SOFT COMPUTING AND FUZZY SET-ENHANCED DEEP LEARNING MODEL FOR BIOLOGICAL DATA INTERPRETATION

Abstract

The integration of soft computing techniques with deep learning has opened new possibilities for modeling complex and uncertain biological systems. Traditional deep learning models often struggle to handle ambiguity, noise, and imprecision inherent in biological data such as gene expression, protein interactions, and medical imaging. To address these challenges, this paper proposes the development of a soft and fuzzy set-based deep learning model for biological applications.

The proposed framework incorporates fuzzy set theory and soft set principles into deep neural networks to effectively manage uncertainty and vagueness in biological datasets. Fuzzy membership functions are used to represent imprecise biological features, while soft set theory provides a flexible parameterization mechanism for handling incomplete and uncertain information. These components are integrated into the learning process to enhance feature representation and decision-making capabilities.

The model is evaluated on biological datasets, demonstrating improved classification accuracy, robustness, and interpretability compared to conventional deep learning approaches. The hybrid framework is particularly effective in tasks such as disease prediction, gene classification, and bio-signal analysis. By combining soft computing with deep learning, the proposed approach provides a reliable and adaptive solution for complex biological data analysis.