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Machine Learning: Algorithms, Real-World Applications and Research Directions
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Abstract
In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study's key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
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“The regularization techniques such as Ridge, Lasso, Elastic-Net, etc. [85, 105] can be used to optimize the linear regression model.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
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“Besides, support vector regression, decision tree regression, random forest regression techniques [85, 105] can be used for building effective regression models depending on the problem type, e.g., non-linear tasks.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
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“Advanced analytics of these data using machine learning techniques [105] can facilitate the efficient management of urban areas including real-time management, e.g., traffic flow management, evidence-based planning decisions which pertain to the longer-term strategic role of forecasting for urban planning, e.g., crime prevention, public safety, and security, or framing the future, e.g.,”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
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“Advanced analytics of rural data including machine learning [105] modeling can facilitate providing new opportunities for them to build insights and capacity to meet current needs and prepare for their futures.”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
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“These deep learning methods can perform well, particularly, when learning from large-scale datasets [105, 140].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
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“Such advanced analytics and decision-making based on machine learning techniques [105], a major part of artificial intelligence (AI) [102] can also play a significant role in the Fourth Industrial Revolution (Industry 4.0) due to its learning capability for smart computing as well as automation [121].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
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“The data can be structured, semi-structured, or unstructured, which increases day by day [105].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [105]
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · cited 1×
“…based on CNN can be used in the field, such as AlexNet [60], Xception [24], In-Preprints ception [118], Visual Geometry Group (VGG) [44], ResNet [45], etc. – LSTM-RNN: Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the area of deep learning…”
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
(1967) · Cited by 23,719
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · cited 1×
“One-R [47], decision trees [87, 88], DTNB [110], Ripple Down Rule learner (RIDOR) [125], Repeated Incremental Pruning to Produce Error Reduction (RIPPER) [126] exist with the ability of rule generation.”
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · cited 1×
“A number of advanced deep learning models 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…”
(1996) · Cited by 20,179
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · cited 1×
“To build a series of decision trees with controlled variation, it combines bootstrap aggregation (bagging) [18] and random feature selection [11].”
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · cited 1×
“AlphaZero, AlphaGo [113] are examples of the model-based approaches.”
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907 · cited 3×
“Association rule learning is a rule-based machine learning approach to discover interesting relationships, “IF-THEN” statements, in large datasets between variables [7].”
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 12,864
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971
(2016) · Cited by 11,679
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,362
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,537
Analysis of a complex of statistical variables into principal components.
Hotelling, H. (1933) · Journal of Educational Psychology · doi:10.1037/h0071325 · Cited by 9,691
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414
Experiments with a new boosting algorithm
Freund, Y., & Schapire, R. E. (1996) · Cited by 8,189
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037 · cited 3×
“The problem to be solved in reinforcement learning (RL) is defined as a Markov Decision Process (MDP) [86], i.”
InfluentialMining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Internet of Things for Smart Cities
Zanella, A., Bui, N., Castellani, A. P., Vangelista, L., & Zorzi, M. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2306328 · Cited by 6,385
Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
A detailed analysis of the KDD CUP 99 data set
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396 · cited 3×
“These are, for example, cybersecurity datasets such as NSL-KDD [119], UNSW-NB15 [76], ISCX’12 [1], CIC-DDoS2019 [2], Bot-IoT [59], etc.”
Instance-Based Learning Algorithms
Aha, D. W., Kibler, D. F., & Albert, M. K. (1991) · Machine Learning · doi:10.1023/a:1022689900470 · Cited by 5,290
Understanding and Using Context
Dey, A. K. (2001) · Personal and Ubiquitous Computing · doi:10.1007/s007790170019 · Cited by 5,035
Data Mining - Concepts and Techniques.
Perner, P. (2002) · Künstliche Intell. · Cited by 4,883
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033 · cited 2×
“These are, for example, cybersecurity datasets such as NSL-KDD [119], UNSW-NB15 [76], ISCX’12 [1], CIC-DDoS2019 [2], Bot-IoT [59], etc.”
