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
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00815-1 · Cited by 3,137
“We take into account such categories based on the nature and learning capabilities of different DL techniques and how they are used to solve problems in real-world applications [97].”
BackgroundMethodResultInfluentialSelf-citationAli, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
“Various machine learning [81] and deep learning [80] techniques can be used to build an analytical AI model to solve a”
BackgroundMethodInfluentialSelf-citationArtificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
Vora, L. K., Gholap, A. D., Jetha, K. L., Singh, T. R. R., Solanki, H. K., & Chavda, V. P. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15071916 · Cited by 1,193
“In pharmaceutical product development, various AI models have been explored to enhance different aspects of the process.”
BackgroundTaye, M. M. (2023) · Computers · doi:10.3390/computers12050091 · Cited by 1,275
“Interactions between these levels guarantee task domain regularities [18,47].”
BackgroundMethodInfluentialArtificial Intelligence in Education: AIEd for Personalised Learning Pathways
Tapalova, O., & Zhiyenbayeva, N. (2022) · The Electronic Journal of e-Learning · doi:10.34190/ejel.20.5.2597 · Cited by 887
“The outcomes of different learning algorithms in one subset may differ greatly because of the initial characteristics and input data (Sarker, 2021).”
BackgroundTaye, M. M. (2023) · Computation · doi:10.3390/computation11030052 · Cited by 812
Javed, A. R., Shahzad, F., Rehman, S. U., Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022) · Cities · doi:10.1016/j.cities.2022.103794 · Cited by 645
Recent advances in decision trees: an updated survey
Costa, V. G., & Pedreira, C. E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10275-5 · Cited by 648
“Underlying these results is not only an abundance of available data, but also important advances in the learning models themselves (Sarker 2021).”
BackgroundA modified Adam algorithm for deep neural network optimization
Reyad, M., Sarhan, A. M., & Arafa, M. M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08568-z · Cited by 663
“Many researchers [6, 7] present several optimization algorithms for deep neural networks in the literature.”
BackgroundSarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570
“In the following, we discuss a wide range of methods such as regression and classification analysis, association rule analysis, time-series analysis, behavioral analysis, log analysis, and so on within the scope of our study.”
InfluentialSelf-citationAI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions
Sarker, I. H., Furhad, M. H., & Nowrozy, R. (2021) · SN Computer Science · doi:10.1007/s42979-021-00557-0 · Cited by 663
“Thus, collecting various types of real-world data such as structured, semi-structured, unstructured, or meta-data [137] . relevant to a particular problem domain with legal access, which may vary from application to application, is challenging.”
BackgroundSelf-citationEnhancing mental health with Artificial Intelligence: Current trends and future prospects
Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F. T., & Eberhardt, J. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100099 · Cited by 667
“Fueled by vast datasets and machine learning capabilities, AI algorithms can analyze an individual ’ s unique characteristics and needs [83,84].”
BackgroundZhao, Z., Alzubaidi, L., Zhang, J., Duan, Y., & Gu, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122807 · Cited by 486
From concept drift to model degradation: An overview on performance-aware drift detectors
Bayram, F., Ahmed, B. S., & Kassler, A. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108632 · Cited by 419
Sarker, I. H., Khan, A. I., Abushark, Y. B., & Alsolami, F. J. (2022) · Mobile Networks and Applications · doi:10.1007/s11036-022-01937-3 · Cited by 445
Self-citationMarques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., da Silva, I. C. B., Magalhães, P., Schmidt, S., & Vale, N. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16030332 · Cited by 444
“Unlike theory-driven methods, these approaches prioritize empirical relationships and statistical methods, enabling pharmacometricians to capture complex interactions between drug concentrations and patient-specific factors for more accurate parameter estimates [147].”
BackgroundBenti, N. E., Chaka, M. D., & Semie, A. G. (2023) · Sustainability · doi:10.3390/su15097087 · Cited by 470
“The algorithms can be categorized into three primary groups: supervised learning, unsupervised learning, and reinforcement learning [45].”
