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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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MethodAdaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor
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“An examination of existing literature reveals that ML algorithms can be categorized into supervised, unsupervised, semi-supervised, and reinforcement learning categories (Sarker, 2021).”
BackgroundFazil, A.-U.-D., Gomes, P. I. A., & Sandamal, K. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.124389 · Cited by 38
Zhou, S., Yang, D., Zhang, Z., Zhang, J., Qu, F., Punetha, P., Li, W., & Li, N. (2024) · Measurement · doi:10.1016/j.measurement.2024.115603 · Cited by 39
Hegde, S., Nanayakkara, S., Jordan, A., Jeha, O., Patel, U., Luu, V., & Gao, J. (2024) · European Journal Of Dental Education · doi:10.1111/eje.13042 · Cited by 44
“extensive use of electronic health records and digital imaging has facilitated the availability of huge data sets that has propelled the success of AI applications in healthcare [4, 5].”
Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research
Chen, G., & Tang, D. (2024) · Nanomaterials · doi:10.3390/nano14211688 · Cited by 40
“Instead of following predetermined instructions, ML systems learn from data to recognize patterns, make decisions, and improve their performance over time [4–10].”
BackgroundInfluentialAzizi, A., Zhang, Z., Hua, W., Li, M., Igathinathane, C., Yang, L., Ampatzidis, Y. G., Ghasemi‐Varnamkhasti, M., Radi, R., Zhang, M., et al. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100690 · Cited by 37
Fatorachian, H., Kazemi, H., & Pawar, K. S. (2025) · Smart Cities · doi:10.3390/smartcities8020056 · Cited by 49
“Machine learning models, including regression algorithms, decision trees, and deep learning-based forecasting, are widely used to predict seasonal variations in logistics demand, optimize delivery schedules, and improve resource allocation [20].”
A Comprehensive Survey on Machine and Deep Learning for Optical Communications
Amirabadi, M. A., Nezamalhosseini, S. A., Kahaei, M. H., & Chen, L. R. (2025) · IEEE Access · doi:10.1109/access.2025.3569559 · Cited by 54
Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models
Ferdaus, M. M., Abdelguerfi, M., Ioup, E., Niles, K. N., Pathak, K., & Sloan, S. D. (2025) · ACM Computing Surveys · doi:10.1145/3777382 · Cited by 106
Flood Prediction Using Machine Learning Models: A Case Study of Kebbi State Nigeria
Lawal, Z. K., Yassin, H., & Zakari, R. Y. (2021) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde53843.2021.9718497 · Cited by 37
Military Applications of Machine Learning: A Bibliometric Perspective
Galán-Hernández, J. J., Carrasco, R. A., & LaTorre, A. (2022) · Mathematics · doi:10.3390/math10091397 · Cited by 50
“The format of these data can be structured (typically tables of databases that are manipulated with SQL), semi-structured (with structure but tables, including NoSQL databases) or unstructured (without a defined format as natural language, images, video, etc.) [37,39].”
BackgroundMethodInfluentialHarnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences
Wilson, B. S., Tucci, D. L., Moses, D. A., Chang, E. F., Young, N. M., Zeng, F., Lesica, N. A., Bur, A. M., Kavookjian, H. L., Mussatto, C. C., et al. (2022) · Journal of the Association for Research in Otolaryngology · doi:10.1007/s10162-022-00846-2 · Cited by 37
“That latter information can be found in excellent overviews (e.g., LeCun et al. 2015; Bur et al. 2019; Hogarty et al. 2019; Sarker 2021) and in online courses such as the outstanding (and comprehensive) ones offered by Duke (https:// www. cours era. org/ learn/ machi ne- learn ing- duke? utm_ campa…”
BackgroundFrom distributed machine to distributed deep learning: a comprehensive survey
Dehghani, M., & Yazdanparast, Z. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00829-x · Cited by 45
“A machine learning solution is influenced by the characteristics of the data and the performance of the learning algorithms [10].”
BackgroundInfluentialBiswas, J., Jobaer, M. A., Haque, S. F., Shozib, M. S. I., & Limon, Z. A. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21245 · Cited by 44
“Every machine learning al-gorithm ’ s efficiency and performance depend on certain conditions [50].”
BackgroundHanif, F., Kanae, S., Farooq, R., Iqbal, M. R., & Petroselli, A. (2023) · Remote Sensing · doi:10.3390/rs15225338 · Cited by 36
Zamanian, H., Shalbaf, A., Zali, M. R., Khalaj, A. R., Dehghan, P., Tabesh, M. R., Hatami, B., Alizadehsani, R., Tan, R., & Acharya, U. R. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107932 · Cited by 37
Review of applications of artificial intelligence (AI) methods in crop research
Bose, S., Banerjee, S., Kumar, S., Saha, A., Nandy, D., & Hazra, S. (2024) · Journal of Applied Genetics · doi:10.1007/s13353-023-00826-z · Cited by 38
“…satellites (Zhang et al. 2020), remote sensing technology and other high-throughput phenotyping platforms can be analysed using ML techniques (Sarker 2021), allowing rapid and non-destructive assessment of various traits, such as plant height, leaf area, disease symptoms and stress tolerance.”
