · Cited by 79
Big data analytics deep learning techniques and applications: A survey
Selmy, H. A., Mohamed, H. K., & Medhat, W. (2023) · Information Systems · doi:10.1016/j.is.2023.102318 · Cited by 86
Satish, N., Anmala, J., Rajitha, K., & Varma, M. R. R. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102500 · Cited by 79
Hajikhani, M., Hegde, A., Snyder, J., Cheng, J., & Lin, M. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134208 · Cited by 75
Integrating Omics Data and AI for Cancer Diagnosis and Prognosis
Ozaki, Y., Broughton, P., Abdollahi, H., Valafar, H., & Blenda, A. V. (2024) · Cancers · doi:10.3390/cancers16132448 · Cited by 85
Atalla, S., Daradkeh, M. K., Gawanmeh, A., Khalil, H., Mansoor, W., Miniaoui, S., & Himeur, Y. (2023) · Mathematics · doi:10.3390/math11051098 · Cited by 71
“It is efficiently implemented to provide optimal accuracy results with minimal functionality [49].”
BackgroundIntegrated Photonic Tensor Processing Unit for a Matrix Multiply: A Review
Peserico, N., Shastri, B. J., & Sorger, V. J. (2023) · Journal of Lightwave Technology · doi:10.1109/jlt.2023.3269957 · Cited by 74
“THE latest decade has seen the exponential growth of Machine Learning (ML) as one of the main branches of the Artificial Intelligence field [1], [2].”
BackgroundSalinari, A., Machì, M., Díaz, Y. A., Cianciosi, D., Qi, Z., Yang, B., Cotorruelo, M. S. F., Villar, S. G., López, L. A. D., Battino, M. A., et al. (2023) · Diseases · doi:10.3390/diseases11030097 · Cited by 71
“Unsupervised learning uses algorithms that investigate hidden/natural patterns or relations within the data (especially in clustering, extraction and visualization without any pre-existing labels [17–22].”
MethodBelsti, Y., Moran, L., Du, L., Mousa, A., Silva, K. D., Enticott, J., & Teede, H. J. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105228 · Cited by 78
Artificial intelligence for hydrogen-enabled integrated energy systems: A systematic review
Mullanu, S., Chua, C., Molnar, A., & Yavari, A. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.08.013 · Cited by 71
Ajin, R. S., Segoni, S., & Fanti, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-72663-x · Cited by 78
Liu, C., Balasubramanian, P., An, J., & Li, F. (2025) · npj Clean Water · doi:10.1038/s41545-024-00429-z · Cited by 71
A systematic review on machine learning models for online learning and examination systems
Kaddoura, S., Popescu, D. E., & Hemanth, J. D. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.986 · Cited by 75
“handle high-dimensional and complex data ( Sarker, 2021 ).”
BackgroundGonzales‐Inca, C., Calle, M., Croghan, D., Haghighi, A. T., Marttila, H., Silander, J., & Alho, P. (2022) · Water · doi:10.3390/w14142211 · Cited by 70
“A complex system with more sophisticated GeoAI methods will demand a large and multidimensional dataset [42].”
MethodRetracted: Breast Cancer Screening Using Machine Learning Models
Singh, G., Chaturvedi, P., Shrivastava, A., & Singh, S. V. (2022) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem54221.2022.9853047 · Cited by 87
RetractedStratified K-fold cross validation optimization on machine learning for prediction
Widodo, S., Brawijaya, H., & Samudi, S. (2022) · SinkrOn · doi:10.33395/sinkron.v7i4.11792 · Cited by 102
“KNN can be used for both classification and regression (Sarker, 2021).”
Tizakast, Y., Kaddiri, M., Lamsaadi, M., & Makayssi, T. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105750 · Cited by 73
Can Artificial Intelligence (AI) Manage Behavioural Biases Among Financial Planners?
Hasan, Z., Vaz, D., Athota, V. S., Désiré, S. S. M., & Pereira, V. E. (2023) · Journal of Global Information Management · doi:10.4018/jgim.321728 · Cited by 87
“In recent years, AI has attained significant efficacy through supervised and unsupervised learning (Dhanaraj, Rajkumar & Hariharan, 2020; Sarker, 2021; Singh, Singh & Deka, 2021).”
