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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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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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BackgroundInfluentialData Analytics for Optimizing and Predicting Employee Performance
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Advanced prediction of perovskite stability for solar energy using machine learning
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Advances in machine intelligence‐driven virtual screening approaches for big‐data
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“e72-3 broad sets of methods; more human-configured, “ symbolic ” machine reasoning and more data-driven, “ neural ” ML (UNESCO, 2021a:10; Sarker et al., 2021).”
Multi-modal deep learning for credit rating prediction using text and numerical data streams
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MethodGlacierNet2: A hybrid Multi-Model learning architecture for alpine glacier mapping
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“ML could be further categorized into 1) supervised ML, such as support vector machine (SVM) and random forest (RF) classifier, 2) unsupervised ML, 3) semi-supervised ML and 4) reinforcement ML [3].”
MethodDevelopment of a deep Q-learning energy management system for a hybrid electric vehicle
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“…fi cation algorithms range from logistic regression, linear discriminant analysis, naive Bayes [85], K ‐ nearest neighbours [78], support vector machine [5], decision tree [30], random forest [68], gradient boosting [7] and rule ‐ based classi fi cation [68] algorithms to deep NNs [67] (Table 2).”
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Review and Empirical Analysis of Machine Learning-Based Software Effort Estimation
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“It is particularly useful for problems where the decision boundaries are non-linear and where the data is low-dimensional [68].”
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Predictive analytics of wear performance in high entropy alloy coatings through machine learning
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Enhancing Urban Sustainability: Developing an Open-Source AI Framework for Smart Cities
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Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides
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Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
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“Previous research has extracted and utilized a wide range of features, from simple to complex, as well as a wide range of learning and clas-sification algorithms, from traditional machine learning techniques to deep learning [6,7].”
MethodRole of artificial intelligence in environmental sustainability
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“This increased utilization of structured over unstructured datasets may be attributed to the common choice of conventional ML architectures such as support vector machines, k-nearest neighbors, and ensemble learners that requires highly organized data for their implementation in this study [193].”
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“But, as per the type of data and the size of samples, ML algorithms can be selected for better performance [75].”
Yolo for Detecting Plant Diseases
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Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data
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“An extensive review of the most common ML techniques is presented in [2,3].”
BackgroundMethodInfluentialIn silico toxicity studies of traditional Chinese herbal medicine: A mini review
Zulkifli, M. H., Abdullah, Z. L., Yusof, N. I. S. M., & Fauzi, F. M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102588 · Cited by 31
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Reservoir Property Prediction in the North Sea Using Machine Learning
Al‐Fakih, A., Kaka, S. I., & Koeshidayatullah, A. (2023) · IEEE Access · doi:10.1109/access.2023.3336623 · Cited by 31
“Numerous studies have utilized machine learning techniques (MLT) to predict porosity, permeability, and resistivity from well logs, often achieving high levels of accuracy and surpassing traditional models in some cases [10], [11], [12], [13], [14], [15], [16].”
MethodThe Potential of Big Data and Machine Learning for Ground Water Quality Assessment and Prediction
Rajeev, A., Shah, R., Shah, P., Shah, M., & Nanavaty, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10156-w · Cited by 31
“Because the nature and quality of the data influence the success and potency of a Machine Learning-based solution [34].”
InfluentialDesigning green logistics networks under carbon tax policy: Post-COVID condition
Abbasi, S., Mazaheri, S., Talaie, H., & Ghasemi, P. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102830 · Cited by 31
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“By leveraging AI-based approaches, power plants can optimize their emission control strategies, reduce costs associated with monitoring and compliance, and ultimately contribute to the global effort to combat climate change [33].”
Rädler, S., Berardinelli, L., Winter, K., Rahimi, A., & Rinderle‐Ma, S. (2024) · Software & Systems Modeling · doi:10.1007/s10270-024-01211-y · Cited by 31
“It has proven to be beneficial in several engineering areas such as manufacturing [31], aerospace industry [17] or other industrial applications [10, 34, 79].”