BackgroundSmart Transportation: An Overview of Technologies and Applications
Oladimeji, D., Gupta, K., Kose, N. A., Gundogan, K., Ge, L., & Liang, F. (2023) · Sensors · doi:10.3390/s23083880 · Cited by 386
“To effectively employ the learning algorithms in the related application area, it is necessary to alter or improve current pre-processing procedures or to propose new data preparation approaches [80].”
BackgroundMethodApplication of artificial intelligence in geotechnical engineering: A state-of-the-art review
Baghbani, A., Choudhury, T., Costa, S., & Reiner, J. (2022) · Earth-Science Reviews · doi:10.1016/j.earscirev.2022.103991 · Cited by 401
Machine learning-based approach: global trends, research directions, and regulatory standpoints
Pugliese, R., Regondi, S., & Marini, R. (2021) · Data Science and Management · doi:10.1016/j.dsm.2021.12.002 · Cited by 425
A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352
“now is highly unjust and excessive [8].”
Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions
Olawade, D. B., Wada, O. Z., Ige, A. O., Egbewole, B. I., Olojo, A. S., & Oladapo, B. I. (2024) · Hygiene and Environmental Health Advances · doi:10.1016/j.heha.2024.100114 · Cited by 418
A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions
Mahadevkar, S. V., Khemani, B., Patil, S., Kotecha, K. V., Vora, D. R., Abraham, A., & Gabralla, L. A. (2022) · IEEE Access · doi:10.1109/access.2022.3209825 · Cited by 304
“It seeks to uncover exciting connections or interactions between the dataset’s variables [58].”
BackgroundInfluentialHan, Z., Yang, Y., Rushlow, J., Huo, J., Liu, Z., Hsu, Y., Yin, R., Wang, M., Liang, R., Wang, K., et al. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00432a · Cited by 309
Déniz-García, A., Fabelo, H. A., Rodríguez-Almeida, A. J., Zamorano, G. B. Z., Castro-Fernández, M., Alberiche-Ruano, M. P., Solvoll, T. G., Granja, C., Schopf, T. R., Callicó, G. M., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/44030 · Cited by 321
“These preprocessed data are then used as input for AI predictive models [73], such as linear and nonlinear machine learning approaches (linear discriminant analysis, logistic regression, naïve Bayes, K-nearest neighbors, support vector machines, random forest, AdaBoost, and XGBoost) or deep…”
Applications of artificial intelligence in business management, e-commerce and finance
Pallathadka, H., Ramirez-Asís, E., Loli-Poma, T. P., Kaliyaperumal, K., Ventayen, R. J. M., & Naved, M. (2021) · Materials Today: Proceedings · doi:10.1016/j.matpr.2021.06.419 · Cited by 240
Sarker, I. H. (2022) · Annals of Data Science · doi:10.1007/s40745-022-00444-2 · Cited by 313
“In general, AI [10] combines human behavior and intelligence into machines or systems, whereas ML [3] is a method of learning from data or experience that automates the creation of analytical models in a particular application domain, e.g., cybersecurity according to our focused area.”
BackgroundMethodInfluentialSelf-citationRecent Progresses in Machine Learning Assisted Raman Spectroscopy
Qi, Y., Hu, D., Jiang, Y., Wu, Z., Zheng, M., Chen, E. X., Liang, Y., Sadi, M. A., Zhang, K., & Chen, Y. P. (2023) · Advanced Optical Materials · doi:10.1002/adom.202203104 · Cited by 269
Artificial Intelligence in Predicting Mechanical Properties of Composite Materials
Kibrete, F., Trzepieciński, T., Gebremedhen, H. S., & Woldemichael, D. E. (2023) · Journal of Composites Science · doi:10.3390/jcs7090364 · Cited by 277
Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023) · Energies · doi:10.3390/en16104025 · Cited by 237
Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions
Bhaiyya, M. L., Panigrahi, D., Rewatkar, P., & Haick, H. (2024) · ACS Sensors · doi:10.1021/acssensors.4c01582 · Cited by 251
Healthcare predictive analytics using machine learning and deep learning techniques: a survey
Badawy, M., Ramadan, N., & Hefny, H. A. (2023) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-023-00108-y · Cited by 287
“K-nearest neighbor (KNN) is an "instance-based learning" or non-generalized learning algorithm, which is often known as a “lazy learning” algorithm [38].”
