HYBRID AI FRAMEWORKS FOR BIG DATA PROCESSING AND PATTERN RECOGNITION

Abstract

The exponential growth of big data has created significant challenges in extracting actionable insights using traditional artificial intelligence (AI) models. Hybrid AI frameworks, which integrate multiple AI paradigms—such as machine learning, deep learning, and symbolic reasoning—offer a robust solution for big data processing and complex pattern recognition tasks. This paper explores the architecture, applications, and advantages of hybrid AI models. It discusses their role in enhancing scalability, interpretability, and accuracy across industries including healthcare, finance, and smart manufacturing. Through four key outlines, we investigate how these systems combine the strengths of various AI approaches to handle the volume, velocity, and variety of big data.