The digitally-enhanced environment is susceptible to massive data, such as information security data, internet technology data, cellular internet, patient records, media data, corporate data, and so on, in the current era of Industry 4.0. Understanding of Machine Learning (ML) is essential for intelligently evaluating these sets of data and developing related "intelligent" and "automated" solutions. Different forms of ML algorithms e.g. reinforcement learning, semi-supervised, unsupervised and supervised learning exist in this segment. In addition, deep learning, which is a wider segment of ML techniques, can smartly evaluate datasets on a massive scale. In this research, a comprehensive analysis of ML techniques and classification analysis algorithms that are applicable to develop capabilities and intelligence of applications are analyzed. Therefore, this research’s contribution is illustrating the key principles of various ML techniques and their application in different real-life application realms e.g. e-commerce, healthcare, agriculture, smart cities, cyber-security systems etc. Lastly, this paper presents a discussion of the challenges and future research based on this research.
Keywords
Machine Learning (ML), Internet of Things (IoT), Classification Analysis, Digital Technology.
J. Daily and B. Gardiner, "Cybersecurity Considerations for Heavy Vehicle Event Data Recorders", SAE International Journal of Transportation Cybersecurity and Privacy, vol. 1, no. 2, pp. 113-143, 2018. Doi: 10.4271/11-01-02-0006.
D. Nikitin, "Industry 4.0", Scientific Development Trends And Education, 2019. Doi: 10.18411/lj-12-2019-143.
S. Choudhary and N. Kesswani, "Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT", Procedia Computer Science, vol. 167, pp. 1561-1573, 2020. Doi: 10.1016/j.procs.2020.03.367.
A. K, “Energy Efficient Network Selection Using 802.16g Based Gsm Technology,” Journal of Computer Science, vol. 10, no. 5, pp. 745–754, May 2014.
D. Toher, G. Downey and T. Murphy, "Semi-supervised linear discriminant analysis", Journal of Chemometrics, vol. 25, no. 12, pp. 621-630, 2011. Doi: 10.1002/cem.1408.
J. Stoltzfus, "Logistic Regression: A Brief Primer", Academic Emergency Medicine, vol. 18, no. 10, pp. 1099-1104, 2011. Doi: 10.1111/j.1553-2712.2011.01185.x.
M. HUANG, "Algorithm for finding k-nearest neighbors based on octree segmentation in space", Journal of Computer Applications, vol. 28, no. 8, pp. 2046-2048, 2008. Doi: 10.3724/sp.j.1087.2008.02046.
J. Li and J. Castagno, "Support Vector Machine (SVM) pattern recognition to AVO classification", Geophysical Research Letters, vol. 31, no. 2, 2004. Doi: 10.1029/2003gl018299.
I. Sarker, Y. Abushark, F. Alsolami and A. Khan, "IntruDTree: A Machine Learning Based Cyber Security Intrusion Detection Model", Symmetry, vol. 12, no. 5, p. 754, 2020. Doi: 10.3390/sym12050754.
Y. Chen, "Fractal Modeling and Fractal Dimension Description of Urban Morphology", Entropy, vol. 22, no. 9, p. 961, 2020. Doi: 10.3390/e22090961.
Acknowledgements
Author(s) thanks to Dr.Yuan Xue for this research completion and support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
J Xin Ge
J Xin Ge
School of Chemistry and Chemical Engineering, Nanjing University, Jiangsu, China, 210093.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
J Xin Ge and Yuan Xue, “Machine Learning Technique and Applications – An Classification Analysis”, Journal of Machine and Computing, vol.1, no.4, pp. 185-190, October 2021. doi: 10.53759/7669/jmc202101022.