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
Finding Groups in Data: An Introduction to Cluster Analysis.
Sarle, W. S., Kaufman, L., & Rousseeuw, P. J. (1991) · Journal of the American Statistical Association · doi:10.2307/2290430 · Cited by 3,653
The estimation of the gradient of a density function, with applications in pattern recognition
Fukunaga, K., & Hostetler, L. D. (1975) · IEEE Transactions on Information Theory · doi:10.1109/tit.1975.1055330 · Cited by 3,031
Estimating Continuous Distributions in Bayesian ClassifiersPreprint
John, G. H., & Pat Langley, B. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.4964 · Cited by 2,914 · cited 1×
“– Naive Bayes (NB): The naive Bayes algorithm is based on the Bayes’ theorem with the assumption of independence between each pair of features [51].”
A Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852 · cited 1×
“Restricted Boltzmann machines (RBM) [46] can be used for dimensionality reduction, classification, regression, collaborative filter-ing, feature learning, and topic modeling.”
Reality mining: sensing complex social systems
Eagle, N., & (Sandy) Pentland, A. (2006) · Personal and Ubiquitous Computing · doi:10.1007/s00779-005-0046-3 · Cited by 2,772
A massively parallel architecture for a self-organizing neural pattern recognition machine
Carpenter, G. A., & Grossberg, S. (1987) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(87)80014-2 · Cited by 2,525
Constrained K-means Clustering with Background Knowledge
Wagstaff, K. L., Cardie, C., Rogers, S., & Schrödl, S. (2001) · Cited by 2,469
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276314 · Cited by 2,412 · cited 1×
“STING [122], CLIQUE [6], etc. are the standard algorithms of grid-based clustering.”
A simple and fast algorithm for K-medoids clustering
Park, H.-S., & Jun, C. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.039 · Cited by 2,186
Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.041 · Cited by 1,983
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Baxter, L. A., & Puterman, M. L. (1995) · Technometrics · doi:10.2307/1269932 · Cited by 1,889
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
Holte, R. C. (1993) · Machine Learning · doi:10.1023/a:1022631118932 · Cited by 1,800
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Keerthi, S. S., Shevade, S., Bhattacharyya, C., & Murthy, K. R. K. (2001) · Neural Computation · doi:10.1162/089976601300014493 · Cited by 1,791
Scalable algorithms for association mining
Zaki, M. J. (2000) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.846291 · Cited by 1,769
Ridge Estimators in Logistic Regression
le Cessie, S., & van Houwelingen, H. C. (1992) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.2307/2347628 · Cited by 1,689 · cited 1×
“cation issues in machine learning is Logistic Regression (LR) [64].”
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2018.05.009 · Cited by 1,440 · cited 1×
“…technologies, etc., where knowledge transfer encourages farmers to enhance their decisions to adopt sustainable agriculture practices utilizing the increasing amount of data captured by emerging technologies, e.g., the Internet of Things (IoT), mobile technologies and devices, etc. [53] [54] [5].”
A survey on opinion mining and sentiment analysis: Tasks, approaches and applications
Ravi, K., & Ravi, V. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.06.015 · Cited by 1,376
Shape Quantization and Recognition with Randomized Trees
Amit, Y., & Geman, D. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1545 · Cited by 1,304
STING: A Statistical Information Grid Approach to Spatial Data Mining
Wang, W. W., Yang, J., & Muntz, R. R. (1997) · Cited by 1,263
Machine Learning and Deep Learning Methods for Cybersecurity
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018) · IEEE Access · doi:10.1109/access.2018.2836950 · Cited by 1,228
Pattern recognition and machine learning
(1993) · Choice Reviews Online · doi:10.5860/choice.30-3308 · Cited by 1,046
State-of-the-Art Speech Recognition with Sequence-to-Sequence Models
Chiu, C.-C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, E., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462105 · Cited by 1,043
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2019.05.022 · Cited by 1,015
Machine Learning for Internet of Things Data Analysis: A Survey
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P. M., & Sheth, A. (2017) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 1,008
The Infinite Gaussian Mixture Model
Rasmussen, C. E. (1999) · Cited by 985
The Application of Computers to Taxonomy
Sneath, P. H. A. (1957) · Microbiology · doi:10.1099/00221287-17-1-201 · Cited by 938 · cited 1×
“Single linkage [115], Complete linkage [116], BOTS [102] etc.”
