A Review of Various Machine Learning Techniques and its Application on IoT and Cloud Computing
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Abstract
The employ of Internet of Things has become an integral part of our daily life, especially in developed countries and societies, which in turn are considered to be one of the basic areas on which the mechanisms of their work depend to a large extent the applications of algorithms used in the field of machine learning are available due to the high accuracy that characterizes these applications and the margin of safety that these techniques offer. As a result, scientific research for these applications is increasing every day and leads to different results for these applications in different areas of the Internet of Things and its multiple uses. This study presents an analysis of machine learning techniques and the need for ML and its types. The article focuses on current research on the integration of IoT with cloud computing technology and the benefits of linking cloud computing techniques with IoT systems. An overview of different machine learning algorithms like SVM and neural network algorithms like ANN are discussed. Deep learning algorithms; CNN, RNN, and ensemble learning techniques are reviewed in terms of developed models, goals, applications, and the results achieved. The study offers a comparative analysis of the application of different ML and deep learning algorithms.
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References
[1] Rana, A. K., Salau, A. O., Gupta, S., & Arora, S. (2018). a survey of machine learning methods for IoT and their future applications. Amity Journal of Computational Sciences, 2(2), 1-5. [2] Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020). A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Communications Surveys & Tutorials, 22(3), 1646-1685. [3] Shanthamallu, U. S., Spanias, A., Tepedelenlioglu, C., & Stanley, M. (2017, August). A brief survey of machine learning methods and their sensor and IoT applications. In 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-8). IEEE. [4] Jader, R., & Aminifar, S. (2022). Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach. Applied Computational Intelligence and Soft Computing, 2022. [5] Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for Internet of Things data analysis: A survey. Digital Communications and Networks, 4(3), 161-175. [6] Aery, M., & Ram, C. (2017). A Review on Machine Learning: Trends and Future Prospects. An International Journal of Engineering Sciences, 25. [7] Jader, R., & Aminifar, S. (2022). Fast and Accurate Artificial Neural Network Model for Diabetes Recogni-tion. NeuroQuantology, 20(10), 2187-2196. [8] Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019). A review of machine learning and IoT in smart transportation. Future Internet, 11(4), 94. [9] Dorri, A., Kanhere, S. S., Jurdak, R., & Gauravaram, P. (2017, March). Blockchain for IoT security and privacy: The case study of a smart home. In 2017 IEEE international conference on pervasive computing and communications workshops (PerCom workshops) (pp. 618-623). IEEE. [10] Kumar, N. M., & Mallick, P. K. (2018). Blockchain technology for security issues and challenges in IoT. Procedia computer science, 132, 1815-1823. [11] Jader, R. F., Abd, M. H. M., & Jumaa, I. H. (2022). Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach. Journal of Studies in Science and Engineering, 2(4), 37-49. [12] Rui, J., & Danpeng, S. (2015, June). Architecture design of the Internet of Things based on cloud computing. In 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation (pp. 206-209). IEEE. [13] Aazam, M., Khan, I., Alsaffar, A. A., & Huh, E. N. (2014, January). Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved. In Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST) Islamabad, Pakistan, 14th-18th January, 2014 (pp. 414-419). IEEE. [14] Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2018). Secure integration of IoT and cloud computing. Future Generation Computer Systems, 78, 964-975. [15] Fortino, G., Guerrieri, A., Russo, W., & Savaglio, C. (2014, May). Integration of agent-based and cloud computing for the smart objects-oriented IoT. In Proceedings of the 2014 IEEE 18th international conference on computer supported cooperative work in design (CSCWD) (pp. 493-498). IEEE. [16] Saha, S., & Soumitra, S. (2019). Secured Integration of IoT and Cloud Computing (Doctoral dissertation).
[17] Abdulhadi, H. M. T., & Talabani, H. S. (2021). Comparative study of supervised machine learning algorithms on thoracic surgery patients based on ranker feature algorithms. UHD Journal of Science and Technology, 5(2), 66-74. [18] Mašetić, Z., Kečo, D., Doǧru, N., & Hajdarević, K. (2017). SYN flood attack detection in cloud computing using support vector machine. TEM Journal, 6(4), 752. [19] Liu, C. C., Chang, Y., Tseng, C. W., Yang, Y. T., Lai, M. S., & Chou, L. D. (2018, July). SVM-based classification mechanism and its application in SDN networks. In 2018 10th International Conference on Communication Software and Networks (ICCSN) (pp. 45-49). IEEE. [20] Kennedy, J. J., Selvam, R. P., & Palanisamy, V. Cloud-Centric IoT based Decision Support System for Gestational Diabetes Mellitus using Optimal Support Vector Machine. International Journal of Recent Technology and Engineering (IJRTE) ISSN, 2277-3878. [21] Kumar, A., Chattree, G., & Periyasamy, S. (2018). Smart healthcare monitoring system. Wireless Personal Communications, 101, 453-463. [22] Aliev, K. (2017). Internet of things applications and artificial neural networks in smart agriculture. Politecnico di Torino: Turin, Italy. [23] Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P. L., Iorkyase, E., Tachtatzis, C., & Atkinson, R. (2016, May). Threat analysis of IoT networks using artificial neural network intrusion detection system. In 2016 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE. [24] Shahid, N., Rappon, T., & Berta, W. (2019). Applications of artificial neural networks in health care organizational decision-making: A scoping review. PloS one, 14(2), e0212356. [25] Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA transactions on Signal and Information Processing, 3, e2. [26] Liu, T., Fang, S., Zhao, Y., Wang, P., & Zhang, J. (2015). Implementation of training convolutional neural networks. arXiv preprint arXiv:1506.01195. [27] Sun, W., Zheng, B., & Qian, W. (2016, March). Computer aided lung cancer diagnosis with deep learning algorithms. In Medical imaging 2016: computer-aided diagnosis (Vol. 9785, pp. 241-248). SPIE. [28] Acharya, U. R., Fujita, H., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adam, M. (2017). Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Information Sciences, 415, 190-198. [29] Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., ... & Ng, A. Y. (2015). An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716. [30] Javed, A., Larijani, H., Ahmadinia, A., & Gibson, D. (2016). Smart random neural network controller for HVAC using cloud computing technology. IEEE Transactions on Industrial Informatics, 13(1), 351-360. [31] HaddadPajouh, H., Dehghantanha, A., Khayami, R., & Choo, K. K. R. (2018). A deep recurrent neural network based approach for internet of things malware threat hunting. Future Generation Computer Systems, 85, 88-96. [32] Moon, J., Kum, S., & Lee, S. (2019). A heterogeneous IoT data analysis framework with collaboration of edge-cloud computing: Focusing on indoor PM10 and PM2. 5 status prediction. Sensors, 19(14), 3038. [33] Singh, A., & Batra, S. (2018). Ensemble based spam detection in social IoT using probabilistic data structures. Future Generation Computer Systems, 81, 359-371. [34] Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020). HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments. Future Generation Computer Systems, 104, 187-200. [35] Ani, R., Krishna, S., Anju, N., Aslam, M. S., & Deepa, O. S. (2017, September). Iot based patient monitoring and diagnostic prediction tool using ensemble classifier. In 2017 International conference on advances in computing, communications and informatics (ICACCI) (pp. 1588-1593). IEEE.