American International Journal of Advanced Research and Development

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Machine Learning Strategies for Automated Performance Optimization

Author(s) Stefano Coniglio
Country Italy
Abstract The increasing complexity of digital systems has created a growing need for automated approaches capable of continuously monitoring and improving system performance. Conventional performance optimization techniques generally depend on manually defined rules, periodic evaluations, and expert intervention, which can become inefficient in dynamic computational environments. Machine learning (ML) provides an advanced alternative by enabling systems to learn from historical and real-time performance data and automatically identify optimization opportunities.
This study examines machine learning strategies for automated performance optimization and proposes an integrated framework combining predictive analytics, adaptive learning, performance monitoring, automated decision-making, and continuous model evaluation. The study adopts a conceptual and descriptive research design based on secondary academic literature related to machine learning, automated optimization, predictive analytics, intelligent systems, and performance management. The proposed framework emphasizes data collection, preprocessing, feature engineering, model development, performance prediction, optimization strategy selection, automated implementation, and feedback-based adaptation.
The analysis indicates that machine learning can improve computational efficiency, resource utilization, system responsiveness, and operational reliability when integrated with appropriate monitoring mechanisms. However, challenges involving data quality, model drift, computational overhead, interpretability, security, and optimization stability remain important. The study concludes that automated performance optimization through machine learning can provide a scalable foundation for intelligent digital systems and advanced multidisciplinary applications.
Keywords Machine Learning, Automated Optimization, Performance Optimization, Predictive Analytics, Intelligent Systems, Adaptive Learning, Resource Management, Artificial Intelligence.
Field Engineering
Published In Volume 1, Issue 1, January-February 2019
Published On 2019-02-28
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