Machine learning for internet of things data analysis: a survey
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.10.002 · Cited by 921
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets
Oh, Y., Park, S. J., & Ye, J. C. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2993291 · Cited by 912
Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110059 · Cited by 845
Sharma, R., Kamble, S., Gunasekaran, A., Kumar, P. V., & Kumar, A., V (2020) · Computers & Operations Research · doi:10.1016/j.cor.2020.104926 · Cited by 837 · cited 2×
“Sustainable agriculture practices help to improve agricultural productivity while also reducing negative impacts on the environment [5, 25, 109].”
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766 · cited 6×
“Usually, the availability of data is considered as the key to construct a machine learning model or data-driven realworld systems [103, 105].”
InfluentialSelf-citationClassification and Regression Trees
Gehrke, J. E. (2005) · IGI Global eBooks · doi:10.4018/978-1-59140-557-3.ch027 · Cited by 733
Feature Extraction Construction and Selection: A Data Mining Perspective
MTW, Liu, H., & Motoda, H. (1999) · Journal of the American Statistical Association · doi:10.2307/2669967 · Cited by 719 · cited 2×
“Thus, “feature selection” [66] [99] is considered as one of”
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J. E., Gunopulos, D., & Raghavan, P. (1998) · ACM SIGMOD Record · doi:10.1145/276305.276314 · Cited by 663
AI-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 · cited 1×
“Artificial intelligence (AI), particularly, machine learning (ML) have grown rapidly in recent years in the context of data analysis and computing that typically allows the applications to function in an intelligent manner [95].”
Self-citationSurvey of Machine Learning Algorithms for Disease Diagnostic
Fatima, M., & Pasha, M. (2017) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2017.91001 · Cited by 638
Sensor Technologies for Intelligent Transportation Systems
Guerrero-Ibáñez, J., Zeadally, S., & Contreras-Castillo, J. (2018) · Sensors · doi:10.3390/s18041212 · Cited by 637
Weka: Practical machine learning tools and techniques with Java implementations
Witten, I. H., Frank, E., Trigg, L. E., Hall, M. A., Holmes, G., & Cunningham, S. J. (1999) · Research Commons (University of Waikato) · Cited by 632
Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17971-2 · Cited by 587
Feature Extraction, Construction and Selection
(1998) · Springer eBooks · doi:10.1007/978-1-4615-5725-8 · Cited by 576
Survey of Model-Based Reinforcement Learning: Applications on Robotics
Polydoros, A., & Nalpantidis, L. (2017) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0468-y · Cited by 560
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Jamshidi, M., Lalbakhsh, A., Talla, J., Peroutka, Z., Hadjilooei, F., Lalbakhsh, P., Jamshidi, M., Spada, L. L., Mirmozafari, M., Dehghani, M., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3001973 · Cited by 551
AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions
Sarker, I. H. (2021) · doi:10.20944/preprints202101.0457.v1 · Cited by 550
Self-citationDeep learning-based image recognition for autonomous driving
Fujiyoshi, H., Hirakawa, T., & Yamashita, T. (2019) · IATSS Research · doi:10.1016/j.iatssr.2019.11.008 · Cited by 439
COVID-19 Outbreak Prediction with Machine Learning
Ardabili, S., Mosavi, A. H., Ghamisi, P., Filip, F., Várkonyi-Kóczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020) · Algorithms · doi:10.3390/a13100249 · Cited by 427
Pattern Recognition and Machine Learning, 2006
Bishop, C., & Park, W. (2007) · Cited by 415
Environmental variability supports chimpanzee behavioural diversity
Kalan, A. K., Kulik, L., Arandjelovic, M., Boesch, C., Haas, F., Dieguez, P., Barratt, C. D., Abwe, E. E., Agbor, A., Angedakin, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18176-3 · Cited by 398
Mohammed, M., Khan, M. B., & Bashier, E. B. M. (2016) · doi:10.1201/9781315371658 · Cited by 389
A machine learning-based framework to identify type 2 diabetes through electronic health records
Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., Yang, G., & Chen, Y. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.09.014 · Cited by 380
Ślusarczyk, B. (2018) · Polish Journal of Management Studies · doi:10.17512/pjms.2018.17.1.19 · Cited by 368 · cited 1×
“0” [114] is typically the ongoing automation of conventional manufacturing and industrial practices, including exploratory data processing, using new smart technologies such as machine learning au-Preprints Fig.”