BackgroundJiang, R., Shang, L., Wang, R., Wang, D., & Wei, N. (2023) · Environmental Science & Technology Letters · doi:10.1021/acs.estlett.3c00293 · Cited by 72
Panduman, Y. Y. F., Funabiki, N., Fajrianti, E. D., Fang, S., & Sukaridhoto, S. (2024) · Information · doi:10.3390/info15030153 · Cited by 75
Wang, H., Kang, X., & Han, B. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00780a · Cited by 76
Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Electronics · doi:10.3390/electronics12071558 · Cited by 77
“…• Cybersecurity and threat intelligence [17]; • Natural language processing and sentiment analysis [18]; • User behavior analytics and context-aware smartphone applications [14,15]; • E-commerce and product recommendations [14,15]; • Sustainable agriculture [19]; • Industrial applications [20].”
BackgroundHoxha, J., Çodur, M. Y., Mustafaraj, E., Kanj, H., & Masri, A. E. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121765 · Cited by 72
Machine learning applications in forensic DNA profiling: A critical review
Barash, M., McNevin, D. B., Fedorenko, V. A., & Giverts, P. (2023) · Forensic Science International Genetics · doi:10.1016/j.fsigen.2023.102994 · Cited by 78
A review of AI for optimization of 3D printing of sustainable polymers and composites
Hassan, M., Misra, M., Taylor, G. W., & Mohanty, A. K. (2024) · Composites Part C Open Access · doi:10.1016/j.jcomc.2024.100513 · Cited by 77
Islam, M., Bijjahalli, S., Fahey, T., Gardi, A., Sabatini, R., & Lamb, D. W. (2024) · Precision Agriculture · doi:10.1007/s11119-024-10112-5 · Cited by 81
Exploration of machine learning approaches for automated crop disease detection
Singla, A., Nehra, A., Joshi, K., Kumar, A., Tuteja, N. K., Varshney, R. K., Gill, S. S., & Gill, R. (2024) · Current Plant Biology · doi:10.1016/j.cpb.2024.100382 · Cited by 80
Megnidio-Tchoukouegno, M. M., & Adedeji, J. A. (2023) · Sustainability · doi:10.3390/su15032014 · Cited by 67
When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry
Tseng, Y. J., Chuang, P.-J., & Appell, M. (2023) · ACS Omega · doi:10.1021/acsomega.2c07722 · Cited by 73
Simeoli, R., Rega, A., Cerasuolo, M., Nappo, R., & Marocco, . (2024) · Review Journal of Autism and Developmental Disorders · doi:10.1007/s40489-024-00435-4 · Cited by 67
“The reliability of the classification typically depends on the learning algorithm used by the classifier, the size and quality of the training data (Sarker, 2021).”
From lab-scale to industrialization: atomically M–N–C catalysts for the oxygen reduction reaction
Zhao, T., Wang, J., Wei, Y., Zhuang, Z. C., Dou, Y., Yang, J., Li, W., & Wang, D. S. (2025) · Energy & Environmental Science · doi:10.1039/d5ee00074b · Cited by 68
Advancing precision oncology with AI-powered genomic analysis
Srivastava, R. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1591696 · Cited by 76
“These models typically perform well in structured, relatively low-dimensional, and properly preprocessed datasets (Sarker, 2021).”
Shin, J., Mahmud, M. S., Rehman, T. U., Ravichandran, P., Heung, B., & Chang, Y. K. (2022) · AgriEngineering · doi:10.3390/agriengineering5010003 · Cited by 66
A systematic review of machine learning approaches in carbon capture applications
Hussin, F., Rahim, S. A. N. M., Hatta, N. S. M., Aroua, M. K., & Mazari, S. A. (2023) · Journal of CO2 Utilization · doi:10.1016/j.jcou.2023.102474 · Cited by 66
Omics-based deep learning approaches for lung cancer decision-making and therapeutics development
Tran, T.-O., Vo, T. H., & Le, N. Q. K. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad031 · Cited by 68
Kiyak, E. O., Ghasemkhani, B., & Birant, D. (2023) · Electronics · doi:10.3390/electronics12183828 · Cited by 66
Rani, P., Kumar, R., Jain, A., Lamba, R., Sachdeva, R. K., Kumar, K., & Kumar, M. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10075-w · Cited by 66
Yu, J., Shvetsov, A. V., & Alsamhi, S. H. (2024) · IEEE Access · doi:10.1109/access.2024.3482987 · Cited by 84
“…models and algorithms that can learn from data and make judgments or predictions without the need for explicit programming. supervised learning, unsupervised learning, and reinforcement learning are the three main categories into which ML approaches fall in the context of cybersecurity [87].”
