International Journal on Science and Technology

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Optimization Techniques in Machine Learning and Artificial Intelligence

Author(s) Dr. Suman Jain
Country India
Abstract This paper explores the application of optimization techniques in machine learning and artificial intelligence (AI) within the Indian context, highlighting their significance, challenges, and future research directions. Optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) play a crucial role in improving the accuracy and efficiency of machine learning models, especially in sectors like healthcare, agriculture, and energy. The paper provides a detailed review of the methodologies, including the integration of hybrid models, parallel computing, and domain-specific adaptations to enhance performance. Key findings indicate that hybrid optimization approaches offer up to 30% faster convergence compared to traditional methods, particularly in complex prediction tasks. Despite these advancements, challenges such as high computational complexity, limited access to high-performance infrastructure, and a lack of high-quality annotated data continue to hinder the widespread application of these techniques. The paper discusses the importance of bridging these gaps and emphasizes the need for scalable, transparent, and interpretable optimization models. Future research should focus on hybrid optimization strategies, the development of domain-specific benchmarks, and the integration of real-time systems for enhanced deployment in real-world applications. This study underlines the potential for AI and optimization to transform critical sectors in India, with a call for increased infrastructure investment and interdisciplinary collaboration.
Field Mathematics
Published In Volume 3, Issue 4, October-December 2012
Published On 2012-12-08
DOI https://doi.org/10.5281/zenodo.15363878
Short DOI https://doi.org/g9hrr8
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