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

A Fuzzy Logic-Based Evaluation System for Primary and Secondary Schools Using Multi-Criteria Decision-Making

2026-10-05T11:58:11+00:00

Conventional student assessment methods primarily emphasize academic achievement and often fail to capture the complexity of learners’ cognitive, behavioral, and socio-emotional development. As a result, evaluation outcomes may not accurately represent students’ overall learning progress or effectively support educational decision-making. This study proposes an integrated student evaluation framework that combines Fuzzy Logic (FL) and Multi-Criteria Decision-Making (MCDM) techniques to develop a holistic, transparent, and context-sensitive grading model for students from Grades 1 to 12 in the Dhanbad district of Jharkhand, India. The framework incorporates both quantitative and qualitative criteria, including academic performance, learning effort, conceptual understanding, classroom participation, motivation, and socio-emotional development, providing a comprehensive assessment of student achievement. To address uncertainty and subjectivity in educational evaluation, fuzzy logic converts linguistic assessments into computationally meaningful values. Teachers’ expert judgments are analyzed using fuzzy linguistic variables to examine the influence of educational infrastructure, classroom conditions, and learning resources on student outcomes. The methodology includes designing a fuzzy inference system, developing membership functions, and rule-based knowledge structures. The proposed framework supports evidence-based instructional planning, personalized feedback, and equitable assessment while reducing inconsistencies associated with traditional grading. By integrating computational intelligence with educational evaluation, the study presents a flexible, transparent, and learner-centered assessment model aligned with India’s National Education Policy (NEP) 2020, enabling more accurate and context-responsive student evaluation.

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.

An Adaptive Fuzzy-Neural Decision Framework for Uncertainty-Aware Classification under Sensor Drift

2026-10-31T04:52:12+00:00

Background: Gas-sensor arrays lose accuracy over time because sensor responses drift with ageing, poisoning, ambient conditions, and classifiers trained on early measurements become inaccurate and over-confident.

Objectives: To design and rigorously evaluate an Adaptive Fuzzy-Neural framework with Uncertainty Calibration (AFN-UC) that remains usable after deployment without new labels.

Methodology: AFN-UC combines a label-free, prior-matched standardisation of each incoming batch, Gaussian fuzzification with Takagi–Sugeno–Kang rule activations, a compact neural head, temperature calibration, a cost-based reject rule, and a batch-level drift monitor. It was evaluated on the public 36-month gas-sensor drift archive (13,910 samples) and on 10 simulated instruments, against five static, eight adaptive, and one supervised reference method, with 10 seeds, bootstrap confidence intervals, and Holm-corrected Wilcoxon tests.

Results: On real data, AFN-UC reached a macro-F1 score of 0.643 compared with 0.628 for the best static model, but was not better than strong adaptive baselines; batch-standardised logistic regression scored 0.693. In simulation, it reached 0.979. Prior-matched standardisation was decisive when one gas dominated a batch, raising macro-F1 from 0.297 to 0.485 on real data and from 0.415 to 0.674 in simulation. A cost-based reject rule removed 72 % of real-data errors, and inference cost 13.4 µs per sample.

Conclusion: Label-free, prior-aware adaptation is the key ingredient; the neural head adds no accuracy on real data, and calibration under severe drift remains an open problem.

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.