ADVANCING ARTIFICIAL INTELLIGENCE AND DATA SCIENCE: A COMPREHENSIVE FRAMEWORK FOR COMPUTATIONAL EFFICIENCY AND SCALABILITY

Abstract

The exponential growth of artificial intelligence (AI) and data science applications has created unprecedented demands for computational efficiency and performance optimization. While high-level programming languages dominate the data science landscape, the C programming language remains fundamental for developing high-performance AI algorithms and data processing systems. This paper presents a comprehensive framework that leverages C programming to enhance AI and data science applications through optimized memory management, parallel processing, and algorithm implementation. Our research addresses the critical need for performance-driven solutions in machine learning, neural networks, and large-scale data analytics. The problem statement centers on the performance bottlenecks encountered in AI and data science applications when using interpreted languages. Traditional approaches often sacrifice computational efficiency for development convenience, leading to suboptimal performance in production environments. This research proposes an integrated framework that combines C programming with modern AI and data science methodologies to achieve superior performance while maintaining code maintainability and scalability.