The Expanding Horizon of Machine Learning: Applications, Challenges, and Future Prospects

Authors

  • Naina Handa

Keywords:

Machine learning (ML), healthcare, adoption, domains

Abstract

Machine learning (ML) has rapidly evolved to become a transformative force across various fields, including healthcare, finance, education, manufacturing, and more. The growing reliance on data-driven decision-making and automation has positioned ML as a critical component of modern technological advancements. However, despite its rapid adoption, numerous challenges hinder its full-scale implementation, such as ethical concerns, data privacy issues, and model interpretability. This paper explores the current applications of ML, the challenges faced in its implementation, and future directions that could further enhance its capabilities. A thorough literature review is conducted, highlighting key contributions in various domains, followed by an analysis of research gaps and findings. The discussion section explores emerging technologies that may shape the future of ML, while the conclusion provides insights into the direction ML should take for continued progress

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Published

27.12.2022

How to Cite

Naina Handa. (2022). The Expanding Horizon of Machine Learning: Applications, Challenges, and Future Prospects. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 304–306. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7337

Issue

Vol. 10 No. 3s (2022)

Section

Research Article

License

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