Anil Kumar Jonnalagadda and Praveen Kumar Myakala, Independent Researcher, USA

The exponential growth of data has outpaced traditional computing systems, necessitating innovative approaches for processing, managing, and extracting actionable insights. In this study, we explore how techniques like fuzzy logic, neural networks, and evolutionary algorithms can help solve some of the biggest problems in Big Data, such as uncertainty, imprecision, and noise in real-world datasets. These methods offer unparalleled adaptability and scalability for diverse applications. We propose a comprehensive framework that integrates hybrid approaches, such as neuro-fuzzy systems and evolutionary-fuzzy optimization, to enhance clustering, feature selection, and predictive analytics with improved accuracy and interpretability. Extensive experiments on real-world datasets from domains like healthcare and IoT demonstrate significant advancements in processing speed, resource utilization, and analytical efficiency over traditional methods. This study highlights the pivotal role of soft computing in unlocking the true potential of Big Data, enabling innovative solutions and driving meaningful advancements across industries.

Big Data, Soft Computing, Fuzzy Logic, Neural Networks, Evolutionary Algorithms, Hybrid Systems, Predictive Analytics, Feature Selection, Clustering.