By: Sneha Raj M. P. and Farsana Muhammed
1-M. Tech Scholar, Department of Electrical & Electronics Engineering, TKM College of Engineering, Kollam, Kerala, India
2-Assistant Professor, Department of Electrical & Electronics Engineering, TKM College of Engineering, Kollam, Kerala, India
Machine learning (ML) has become a disruptive technology with applications in many different fields and sectors. Machine learning techniques are transforming the way data is examined, patterns are found, and decisions are made in a variety of industries, including healthcare, finance, manufacturing, and transportation. ML is improving drug discovery, treatment strategies, and diagnostics in the healthcare industry. In the financial sector, this means maximizing risk mitigation, credit scoring, and fraud detection. Machine learning (ML) enables process optimization, quality control, and predictive maintenance in manufacturing. ML is useful for transportation in the areas of route planning, autonomous vehicles, and logistics. These uses highlight machine learning’s ability to increase productivity, cut expenses, and spur creativity, making it a vital component of contemporary technological developments. Machine learning (ML) is a rapidly evolving field with immense potential to revolutionize various aspects of our lives. This review explores the diverse applications of ML across various sectors like finance, health care, transportation, autonomous industries, manufacturing, data security and analyzing its impact and future potential. It also discusses ML’s role in industrial sensing and control, addressing challenges and offering practical perspectives on process monitoring, fault detection, and control optimization. The paper gives an idea about the machine learning techniques performed in various applications and can analyze that which of the techniques are suitable for each application. This comprehensive review provides a valuable resource for researchers, practitioners, and policymakers interested in understanding the transformative power of ML across diverse industries.
Keywords: Â Machine Learning (ML), Applications, Optimization, unlocking, Application Programming Interface, Support Vector Regression.
Citation:
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