MethodScope of machine learning in materials research—A review
Mobarak, M. H., Mimona, M. A., Islam, M. A., Hossain, N., Zohura, F. T., Imtiaz, I., & Rimon, M. I. H. (2023) · Applied Surface Science Advances · doi:10.1016/j.apsadv.2023.100523 · Cited by 253
Rahman, A., Debnath, T., Kundu, D., Khan, M. S. I., Aishi, A. A., Sazzad, S., Sayduzzaman, M., & Band, S. S. (2024) · AIMS Public Health · doi:10.3934/publichealth.2024004 · Cited by 260
Padhiary, M., Saha, D., Kumar, R., Sethi, L. N., & Kumar, A. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100483 · Cited by 257
Applications of machine learning in thermochemical conversion of biomass-A review
Khan, M., Naqvi, S. R., Ullah, Z., Taqvi, S. A. A., Khan, M. N. A., Farooq, W., Mehran, M. T., Juchelková, D., & Štěpanec, L. (2022) · Fuel · doi:10.1016/j.fuel.2022.126055 · Cited by 223
Current AI technologies in cancer diagnostics and treatment
Tiwari, A., Mishra, S. N., & Kuo, T. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02369-9 · Cited by 257
A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM
Miah, M. S. U., Kabir, M. M., Sarwar, T. B., Safran, M., Alfarhood, S., & Mridha, M. F. (2024) · Scientific Reports · doi:10.1038/s41598-024-60210-7 · Cited by 210
Fundamentals of the recycling of spent lithium-ion batteries
Li, P., Luo, S., Lin, Y., Xiao, J., Xia, X., Liu, X., Wang, L., & He, X. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00362d · Cited by 205
Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions
Mandalapu, V., Elluri, L., Vyas, P., & Roy, N. (2023) · IEEE Access · doi:10.1109/access.2023.3286344 · Cited by 201
Ansari, A. N., Ahmad, S., & Bhutta, S. M. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12223-4 · Cited by 198
“There are various types of AI including analytical, functional, interactive, textual, and visual that provide diverse applications (Sarker, 2021a, b, c).”
Generative AI in Higher Education: The ChatGPT Effect
Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203
Sarker, I. H., Janicke, H., Mohsin, A., Gill, A. Q., & Maglaras, L. A. (2024) · ICT Express · doi:10.1016/j.icte.2024.05.007 · Cited by 227
Self-citationFountzilas, E., Pearce, T. E., Baysal, M. A., Chakraborty, A., & Tsimberidou, A. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01471-y · Cited by 233
Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods
Mazhar, T., Asif, R. N., Malik, M. A., Nadeem, M. A., Haq, I., Iqbal, M. W., Kamran, M., & Ashraf, S. (2023) · Sustainability · doi:10.3390/su15032603 · Cited by 190
Yaghoubi, E., Yaghoubi, E., Khamees, A. A., Razmi, D., & Lü, T. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108789 · Cited by 188
Digitalization for sustainable buildings: Technologies, applications, potential, and challenges
Asif, M., Naeem, G., & Khalid, M. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.141814 · Cited by 217
Machine learning in precision diabetes care and cardiovascular risk prediction
Oikonomou, E. K., & Khera, R. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01985-3 · Cited by 190
“The learning process can be supervised or unsupervised [22].”
BackgroundAdvances in artificial intelligence for drug delivery and development: A comprehensive review
Gholap, A. D., Uddin, M. J., Faiyazuddin, M., Omri, A., Gowri, S., & Khalid, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108702 · Cited by 197
Smart City Data Science: Towards data-driven smart cities with open research issues
Sarker, I. H. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100528 · Cited by 204
Self-citationMachine Learning in Nutrition Research
Kirk, D., Kok, E. J., Tufano, M., Teki̇nerdoğan, B., Feskens, E. J. M., & Camps, G. (2022) · Advances in Nutrition · doi:10.1093/advances/nmac103 · Cited by 206
“Since the labels of the data are required, human intervention plays a larger role than in other ML types, which can increase costs and time (51).”
