A NOVEL SOFT AND FUZZY SET-BASED DEEP LEARNING FRAMEWORK FOR BIOLOGICAL DATA MODELING

Abstract

Biological data is inherently complex, uncertain, and often affected by noise and variability, making accurate modeling and analysis a challenging task for conventional deep learning approaches. To address these limitations, this paper proposes a novel soft and fuzzy set-based deep learning framework for biological data modeling. The proposed approach integrates fuzzy set theory and soft set principles into deep neural networks to effectively handle ambiguity, imprecision, and incomplete information present in biological datasets such as gene expression data, medical images, and bio-signals.

In this framework, biological features are first transformed into fuzzy representations using appropriate membership functions, enabling the model to capture gradual transitions and uncertain patterns. Soft set-based feature selection is then applied to identify the most relevant attributes, reducing dimensionality and improving computational efficiency. The refined feature set is processed through a deep learning architecture enhanced with fuzzy reasoning capabilities, allowing the model to learn complex nonlinear relationships while maintaining robustness against noise.

Experimental analysis demonstrates that the proposed framework achieves improved classification accuracy, stability, and interpretability compared to traditional deep learning models. The integration of soft computing techniques enhances the model’s ability to generalize across diverse biological datasets. This framework provides a reliable and scalable solution for biological data analysis and has potential applications in disease diagnosis, bioinformatics, and medical decision support systems.