BackgroundThe Integration of AI and Metaverse in Education: A Systematic Literature Review
Almeman, K., Ayeb, F. E., Berrima, M., Issaoui, B., & Morsy, H. (2025) · Applied Sciences · doi:10.3390/app15020863 · Cited by 100
“According to [15,80], it is useful to consider the following classification and projection of algorithms for AI techniques: In Table 4, we have included some definitions of most AI algorithms discussed in this investigation.”
Student dropout prediction through machine learning optimization: insights from moodle log data
Marcolino, M. R., Porto, T. R., Primo, T. T., Targino, R., Ramos, V. F. C., Queiroga, E. M., Muñoz, R., & Cechinel, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-93918-1 · Cited by 66
“Studies have applied various machine learning algorithms, demonstrating their effectiveness in real-world educational settings 18 .”
Wang, S., Wang, Y., Wang, Y., & Wang, Z. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.109084 · Cited by 65
Synergy between Artificial Intelligence and Hyperspectral Imagining—A Review
Khonina, S. N., Kazanskiy, N. L., Oseledets, I. V., Nikonorov, A. V., & Butt, M. A. (2024) · Technologies · doi:10.3390/technologies12090163 · Cited by 69
“In contrast, unsupervised learning algorithms, such as k-means clustering [62], hierarchical clustering [63], and principal component analysis (PCA) [64], are employed to find hidden patterns or groupings in data without predefined labels [65].”
Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications
Binetti, M. S., Massarelli, C., & Uricchio, V. F. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6020059 · Cited by 78
A comprehensive review for chronic disease prediction using machine learning algorithms
Islam, R., Sultana, A., & Islam, M. R. (2024) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-024-00150-4 · Cited by 72
“Many researchers use hyperpa-rameter tuning techniques to improve accuracy [24, 25].”
Tripathy, A., Patne, A. Y., Mohapatra, S., & Mohapatra, S. S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252212368 · Cited by 71
“ML involves the study and training of various algorithms to analyze large datasets, identify patterns, and make independent predictions [1,2].”
A physics-informed neural network framework for laminated composite plates under bending
Wang, W., & Thai, H. (2025) · Thin-Walled Structures · doi:10.1016/j.tws.2025.113014 · Cited by 64
Mohan, S., Kumar, B., & Nejadhashemi, A. P. (2025) · Sustainability · doi:10.3390/su17030998 · Cited by 64
Javed, A. R., Zikria, Y. B., Rehman, S. U., Shahzad, F., & Jalil, Z. (2021) · doi:10.36227/techrxiv.14722854.v1 · Cited by 65
Yin, F., Ahmadianfar, I., Samadi-Koucheksaraee, A., Azarsa, R., Scholz, M., & Yaseen, Z. M. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.11.010 · Cited by 63
Explainable AI lacks regulative reasons: why AI and human decision-making are not equally opaque
Peters, U. (2022) · AI and Ethics · doi:10.1007/s43681-022-00217-w · Cited by 78
“) clusters and assigning data to them), and “reinforcement” learning (which involves optimizing a reward function through the consequences of decisions made in interactions with the environment) [56].”
BackgroundAvershina, E., Khezri, A., & Ahmad, R. (2023) · Antibiotics · doi:10.3390/antibiotics12040781 · Cited by 69
Alex, C., Creado, G., Almobaideen, W. A., Alghanam, O. A., & Saadeh, M. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103283 · Cited by 81
Network Slicing-Based Learning Techniques for IoV in 5G and Beyond Networks
Hamdi, W., Ksouri, C., Bulut, H., & Mosbah, M. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3372083 · Cited by 63
Gupta, R., & Singh, A. K. (2022) · New Generation Computing · doi:10.1007/s00354-022-00185-z · Cited by 65
“Nowadays, machine learning has gained a lot of attention in a wide range of real-world applications, including image recognition, spam detection, financial market analysis, and recommendation systems [4, 5].”
