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Survey on Applications of Machine Learning

Praveen Halingali, Santosh Kumar, Sanket Desai, Punith S Alagoudar

Abstract


Machine learning (ML) has transformed research methodologies across technical disciplines by enabling data-driven decision-making and predictive analytics. This paper explores ML’s core principles, issues and future developments, emphasizing its impact on optimizing research processes in fields such as engineering, materials science, and telecommunications. Key ML paradigms, including supervised, unsupervised, and reinforcement learning, are analyzed in the context of technical applications. Additionally, this study addresses challenges such as data quality and model interpretability while discussing advancements in explainable AI and federated learning. The findings underscore ML’s role in accelerating innovation and enhancing research precision.

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References


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