Machine Learning Strategies in Real-World Engineering Applications: A Comprehensive Survey
Keywords:
Machine Learning, Machine Learning Techniques, Deep Learning, Current Challenges, Natural Language Processing, Future Research DirectionsAbstract
In the age of Industry 5.0, we find ourselves amidst a torrent of digital data. Machine learning has emerged as a resounding success story, making substantial inroads across various sectors including computer graphics, intelligent control, computer vision, speech recognition , decision making & natural language processing. Its remarkable performance has catapulted Deep Learning & Machine Learning Techniques into the spotlight, where they are now being widely embraced and integrated into a multitude of real time (happen instantly) engineering applications. A profound understanding of machine learning has become indispensable in the development of automated and intelligent applications, especially in fields like healthcare, cyber security, and intelligent transportation systems. This survey paper aims to comprehensively explore the diverse applications of machine learning strategies in real-world engineering scenarios. The review covers a wide spectrum of engineering fields, including but not limited to robotics, manufacturing, energy systems, civil engineering, and biomedical applications. The paper discusses the challenges, opportunities, and recent advancements in deploying machine learning techniques in these domains, emphasizing the impact on performance, efficiency, and adaptability. The study also illuminates the research objectives and challenges faced by machine learning approaches when navigating the complexities of real-world.
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