Comparison of deep learning approaches to predict COVID-19 infection
Alakuş, T. B., & Türkoğlu, İ. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110120 · Cited by 357
IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model
Sarker, I. H., Abushark, Y. B., Alsolami, F., & Khan, A. I. (2020) · Symmetry · doi:10.3390/sym12050754 · Cited by 356
Self-citationMachine Learning-based traffic prediction models for Intelligent Transportation Systems
Boukerche, A., & Wang, J. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107530 · Cited by 351 · cited 1×
“Accurate traffic prediction based on machine and deep learning modeling can help to minimize the issues [17, 30, 31].”
Deep Learning applications for COVID-19
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350
Mohamadou, Y., Halidou, A., & Kapen, P. T. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01770-9 · Cited by 341
Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00535-6 · Cited by 324 · cited 7×
“In addition to these most common deep learning methods discussed above, several other deep learning approaches [96] exist in the area for various purposes.”
InfluentialSelf-citationManufacturing process data analysis pipelines: a requirements analysis and survey
Ismail, A., Truong, H., & Kästner, W. (2019) · Journal Of Big Data · doi:10.1186/s40537-018-0162-3 · Cited by 305
Deep Cybersecurity: A Comprehensive Overview from Neural Network and Deep Learning Perspective
Sarker, I. H. (2021) · doi:10.20944/preprints202102.0340.v1 · Cited by 295
Self-citationSarker, I. H., Kayes, A. S. M., & Watters, P. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0219-y · Cited by 287 · cited 6×
“pending on the data characteristics [106].”
InfluentialSelf-citationLópez, G., Quesada, L., & Guerrero, L. A. (2018) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-60366-7_23 · Cited by 277 · cited 1×
“Speech recognition [23] is also very popular that typically uses sound and linguistic models, e.g., Google Assistant, Cor-tana, Siri, Alexa, etc. [67], where machine learning methods are used.”
Toward multi-label sentiment analysis: a transfer learning based approach
Tao, J., & Fang, X. (2020) · Journal Of Big Data · doi:10.1186/s40537-019-0278-0 · Cited by 265
Set-oriented mining for association rules in relational databases
Houtsma, M., & Swami, A. (2002) · Proceedings of the Eleventh International Conference on Data Engineering · doi:10.1109/icde.1995.380413 · Cited by 263 · cited 5×
“Another approach SETM [49] exhibits good performance and stable behavior with execution time; however, it suffers from the same flaw as the AIS algorithm.”
