Type-2 Fuzzy Deep Learning Framework for Robust Marine Species Identification Under Underwater Environmental Uncertainty

2026-09-17T11:15:42+00:00

Underwater imagery is characterized by non-stationary optical noise, unpredictable turbidity, and inconsistent illumination that severely undermine the reliability of conventional deep learning classifiers used for marine species identification. This study proposes IT2F-AquaNet, an interval type-2 fuzzy deep learning framework that embeds a second layer of uncertainty modeling, termed the footprint of uncertainty, directly within a convolutional-transformer hybrid backbone to explicitly represent both aleatoric image noise and epistemic labeling ambiguity. The framework couples an adaptive preprocessing stage, an Interval Type-2 Fuzzy Logic System (IT2FLS) uncertainty module, a fuzzy feature-fusion layer employing Karnik-Mendel type reduction, and an uncertainty-aware attention classifier that outputs both a species label and a calibrated confidence band. The framework was evaluated on a composite benchmark built from multiple public underwater datasets augmented with simulated turbidity, illumination drift, and motion blur. Experimental results demonstrate that IT2F-AquaNet attains 93.8% classification accuracy and a macro F1-score of 92.9%, outperforming six state-of-the-art baselines by margins of 5.4 to 9.6 percentage points under degraded visual conditions. Ablation analysis confirms that the fuzzy uncertainty module contributes the largest performance gain among all architectural components. These findings position type-2 fuzzy-augmented deep architectures as a viable direction for dependable marine biodiversity monitoring systems.

Theoretical Foundations, System Design Methodologies, and Applications of Fuzzy Sets

2026-09-18T11:34:33+00:00

Fuzzy set theory, introduced by Lotfi Zadeh in 1965, extends classical set theory by allowing partial membership, providing a mathematically rigorous framework for representing and reasoning about vagueness and imprecision. Building on this foundation, fuzzy logic systems translate human-like linguistic reasoning into computational form, enabling control and decision-making under uncertainty that traditional binary logic handles poorly. This paper presents a comprehensive review of fuzzy sets and fuzzy logic design, covering the mathematical foundations of membership functions and fuzzy operators, the architecture of fuzzy inference systems, including fuzzification, rule evaluation, and defuzzification stages, and the principal design methodologies used to construct fuzzy rule bases, including expert-derived and data-driven approaches such as neuro-fuzzy and genetic-fuzzy hybrids. The paper further surveys major application domains, including industrial control, consumer electronics, medical decision support, and financial forecasting, and offers a comparative analysis against classical crisp-logic approaches. Finally, the paper discusses open challenges in fuzzy system design, including rule-base scalability, interpretability-accuracy trade-offs, and integration with modern machine learning pipelines, before outlining promising directions for future research, including type-2 fuzzy sets and explainable fuzzy-neural architectures.

Journal of Fuzzy Sets and Fuzzy Logic Design (e-ISSN: 3049-0227)

Type-2 Fuzzy Deep Learning Framework for Robust Marine Species Identification Under Underwater Environmental Uncertainty

Theoretical Foundations, System Design Methodologies, and Applications of Fuzzy Sets