A Review of Various Machine Learning Techniques and its Application on IoT and Cloud Computing

• Published in: Tikrit Journal of Pure Science Vol. 29; no. 1; pp. 185 - 195
• Main Authors: Talabani, Hardi Sabah, Jumaa, Ihsan Hamza
• Format: Journal Article
• Language: English
• Published: Tikrit University 25.02.2024
• Subjects: ANN; CNN; Deep Learning IoT; Machine Learning; ML Application; SVM
• DOI: 10.25130/tjps.v29i1.1618
• ISSN: 1813-1662

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.