BackgroundTowards a Definition of Generative Artificial Intelligence
Ronge, R., Maier, M., & Rathgeber, B. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00863-y · Cited by 89
“In contrast to discriminative models, which are often used for classification tasks such as object recognition or spam detection (Sarker, 2021), generative models can be used to synthesize new data instances by approximating the underlying ground-truth reality of a class of observations.”
Niu, B., Wang, X., & Xu, Z. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.137188 · Cited by 64
ZainEldin, H. Y., Gamel, S. A., Talaat, F. M., Aljohani, M., Baghdadi, N. A., Malki, A., Badawy, M., & Elhosseini, M. A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10816-0 · Cited by 61
“On the other hand, reinforcement learning is centered around the process of instructing systems to make decisions by utilizing feedback obtained from their surrounding environment (Sarker 2021a; Janiesch et al. 2021).”
Haghirad, M., Heidari, S., & Hosseini, H. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111799 · Cited by 61
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Shaik, T., Tao, X., Xie, H., Li, L., Zhu, X., & Li, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3486109 · Cited by 113
“Machine learning (ML) refers to the process of training an algorithm to make predictions or decisions based on data [1].”
Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods
Revelou, P., Tsakali, E., Batrinou, A., & Strati, I. F. (2025) · Foods · doi:10.3390/foods14060922 · Cited by 76
“Classification concerns educating the computer program on a training dataset, which enables it to categorize the data according to the class labels [37,51].”
Omar, M., Agbareia, R., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2025) · JMIR Medical Informatics · doi:10.2196/66917 · Cited by 53
“For example, one possible explanation could be that data-rich or frequently discussed topics in training sets may be perceived as more certain [18], even if this does not translate into clinical accuracy.”
Daradkeh, M. K., Abualigah, L., Atalla, S., & Mansoor, W. (2022) · Electronics · doi:10.3390/electronics11132066 · Cited by 60
Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology
Sowers, A., Wang, G., Xing, M. M. Q., & Li, B. (2023) · Microorganisms · doi:10.3390/microorganisms11051129 · Cited by 64
Inverse design and AI/Deep generative networks in food design: A comprehensive review
Al-Sarayreh, M., Reis, M. G., Carr, A. J., & dos Reis, M. M. (2023) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2023.06.005 · Cited by 63
Putri, L. A., Rahman, I., Puspita, M., Hidayat, S. N., Dharmawan, A. B., Rianjanu, A., Wibirama, S., Roto, R., Trıyana, K., & Wasisto, H. S. (2023) · npj Science of Food · doi:10.1038/s41538-023-00205-2 · Cited by 60
Batz, P., Will, T., Thiel, S., Ziesche, T., & Joachim, C. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1150748 · Cited by 73
Uddin, M. G., Nash, S., Rahman, A., Dabrowski, T., & Olbert, A. I. (2023) · Environmental Research · doi:10.1016/j.envres.2023.117755 · Cited by 62
Generative AI for Consumer Behavior Prediction: Techniques and Applications
Madanchian, M. (2024) · Sustainability · doi:10.3390/su16229963 · Cited by 60
“Acquiring such data can be difficult in many real-world applications, particularly in developing industries like public health and e-commerce [98].”
Rishiwal, V., Agarwal, U., Yadav, M., Tanwar, S., Garg, D., & Guizani, M. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3535414 · Cited by 61
“ML algorithms fall into categories, such as supervised, unsupervised, semi-supervised, and reinforcement learning algorithms [57].”
Uddin, M. H., Mulla, M. H., Abedin, T., Manap, A., Yap, B. K., Rajamony, R. K., Shahapurkar, K., Khan, T. M. Y., Soudagar, M. E. M., & Nur‐E‐Alam, M. (2025) · Journal of Polymer Research · doi:10.1007/s10965-025-04282-7 · Cited by 68
“In recent years, AI has demonstrated significant promise in addressing complex problems in various sections of sci‑ ence and engineering, especially in the areas of energy and building sustainability, materials, and mechanical properties evaluation and prediction [19–21].”
Systematic Review of Financial Distress Identification using Artificial Intelligence Methods
Kuizinienė, D., Krilavičius, T., Damaševičius, R., & Maskeliūnas, R. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2138124 · Cited by 75
“This Data-Driven approach is widely used for feature extraction, clustering, association rules detection, density estimation, anomaly detection, etc. (Sarker 2021).”
