Machine learning is an effective technique for optimizing real-time electronics product data analysis. It can efficiently handle large electronics product datasets, reducing processing time and resource requirements for generating insights. This study assesses the current status of methods and applications for optimizing real-time data analysis by examining existing research in machine learning-based recommendation systems for electronic products. The indicated subjects encompass using machine learning algorithms to discern characteristics and correlations from large datasets, applying machine learning for prognostic analytics and projection, and utilizing machine learning to identify anomalies. The paper provides examples of machine learning-based evaluation optimization solutions that focus on utilizing unorganized data and delivering real-time dashboards. Presented here is a discussion on the complex challenges and potential benefits associated with utilizing machine learning to optimize real-time data processing. Machine learning may efficiently expedite real-time data assessment while delivering precise and timely outcomes
Keywords
Machine Learning, Processing Time, Optimization, Prognostic Analysis, Data Assessment, Electronics Product.
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This work was supported by Dongseo University, "Dongseo Frontier Project" Research Fund of 2023.
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Young Jun Park
Young Jun Park
Department of Game, Dong-Seo University, Sasang-gu, Busan, South Korea.
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Cite this article
Young Jun Park, “Enhanced analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning”, Journal of Machine and Computing, pp. 059-070, January 2024. doi: 10.53759/7669/jmc202404007.