BackgroundUnleashing the Power of Artificial Intelligence in Materials Design
Badini, S., Regondi, S., & Pugliese, R. (2023) · Materials · doi:10.3390/ma16175927 · Cited by 193
Towards sustainable agriculture: Harnessing AI for global food security
Pandey, D. K., & Mishra, R. (2024) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2024.04.003 · Cited by 205
Samala, A. D., Rawas, S., Wang, T., Reed, J. M., Kim, J., Howard, N.-J., & Ertz, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12936-0 · Cited by 229
“Deep learning, a specific ML category, leverages artificial neural networks loosely modeled after the human brain’s neural networks (Sarker, 2021a).”
Clinical Applications of Artificial Intelligence—An Updated Overview
Busnatu, Ș., Niculescu, A.-G., Bolocan, A., Petrescu, G. E. D., Păduraru, D. N., Năstasă, I., Lupușoru, M. O. D., Geantă, M., Andronic, O., Grumezescu, A. M., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11082265 · Cited by 194
Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization
Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023) · Information and Organization · doi:10.1016/j.infoandorg.2023.100478 · Cited by 173
“The output of an algorithm is a function of the quality of its input (Sarker, 2021).”
Yang, M., Lim, M. K., Qu, Y., Ni, D., & Xiao, Z. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108859 · Cited by 194
“By analyzing the articles in this study, we find that 11 out of 40 commonly used ML algorithms are applied to SCRM (Sarker, 2021).”
Lung and colon cancer classification using medical imaging: a feature engineering approach
Chehade, A. H., Abdallah, N., Marion, J.-M., Oueidat, M., & Chauvet, P. E. (2022) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-022-01139-x · Cited by 171
“Extreme Gradient Boosting (XGBoost) is a type of gradient boosting that minimizes the loss by computing the second order gradients of the loss function, which improves model generalization and performance, and reduces overfitting [26].”
BackgroundMethodInfluentialA review of deep learning applications in human genomics using next-generation sequencing data
Alharbi, W., & Rashid, M. (2022) · Human Genomics · doi:10.1186/s40246-022-00396-x · Cited by 179
“Although the first concept of the DL theory originated in the 1980s was based on the perceptron model and neuron concept [20], within the last decade, DL algorithms have become a state-of-the-art predictive technology for big data [21–23].”
MethodHasan, M. K., Alam, M. A., Roy, S., Dutta, A., Jawad, M. T., & Das, S. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100799 · Cited by 159
Sahbeni, G., Ngabire, M., Musyimi, P. K., & Székely, B. (2023) · Remote Sensing · doi:10.3390/rs15102540 · Cited by 156
“As the field of salinization mapping continues to progress, it has become increasingly evident that integrating remote sensing data with machine learning offers a more robust framework for effectively processing large datasets and generating more accurate products [35].”
BackgroundTraffic management approaches using machine learning and deep learning techniques: A survey
Almukhalfi, H. M., Noor, A., & Noor, T. H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108147 · Cited by 179
AI in precision agriculture: A review of technologies for sustainable farming practices
Adewusi, A. O., Asuzu, O. F., Olorunsogo, T., Olorunsogo, T., Adaga, E. M., & Daraojimba, D. O. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.1.0314 · Cited by 184
“By examining historical data, machine learning algorithms can uncover patterns and trends that may not be apparent through traditional methods (Sarker, 2021).”
BackgroundSrivastava, S., Wang, W., Zhou, W. M., Jin, M., & Vikesland, P. J. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c06737 · Cited by 165
Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review
Nath, D., Ankit, Neog, D. R., & Gautam, S. S. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10063-0 · Cited by 167
“Some of the real-world issues are increasingly being addressed by ML and DL [3] in a variety of fields including science [4, 5], engineering, healthcare [6, 7], finance and economics [8, 9], agriculture [10], and computer vision and natural language processing [11].”