InfluentialProceedings of the Fourth International Conference on Knowledge Discovery and Data Mining
Agrawal, R., & Stolorz, P. (1998) · Knowledge Discovery and Data Mining · Cited by 252
Manufacturing Analytics and Industrial Internet of Things
Lade, P., Ghosh, R., & Srinivasan, S. (2017) · IEEE Intelligent Systems · doi:10.1109/mis.2017.49 · Cited by 246
A survey of Clustering Algorithms
Rokach, L. (2009) · Data Mining and Knowledge Discovery Handbook · doi:10.1007/978-0-387-09823-4_14 · Cited by 245
Parking availability prediction for sensor-enabled car parks in smart cities
Zheng, Y., Rajasegarar, S., & Leckie, C. A. (2015) · IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) · doi:10.1109/issnip.2015.7106902 · Cited by 234
Data Science: A Comprehensive OverviewPreprint
Cao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03606 · Cited by 216
A deep-learning model for urban traffic flow prediction with traffic events mined from twitter
Essien, A. E., Petrounias, I., Sampaio, P., & Sampaio, S. (2020) · World Wide Web · doi:10.1007/s11280-020-00800-3 · Cited by 209 · cited 1×
“Accurate Accurate traffic prediction based on machine and deep learning modeling can help to minimize the issues [17] [31] [30].”
Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions
Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01650-z · Cited by 207
Self-citationMcCallum, A. (2005) · Queue · doi:10.1145/1105664.1105679 · Cited by 205
Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement
Balducci, F., Impedovo, D., & Pirlo, G. (2018) · Machines · doi:10.3390/machines6030038 · Cited by 205
Finding Association Rules That Trade Support Optimally against Confidence
Scheffer, T. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44794-6_35 · Cited by 202
An analytical method for diseases prediction using machine learning techniques
Nilashi, M., Ibrahim, O. B., Ahmadi, H., & Shahmoradi, L. (2017) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2017.06.011 · Cited by 190
Significant Applications of Machine Learning for COVID-19 Pandemic
Kushwaha, S., Bahl, S., Bagha, A. K., Parmar, K. S., Javaid, M., Haleem, A., & Singh, R. P. (2020) · Journal of Industrial Integration and Management · doi:10.1142/s2424862220500268 · Cited by 179 · cited 5×
“Thus, it’s important to understand the principles of various machine learning algorithms and their applicability to apply in various real-world application areas, such as IoT systems, cybersecurity services, business and recommendation systems, smart cities, healthcare and COVID-19, context-aware systems, sustainable…”
Influential(2003) · Cited by 165
Mining Mobile User Preferences for Personalized Context-Aware Recommendation
Zhu, H., Chen, E., Xiong, H., Yu, K., Cao, H., & Tian, J. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2532515 · Cited by 157 · cited 2×
“, smartphone datasets such as phone call logs [84, 101], SMS Log [29], mobile application usages logs [137] [117], mobile phone notification logs [73] etc.”
Machine learning based decision support systems (DSS) for heart disease diagnosis: a review
Safdar, S., Zafar, S. N., Zafar, N., & Khan, N. F. (2017) · Artificial Intelligence Review · doi:10.1007/s10462-017-9552-8 · Cited by 156
MobileMiner: mining your frequent patterns on your phone
Srinivasan, V., Moghaddam, S., Mukherji, A., Rachuri, K. K., Xu, C.-R., & Tapia, E. M. (2014) · Ubiquitous Computing · doi:10.1145/2632048.2632052 · Cited by 150 · cited 1×
“…CIC-DDoS2019 [2], Bot-IoT [59], etc., smartphone datasets such as phone call logs [84] [101], SMS Log [29], mobile application usages logs [137] [117], mobile phone notification logs [73] etc., IoT data [57] [62] [16], agri-culture and e-commerce data [138] [120], health data such as heart…”
An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network
Wei, P., Li, Y., Zhang, Z., Hu, T., Li, Z., & Liu, D. (2019) · IEEE Access · doi:10.1109/access.2019.2925828 · Cited by 144
BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model
Sarker, I. H., Colman, A., Han, J., Khan, A. I., Abushark, Y. B., & Salah, K. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-019-01443-z · Cited by 142 · cited 2×
“Moreover, recently proposed BehavDT [100], and IntrudTree [97] by Sarker et al. are effective in the relevant application domains, such as user behavior analytics and cybersecurity analytics, respectively.”