Li, B., & Gilbert, S. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01345-9 · Cited by 42
Kokori, E., Patel, R., Olatunji, G. D., Ukoaka, B. M., Abraham, I. C., Ajekiigbe, V. O., Kwape, J. M., Babalola, A. E., Udam, N. G., & Aderinto, N. (2024) · Heart Failure Reviews · doi:10.1007/s10741-024-10474-y · Cited by 31
“ML models provide evidence-based insights that support decision-making processes, leading to more reliable and standardized care practices [39].”
Artificial Intelligence (AI) Empowerment in E-Commerce: A Bibliometric Voyage
Chugh, P., & Jain, V. (2024) · NMIMS Management Review · doi:10.1177/09711023241303621 · Cited by 31
Chen, X., Lei, Z., Chang, J., & Lee, D. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.145467 · Cited by 35
Insights into radiomics: a comprehensive review for beginners
Mariotti, F., Agostini, A., Borgheresi, A., Marchegiani, M., Zannotti, A., Giacomelli, G., Pierpaoli, L., Tola, E., Galiffa, E., & Giovagnoni, A. (2025) · Clinical & Translational Oncology · doi:10.1007/s12094-025-03939-5 · Cited by 43
“Firstly, it is necessary to decide whether to proceed with supervised or unsupervised learning [68].”
Krishnamurthy, S., Adewuyi, O. B., & Salimon, S. A. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116340 · Cited by 33
A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
Lian, B., Wei, Z., Sun, X., Li, Z., & Zhao, J. (2022) · Sensors · doi:10.3390/s22124395 · Cited by 31
Survey and Comparison of Milliwatts Micro controllers for Tiny Machine Learning at the Edge
Giordano, M., Piccinelli, L., & Magno, M. (2022) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas54282.2022.9870017 · Cited by 31
“It has been enabled by the large amount of data continuously generated and the growing computational capabilities, and can now be considered pervasive in everyday life, being at the core of applications such as speech recognition and computer vision [1].”
BackgroundPore, S., Banerjee, A., & Roy, K. (2023) · Sustainable Energy & Fuels · doi:10.1039/d3se00457k · Cited by 31
Ma, X., & Huo, Y. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103755 · Cited by 30
Ameen, S., Wong, M. C., Yee, K., & Turner, P. A. (2022) · Applied Sciences · doi:10.3390/app12073341 · Cited by 31
Reprint of: Precision nutrition: A review of current approaches and future endeavors
Livingstone, K. M., Ramos-López, O., Përusse, L., Kato, H., Ordovás, J. M., & Martínez, J. A. (2022) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2022.10.010 · Cited by 31
Early Prediction of Diabetes Using Feature Selection and Machine Learning Algorithms
Abdollahi, J., & Aref, S. (2024) · SN Computer Science · doi:10.1007/s42979-023-02545-y · Cited by 30
“Thus, to intelligently analyze these data and construct the related real-world applications, machine learning algorithms are the key [36].”
Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms
Kiran, S. M., & Chandrappa, D. N. (2023) · Journal of Robotics and Control (JRC) · doi:10.18196/jrc.v4i6.20342 · Cited by 30
“Notable machine learning algorithms viz., Random Forest Classifier (RFC), Logistic Regression Classifier (LRC), Support Vector Machine (SVM), Decision Tree Classifier (DTC), Linear Discriminant Analysis (LDA), and 𝐾 -Nearest Neighbor (K-NN) [22].”
BackgroundUse of machine learning models for the prognostication of liver transplantation: A systematic review
Chongo, G., & Soldera, J. (2024) · World Journal of Transplantation · doi:10.5500/wjt.v14.i1.88891 · Cited by 32
Gaur, S., & Drewry, D. T. (2024) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2024.109955 · Cited by 30
Real-World Applications of Data Analytics, Big Data, and Machine Learning
Chaudhary, P. S., Khurana, M., & Ayalasomayajula, M. (2024) · Studies in big data · doi:10.1007/978-981-97-0448-4_12 · Cited by 32
Machine Learning Assisted State-of-the-Art-of Petrographic Classification From Geophysical Logs
Mukherjee, B., Kar, S., & Sain, K. (2024) · Pure and Applied Geophysics · doi:10.1007/s00024-024-03563-4 · Cited by 30
“It is essential to choose the appropriate algorithm based on your data characteristics and requirements (Sarker, 2021).”
