Machine Learning-Based Adaptive Models for Real-Time Resource Optimization

Hong-Linh Truong

Machine Learning-Based Adaptive Models for Real-Time Resource Optimization

Author(s)	Hong-Linh Truong
Country	United States
Abstract	The increasing complexity of modern computational, industrial, and digital environments has created a significant demand for intelligent resource optimization approaches capable of making real-time decisions. Traditional resource management techniques often rely on predefined rules and static optimization strategies, which may become ineffective when dealing with rapidly changing conditions, dynamic workloads, and uncertain operational environments. Machine Learning (ML)-based adaptive models have emerged as advanced approaches that enable systems to learn from historical and real-time data, adjust operational strategies, and optimize resource utilization through intelligent decision-making mechanisms. This study examines machine learning-based adaptive models designed for real-time resource optimization by analysing their ability to improve efficiency, adaptability, and decision accuracy in dynamic environments. The research explores how supervised learning, adaptive algorithms, predictive analytics, and intelligent optimization techniques contribute to effective resource management. A simulation-based analytical framework is developed to compare traditional resource allocation approaches with machine learning-driven adaptive models using evaluation indicators including resource utilization efficiency, response time, optimization accuracy, and system adaptability. The findings indicate that machine learning-based adaptive models provide significant improvements in real-time resource optimization by continuously learning from changing conditions and dynamically adjusting resource allocation strategies. The results demonstrate that adaptive ML frameworks enhance operational efficiency, reduce resource wastage, and support intelligent decision-making in complex environments. The study further highlights the importance of high-quality data management, scalable architectures, and continuous model improvement for successful implementation of adaptive optimization systems.
Keywords	Machine Learning, Adaptive Models, Real-Time Optimization, Resource Management, Intelligent Systems, Predictive Analytics, Dynamic Decision-Making.
Field	Engineering
Published In	Volume 6, Issue 1, January-February 2024
Published On	2024-02-25

View / Download PDF File

About AIJRI Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@aijri.com

American International Journal of Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Machine Learning-Based Adaptive Models for Real-Time Resource Optimization

Share this