Self-citationMehrotra, A., Hendley, R., & Musolesi, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971747 · Cited by 138 · cited 1×
“…smartphone datasets such as phone call logs [84] [101], SMS Log [29], mobile application usages logs [137] [117], mobile phone notification logs [73] etc., IoT data [57] [62] [16], agri-culture and e-commerce data [138] [120], health data such as heart disease [92], diabetes mellitus [134]…”
Confirmation-Guided Discovery of First-Order Rules with Tertius
Flach, P. A., & Lachiche, N. (2001) · Machine Learning · doi:10.1023/a:1007656703224 · Cited by 131
A stochastic multi-criteria decision analysis for sustainable biomass crop selection
Cobuloglu, H. I., & Büyüktahtakın, İ. E. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.04.006 · Cited by 130
Association Rule Mining:A Survey
Cai, J. (2003) · Cited by 122
A machine learning based robust prediction model for real-life mobile phone data
Sarker, I. H. (2019) · Internet of Things · doi:10.1016/j.iot.2019.01.007 · Cited by 113
Self-citationChallenges and research opportunities in eCommerce search and recommendations
Tsagkias, M., King, T. H., Kallumadi, S. T., Murdock, V., & de Rijke, M. (2020) · ACM SIGIR Forum · doi:10.1145/3451964.3451966 · Cited by 108 · cited 1×
“culture and e-commerce data [138] [120], health data such as heart disease [92], diabetes mellitus [134] [83], COVID-19 [43] [74], etc.”
Context-aware rule learning from smartphone data: survey, challenges and future directions
Sarker, I. H. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0258-4 · Cited by 105 · cited 1×
“…etc. and in the distribution phase - the inventory management, consumer analysis, etc. – User Behavior Analytics and Context-Aware Smart-phone Applications: Context-awareness is a system’s ability to capture knowledge about its surroundings at any moment and modify behaviors accordingly [28] [93].”
Self-citationAutomated Product Recommendations with Preference-Based Explanations
Marchand, A., & Marx, P. (2020) · Journal of Retailing · doi:10.1016/j.jretai.2020.01.001 · Cited by 100
Perveen, S., Shahbaz, M., Keshavjee, K., & Guergachi, A. (2018) · IEEE Access · doi:10.1109/access.2018.2884249 · Cited by 87
Das, A., Ng, W. K., & Woon, Y.-K. (2001) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/502585.502665 · Cited by 85
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior
Sarker, I. H., Colman, A., Kabir, M. A., & Han, J. (2018) · The Computer Journal · doi:10.1093/comjnl/bxx082 · Cited by 80 · cited 3×
“Single linkage [115], Complete linkage [116], BOTS [102] etc. are some examples of such techniques.”
InfluentialSelf-citationAdnan, N., Nordin, S. M., Rahman, I., & Noor, A. (2018) · World Journal of Science, Technology and Sustainable Development · doi:10.1108/wjstsd-11-2016-0062 · Cited by 78 · cited 1×
“Sustainable agriculture practices help to improve agricultural productivity while also reducing negative impacts on the environment [5, 25, 109].”
Sarker, I. H., & Kayes, A. S. M. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102762 · Cited by 75 · cited 3×
“For example, context-aware association rules can be used to build an intelligent phone call application [104].”
InfluentialSelf-citationImproving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning
Essien, A. E., Petrounias, I., Sampaio, P., & Sampaio, S. (2019) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2019.8679231 · Cited by 66 · cited 1×
“Accurate traffic prediction based on machine and deep learning modeling can help to minimize the issues [17, 30, 31].”