A scalable blockchain-enabled federated learning architecture for edge computing
Ren, S., Kim, E., & Lee, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308991 · Cited by 48
Yalçın, N., & Alisawi, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38913 · Cited by 36
Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies
Edaugal, J. P., Zhang, D., Liu, D., Glezakou, V., & Sun, N. (2025) · Chem & Bio Engineering · doi:10.1021/cbe.4c00170 · Cited by 39
Mari, T., Henderson, J., Maden, M., Nevitt, S. J., Duarte, R. V., & Fallon, N. B. (2021) · Journal of Pain · doi:10.1016/j.jpain.2021.07.011 · Cited by 38
Salamzadeh, A., Ebrahimi, P., Soleimani, M., & Fekete‐Farkas, M. (2022) · Journal of risk and financial management · doi:10.3390/jrfm15100424 · Cited by 36
Ahamad, M. M., Aktar, S., Uddin, M. J., Rashed-Al-Mahfuz, M., Azad, A., Uddin, S., Alyami, S. A., Sarker, I. H., Khan, A., Lió, P., et al. (2022) · Healthcare · doi:10.3390/healthcare11010031 · Cited by 49
Self-citationAhmed, S. N., & Prakasam, P. (2023) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2023.07.001 · Cited by 36
Review: Biotic and abiotic approaches to artificial humic acids production
Wang, M., Li, Y., Peng, H., Wang, J., Li, Q., Li, P., Fan, J., Liu, S., & Zheng, G. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113771 · Cited by 35
Ali, I. E., Tanikawa, C., Chikai, M., Ino, S., Sumita, Y. I., & Wakabayashi, N. (2023) · Journal of Prosthodontic Research · doi:10.2186/jpr.jpr_d_23_00073 · Cited by 35
Toward QoS Monitoring in IoT Edge Devices Driven Healthcare—A Systematic Literature Review
Younas, M. I., Iqbal, M. J., Aziz, A., & Sodhro, A. H. (2023) · Sensors · doi:10.3390/s23218885 · Cited by 40
A toolbox of machine learning software to support microbiome analysis
Marcos-Zambrano, L. J., López-Molina, V. M., Bakır-Güngör, B., Frohme, M., Karađuzović-Hadžiabdić, K., Klammsteiner, T., Ibrahimi, E., Lahti, L., Lončar-Turukalo, T., Dhamo, X., et al. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1250806 · Cited by 43
“These algorithms can be divided into different categories, including supervised, unsupervised, semi-supervised and reinforcement learning (Sarker, 2021), whereof supervised and unsupervised methods are the most applied in human microbiome studies (Ghannam and Techtmann, 2021; Goodswen et al., 2021;…”
MethodCurrent State of Application of Machine Learning for Investigation of MgO-C Refractories: A Review
Sado, S., Jastrzębska, I., Zelik, W., & Szczerba, J. (2023) · Materials · doi:10.3390/ma16237396 · Cited by 35
“According to Sarker [52], the algorithms with the highest popularity index worldwide are assigned to the group of reinforcement learning, but their popularity decreased in 2020.”
BackgroundMethodInfluentialAlqahtani, A. F., & Ilyas, M. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6010009 · Cited by 47
Graph convolution networks for social media trolls detection use deep feature extraction
Asif, M., Al‐Razgan, M., Ali, Y. A., & Yun-rong, L. (2024) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-024-00600-4 · Cited by 35
“Currently, three primary methods are employed for this purpose [19, 20].”
MethodDevelopment of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques
Jaiyeoba, O., Ogbuju, E., Yomi, O. T., & Oladipo, F. O. (2024) · Journal of Computing Theories and Applications · doi:10.62411/jcta.10488 · Cited by 35
“The study [20] used ML and multi-model ensemble techniques to classify and predict ESD diseases.”
MethodSipai, U. A., Jadeja, R. B., Kothari, N. H., Trivedi, T., Mahadeva, R., & Patole, S. P. (2024) · IEEE Access · doi:10.1109/access.2024.3426039 · Cited by 35
“Its unique quality also resides in the random feature splitting value selection, which helps to generate diverse and uncorrelated DTs in the ensemble [32].”
BackgroundMethodInfluentialZhang, X. J., Hu, X., Zhang, T., Yang, L., Liu, C., Xu, N., Wang, H., & Sun, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae404 · Cited by 38
“Another booster for protein solubility predictor is the emergency of machine learning (ML), which shifted the focus toward the utilization of feature engineering and supervised ML algorithms, such as linear regression, support vector machines, and gradient boosting machines [18].”
Background
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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