BackgroundMethodHuang, Y., Harilal, S. S., Bais, A., & Hussein, A. (2023) · IEEE Transactions on Plasma Science · doi:10.1109/tps.2022.3231985 · Cited by 59
“enabled a paradigm shift from empirical modeling toward data-driven ML approaches [23].”
BackgroundKhan, W., Somers, W., Walker, S. S. W., de Bont, K., van der Velden, J., & Zeiler, W. (2023) · Energy · doi:10.1016/j.energy.2023.129213 · Cited by 59
Flood prediction with time series data mining: Systematic review
Hakim, D. K., Gernowo, R., & Nirwansyah, A. W. (2023) · Natural Hazards Research · doi:10.1016/j.nhres.2023.10.001 · Cited by 70
Wanyama, J., Bwambale, E., Kiraga, S., Katimbo, A., Nakawuka, P., Kabenge, I., & Oluk, I. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100412 · Cited by 59
Aklilu, E. G., & Bounahmidi, T. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.02.309 · Cited by 59
Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipeline
Alevizos, L., & Dekker, M. (2024) · Electronics · doi:10.3390/electronics13112021 · Cited by 83
“As new data is processed, the model can update its understanding, hence its predictions and recommendations are always based on the latest threat intelligence [42].”
MethodArtificial intelligence in smart drug delivery systems: a step toward personalized medicine
Panchpuri, M., Painuli, R., & Kumar, C. (2025) · RSC Pharmaceutics · doi:10.1039/d5pm00089k · Cited by 66
DDoS attack detection using MLP and Random Forest Algorithms
Najar, A. A., & Naik, S. M. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01003-x · Cited by 58
“Unlike binary, it is not limited to a single class [26].”
BackgroundMethodInfluentialPeng, Y., Zheng, C., Guo, S., Gao, F., Wang, X., Du, Z., Gao, F., Su, F., Zhang, W., Yu, X., et al. (2023) · npj Science of Food · doi:10.1038/s41538-023-00187-1 · Cited by 59
A Survey on Yogic Posture Recognition
Rajendran, A. K., & Sethuraman, S. C. (2023) · IEEE Access · doi:10.1109/access.2023.3240769 · Cited by 63
“This model was trendy in ML and Data Analysis [142].”
BackgroundMethodDing, C., Yan, X., Yuan, Z.-P., Yang, H., Fu, J., & Chen, Z. (2023) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2023.102070 · Cited by 57
Machine learning-based computation offloading in multi-access edge computing: A survey
Choudhury, A., Ghose, M., Islam, A., & Yogita, Y. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103090 · Cited by 57
Adaptive approximate computing in edge AI and IoT applications: A review
Damsgaard, H. J., Grenier, A., Katare, D., Taufique, Z., Shakibhamedan, S., Troccoli, T., Chatzitsompanis, G., Kanduri, A., Ometov, A., Ding, A. Y., et al. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103114 · Cited by 61
“This ensures the model converges towards an optimal behavior within its environment [40]. performing computations closer to the end devices [32,47,48].”
BackgroundInfluentialMachine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends
Valizadeh, A., & Amirhosseini, M. H. (2024) · SN Computer Science · doi:10.1007/s42979-024-03046-2 · Cited by 72
“While these models can provide accurate predictions, their inner workings can be difficult to interpret, limiting their usefulness for scientific discovery [ 69].”
Metadata
- Authors
- Sarker, I. H.
- Affiliations
- Chittagong University of Engineering & Technology (BD)
- Corresponding author
- Sarker, Iqbal H.
- ORCID
- Sarker, Iqbal H.
- Venue
- SN Computer Science
- Published
- 2021-03-22
- Volume
- 2
- Issue
- 3
- Pages
- 160-160
- ISSN
- 2661-8907 (ISSN-L)
- 2662-995X
- PMC
- PMC7983091
- OpenAlex
- W3135028703
- Cited by
- 5,583
- References
- 153
- Citations by year
- 2026: 867 · 2025: 1685 · 2024: 1511 · 2023: 1008 · 2022: 416 · 2021: 96
- FWCI
- 54.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- SN Computer Science (publishedVersion)
- Preprints.org (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- machine learning algorithms, supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, deep learning, Industry 4.0, Internet of Things data, cybersecurity, smart cities, medical applications, e-commerce, agricultural applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
