Author: Shakya Singha Guria
DOI Link: https://doi.org/10.70798/Bijmrd/04020016
Abstract: The rapid expansion of digital technologies has resulted in unprecedented growth in data generation across scientific, industrial, and social domains. Traditional analytical techniques, often dependent on deterministic and precise mathematical formulations, struggle to handle uncertainty, incompleteness, and nonlinear relationships inherent in real-world datasets. Soft computing, a paradigm introduced to emulate human-like reasoning and adaptive learning, provides flexible and efficient approaches for complex data analysis. This research article examines the application of soft computing methods—including fuzzy logic, artificial neural networks, genetic algorithms, swarm intelligence, and probabilistic reasoning—in enhancing data analysis efficiency. The study explores conceptual foundations, methodological frameworks, domain applications, hybrid models, challenges, and future research directions. The findings suggest that soft computing significantly improves predictive accuracy, adaptability, and robustness in data-driven environments, thereby playing a transformative role in modern Artificial Intelligence systems.
Keywords: Soft Computing, Data Analysis, Artificial Intelligence, Neural Networks, Fuzzy Logic,Evolutionary Algorithms, Machine Learning, Optimization..
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