Finding association rules that trade support optimally against confidence
Scheffer, T. (2005) · Intelligent Data Analysis · doi:10.3233/ida-2005-9405 · Cited by 56
Behavior-based adaptive call predictor
Phithakkitnukoon, S., Dantu, R., Claxton, R., & Eagle, N. (2011) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/2019583.2019588 · Cited by 54 · cited 1×
“datasets such as phone call logs [84] [101], SMS Log [29], mobile application usages logs [137] [117], mobile phone notification logs [73] etc.”
ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques
Sarker, I. H., Abushark, Y. B., & Khan, A. I. (2020) · Symmetry · doi:10.3390/sym12040499 · Cited by 53 · cited 2×
“reduces the dimensionality of the datasets, and to build an effective machine learning model [98].”
Self-citationAppsPred: Predicting context-aware smartphone apps using random forest learning
Sarker, I. H., & Salah, K. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100106 · Cited by 52 · cited 1×
“Thus, the developers of mobile apps can rely on machine learning to create smart apps that can understand human behavior, support, and entertain users [107, 137, 140].”
Self-citationExploiting enriched contextual information for mobile app classification
Zhu, H., Cao, H., Chen, E., Xiong, H., & Tian, J. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398484 · Cited by 48
A Mobile Intelligent Interruption Management System
Zulkernain, S., Madiraju, P., Ahamed, S. I., & Stamm, K. (2010) · e-Publications@Marquette (Marquette University) · doi:10.3217/jucs-016-15-2060 · Cited by 45
Decision tree induction with a constrained number of leaf nodes
Wu, C., Chen, Y., Liu, Y., & Yang, X. (2016) · Applied Intelligence · doi:10.1007/s10489-016-0785-z · Cited by 44
Sarker, I. H., Alqahtani, H., Alsolami, F. J., Khan, A. I., Abushark, Y. B., & Siddiqui, M. K. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00328-3 · Cited by 43 · cited 3×
“method to understand a feature’s relation to the response variable and can be used for feature selection [99].”
Self-citationAn Empirical Comparison of Supervised Machine Learning Algorithms for Internet of Things Data
Khadse, V. M., Mahalle, P. N., & Biraris, S. V. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697476 · Cited by 41 · cited 1×
“…such as phone call logs [84] [101], SMS Log [29], mobile application usages logs [137] [117], mobile phone notification logs [73] etc., IoT data [57] [62] [16], agri-culture and e-commerce data [138] [120], health data such as heart disease [92], diabetes mellitus [134] [83], COVID-19 [43]…”
Constrained clustering with Minkowski Weighted K-Means
de Amorim, R. C. (2012) · doi:10.1109/cinti.2012.6496753 · Cited by 37 · cited 1×
“The typical algorithms of this kind of clustering are COP K-means [121], CMWK-Means [27], etc.”
Hybrid Cost-Sensitive Decision Tree
Sheng, S., & Ling, C. X. (2005) · Lecture notes in computer science · doi:10.1007/11564126_29 · Cited by 35
Phone call log as a context source to modeling individual user behavior
Sarker, I. H., Colman, A., Kabir, M. A., & Han, J. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2971592 · Cited by 33 · cited 1×
“, smartphone datasets such as phone call logs [84, 101], SMS Log [29], mobile application usages logs [137] [117], mobile phone notification logs [73] etc.”
Self-citationUML Fever: Diagnosis and Recovery
Bell, A. E. (2005) · Queue · doi:10.1145/1053331.1053347 · Cited by 30
Sentiment analysis of agricultural product ecommerce review data based on deep learning
Zi-kang, H., Yang, Y., Guofeng, Y., & Xinyu, Z. (2020) · International Conference on Internet of Things and Intelligent Applications (ITIA) · doi:10.1109/itia50152.2020.9312251 · Cited by 21
A Context Aware Interruption Management System for Mobile Devices
Zulkernain, S., Madiraju, P., & Ahamed, S. I. (2010) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-17758-3_16 · Cited by 15
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].”
BackgroundMethodInfluential
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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