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Electronics · doi:10.3390/electronics12102287 · Cited by 176
“…[8,9] User behavior analytics and context-aware smartphone applications [8,9] Healthcare services [10–12] Traffic prediction and transportation [8,13] Internet of things (IoT) and smart cities [13] Cybersecurity and threat intelligence [14] Natural language processing and sentiment analysis…”
BackgroundInfluentialDeep Learning in Image-Based Plant Phenotyping
Murphy, K. M., Ludwig, E., Gutierrez, J., & Gehan, M. A. (2024) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-070523-042828 · Cited by 173
A Review of Computational Modeling in Wastewater Treatment Processes
Duarte, M. S., Martins, G., Oliveira, P., Fernandes, B., Ferreira, E. C., Alves, M. M., Lopes, F., Pereira, M. A., & Nováis, P. (2023) · ACS ES&T Water · doi:10.1021/acsestwater.3c00117 · Cited by 168
“45 Regarding classification, the program can make fewer complex decisions, such as indicating a positive or negative response.”
BackgroundHe, F., Yuan, L., Mu, H., Ros, M., Ding, D., Pan, Z. S., & Li, H. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102525 · Cited by 149
Machine Learning and Smart Devices for Diabetes Management: Systematic Review
Makroum, M. A., Adda, M., Bouzouane, A., & Ibrahim, H. (2022) · Sensors · doi:10.3390/s22051843 · Cited by 145
Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images
Kim, S., Rim, B., Choi, S., Lee, A., Min, S. D., & Hong, M. (2022) · Diagnostics · doi:10.3390/diagnostics12040915 · Cited by 144
Yaghoubi, E., Yaghoubi, E., Khamees, A. A., & Vakili, A. H. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09893-7 · Cited by 166
“Clustering groups similar data points based on specific features or similarities [196, 212, 213].”
SMOTE-DRNN: A Deep Learning Algorithm for Botnet Detection in the Internet-of-Things Networks
Popoola, S. I., Adebisi, B., Ande, R., Hammoudeh, M. A. A., Anoh, K., & Atayero, A. A. A. (2021) · Sensors · doi:10.3390/s21092985 · Cited by 148
Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
Sánchez, J. A. H., Casilimas, K., & Rendón, O. M. C. (2022) · Sensors · doi:10.3390/s22083031 · Cited by 165
Design thinking and artificial intelligence: A systematic literature review exploring synergies
Sreenivasan, A., & Suresh, M. (2024) · International Journal of Innovation Studies · doi:10.1016/j.ijis.2024.05.001 · Cited by 202
Mansour, M., Darweesh, M. S., & Soltan, A. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.01.021 · Cited by 165
Σιγάλα, Μ., Ooi, K., Tan, G. W., Aw, E. C., Buhalis, D., Cham, T., Chen, M.-M., Dwivedi, Y. K., Ulrike Gretzel, B., Inversini, A., et al. (2024) · Journal of Hospitality and Tourism Management · doi:10.1016/j.jhtm.2024.08.004 · Cited by 165
Machine learning in cybersecurity: A review of threat detection and defense mechanisms
Okoli, U. I., Obi, O. C., Adewusi, A. O., & Abrahams, T. O. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.1.0315 · Cited by 137
“…learning, reinforcement learning, deep learning, neural networks, decision trees, random forest, support vector machines (SVM) and K-Nearest neighbours (KNN), which can be appropriately applied based on their used cases, either as classification, clustering, regression or otherwise [57].”
MethodStudent Performance Prediction Using Machine Learning Algorithms
Ahmed, E. (2024) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2024/4067721 · Cited by 135
“Cluster analysis, also known as clustering, is an unsupervised machine learning technique for identifying and grouping related data points in large datasets without concern for the specifc outcome [22].”
Islam, M. N., Mustafina, S. N., Mahmud, T., & Khan, N. I. (2022) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-022-04594-2 · Cited by 154
“Classification algorithms predict the class label of new data points (test data) depending on how the model is supervised by training data [21].”
BackgroundGupta, R., Kumari, S., Senapati, A., Ambasta, R. K., & Kumar, P. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102013 · Cited by 134
Lubongo, C., Daej, M. A. A. B., & Alexandridis, P. (2024) · Recycling · doi:10.3390/recycling9040059 · Cited by 150
Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction
Santos, D., Saias, J., Quaresma, P., & Nogueira, V. (2021) · Computers · doi:10.3390/computers10120157 · Cited by 153
“As an illustration of such algorithms, consider for instance logistic regression, random forest, support vector machines, decision trees, naive Bayes, etc. [27].”
