Machine Learning Technique for Practical Engineering Use

Abstract

In the age of Industry 5.0, where the digital world generates massive amounts of data, AIML has emerged as a powerful tool for analyzing and interpreting this data. It has proven successful in various fields such as intelligent control, decision making, computer graphics, and computer vision and many more. The performance in AIML and deep learning methods has led to their widespread adoption in real-time engineering applications. These tools are necessarily required for creating intelligent, automated tools that can recognize the data in areas like healthcare, cybersecurity, and intelligent transportation systems. Machine learning encompasses different strategies, including reinforcement learning, semi- supervised, unsupervised and supervised learning algorithms. This study aims to comprehensively explore the utilization of ML in managing real world engineering applications, enhancing their functionality and intelligence. By investigating the applicability of various machine learning approaches in domains such as cybersecurity, healthcare, and intelligent transportation systems, this research contributes to our understanding of their effectiveness. Additionally, it addresses the research goals and difficulties associated with ML in practical life. This study serves as reference for industry professionals, academics, and decision-makers, providing insights and benchmarks for different use cases and real-world applications.