Detection of cardiovascular disease cases using advanced tree-based machine learning algorithms
Asadi, F., Homayounfar, R., Mehrali, Y., Masci, C., Talebi, S., & Zayeri, F. (2024) · Scientific Reports · doi:10.1038/s41598-024-72819-9 · Cited by 37
“method often depends on the specific requirements of the task at hand, the characteristics of the data, and the desired model performance 18 .”
MethodA review on application of machine learning-based methods for power system inertia monitoring
Heidari, M., Ding, L., Kheshti, M., Bao, W., Zhao, X., Popov, M., & Terzija, V. V. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.110279 · Cited by 32
Enhanced landslide susceptibility mapping in data-scarce regions via unsupervised few-shot learning
Kong, L. S. L., Feng, W., Yi, X., Xue, Z., & Bai, L. (2024) · Gondwana Research · doi:10.1016/j.gr.2024.10.011 · Cited by 30
Satheeskumar, R. (2024) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.11.002 · Cited by 33
Alhadi, A., Beach, T., & Yacine, R. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.111515 · Cited by 35
Kumar, V., Sharma, K. V., Mangukiya, N. K., Tiwari, D. K., Ramkar, P., & Rathnayake, U. (2025) · AIMS environmental science · doi:10.3934/environsci.2025004 · Cited by 46
“In the domain of data analysis and computing, AI particularly ML has witnessed significant growth in recent years, enabling the intelligent functioning of various applications [20].”
InfluentialPatel, P., Pampaniya, S., Ghosh, A., Raj, R., Karuppaih, D., & Kandasamy, S. (2025) · IEEE Access · doi:10.1109/access.2025.3539081 · Cited by 43
“For instance, RF models can achieve high accuracy in classification tasks [34].”
InfluentialAl‐Mudhafar, W. J., Hasan, A. A., Abbas, M. A., & Wood, D. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-95490-0 · Cited by 30
Deep neural networks for speech enhancement and speech recognition: A systematic review
Natarajan, S., Al-Haddad, S. A. R., Ahmad, F. A., Kamil, R., Hassan, M. K., Azrad, S., Macleans, J. F., Abdulhussain, S. H., Mahmmod, B. M., Saparkhojayev, N. P., et al. (2025) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2025.103405 · Cited by 36
Toğaçar, M. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106579 · Cited by 30
Li, L., Alimu, A., Luan, Q., Yang, B., Gong, H., Zulipikaer, A., Xu, J., Zhong, X., Subinuer, Y., Ren, J., et al. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.881234 · Cited by 31
“TextCNN uses a k-dimensional vector to represent a word in a sentence (18).”
MethodPredicting Window View Preferences Using the Environmental Information Criteria
Kent, M. G., & Schiavon, S. (2022) · LEUKOS The Journal of the Illuminating Engineering Society of North America · doi:10.1080/15502724.2022.2077753 · Cited by 32
Korkmaz, M. E., Gupta, M. K., Yılmaz, H., Ross, N. S., Boy, M., Sivalingam, V., Chan, C. K., & Raman, J. (2023) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2023.10.192 · Cited by 30
Identifying high crash risk segments in rural roads using ensemble decision tree-based models
Iranmanesh, M., Seyedabrishami, S., & Moridpour, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-24476-z · Cited by 29
Applications of machine learning in animal and veterinary public health surveillance
Guitián, J., Snary, E., ARNOLD, M., & Chang, Y. (2023) · Scientific and Technical Review (World Organisation for Animal Health) · doi:10.20506/rst.42.3366 · Cited by 36
Wang, M., Yang, N., Gunasinghe, D. H., & Weng, N. (2023) · Computers · doi:10.3390/computers12100209 · Cited by 29
ITF-GAN: Synthetic time series dataset generation and manipulation by interpretable features
Klopries, H., & Schwung, A. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111131 · Cited by 29
Scanning probe microscopy in the age of machine learning
Laskar, M. A. R., & Celano, U. (2023) · APL Machine Learning · doi:10.1063/5.0160568 · Cited by 29
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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