Sarker, I. H. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100393 · Cited by 129
Self-citationNoman, A. A., Akter, U. H., Pranto, T. H., & Haque, A. B. (2022) · Annals of Emerging Technologies in Computing · doi:10.33166/aetic.2022.02.002 · Cited by 129
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.2022.08.017 · Cited by 140
Afsaneh, E., Sharifdini, A., Ghazzaghi, H., & Ghobadi, M. Z. (2022) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-022-00969-9 · Cited by 149
“The algorithms create a model that relies on sample data, called training data, in order to predict or make decisions [24, 25].”
MethodSystematic review of research on artificial intelligence in K–12 education (2017–2022)
Martin, F., Zhuang, M., & Schaefer, D. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100195 · Cited by 132
“Some common algorithms are K-means clustering and Mean-shift clustering (Sarker, 2021).”
InfluentialModeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques
Guptta, S. D., Shahriar, K. T., Alqahtani, H., Alsalman, D., & Sarker, I. H. (2022) · Annals of Data Science · doi:10.1007/s40745-022-00379-8 · Cited by 128
“Five popular ML techniques are used to do classification work such as Random Forest, Decision tree, Support Vector Machine, Logistic Regression, XG Boost [33].”
Self-citationDetecting Anomalies in Financial Data Using Machine Learning Algorithms
Bakumenko, A., & Elragal, A. A. (2022) · Systems · doi:10.3390/systems10050130 · Cited by 127
“The popularity of supervised and unsupervised machine learning techniques almost equally and linearly grows with time, while the performance of machine learning solutions depends both on the qualitative and quantitative data characteristics and a learning algo-rithm [15].”
BackgroundArtificial Intelligence in Operating Room Management
Bellini, V., Russo, M., Domenichetti, T., Panizzi, M., Allai, S., & Bignami, E. G. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02038-2 · Cited by 142
“[32 ] Another key finding in the previous review was the potential cost savings associated with accurate surgical case duration predictions in robotic surgery.”
BackgroundNazir, A., He, J., Zhu, N., Wajahat, A., Ma, X., Ullah, F., Qureshi, S., & Pathan, M. S. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101820 · Cited by 134
“ML and DL techniques are increasingly being used for IoT botnet attack detection (Fu et al., 2015; Gao et al., 2019; Sarker, 2021; Zheng and Yang, 2019; Hussain et al., 2020).”
A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research
Zhang, X. J., Guo, F., Chen, T., Pan, L., Beliakov, G., & Wu, J. (2023) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer18040110 · Cited by 148
Nutakki, M., & Mandava, S. (2023) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2022.105721 · Cited by 104
Synthetic biology and artificial intelligence in crop improvement
Zhang, D., Xu, F., Wang, F., Le, L., & Pu, L. (2024) · Plant Communications · doi:10.1016/j.xplc.2024.101220 · Cited by 129
“…AI techniques encompass various methods such as machine learning, which trains algorithms on data to identify patterns and make decisions; natural language processing for understanding and generating human language; and computer vision for analyzing visual information, among others (Sarker, 2021).”
Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
Abu Al-Haija, Q., Krichen, M., & Abu Elhaija, W. (2022) · Electronics · doi:10.3390/electronics11040556 · Cited by 120
Gonuguntla, S., Kamesh, R., Pal, U., & Chatterjee, D. (2023) · Journal of Photochemistry and Photobiology C Photochemistry Reviews · doi:10.1016/j.jphotochemrev.2023.100621 · Cited by 120
Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview
Karl, F., Pielok, T., Moosbauer, J., Pfisterer, F., Coors, S., Binder, M., Schneider, L., Thomas, J., Richter, J., Lang, M., et al. (2023) · ACM Transactions on Evolutionary Learning and Optimization · doi:10.1145/3610536 · Cited by 160
“With the immense popularity of Machine Learning (ML) and data-driven solutions for many domains [Sarker 2021 ], the demand for automating the creation of suitable ML pipelines has strongly increased [Hutter et al. 2018 ].”
BackgroundMethodResultInfluentialArtificial Intelligence in Drug Discovery and Development
Mak, K., Wong, Y.-H., & Pichika, M. R. (2024) · Springer eBooks · doi:10.1007/978-3-031-35529-5_92 · Cited by 131
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
