AI-Driven Sorting Algorithms for Big Data: Techniques and Real- World Applications

This research paper examines AI-driven sorting algorithms tailored for big data, exploring the integration of traditional and advanced methods to enhance data processing efficiency. The focus is on the performance of QuickSort, MergeSort, and AI-augmented algorithms such as deep neural networks (DNNs) and reinforcement learning (RL) models across various data types and sizes. The proposed methodology emphasizes the use of diverse real-world and synthetic datasets to assess execution time, memory usage, and computational efficiency. The findings demonstrate that AI techniques offer significant advantages in processing large-scale data, particularly in dynamic and multimodal scenarios.