Search Results (110)

Existing post hoc XAI (Explainable Artificial Intelligence) methods produce numerical attributions without symbolic structure (SHAP, LIME), low-coverage local rules (Anchors), or crisp tree surrogates without an interpretable rule-level uncertainty proxy. We present a fuzzy rule-based explanation framework for tabular black-box classifiers, extracting global IF–THEN rules with linguistic labels. This was validated on a 13-dataset benchmark with four model families (Wilcoxon, Friedman, TOST equivalence): (i) prediction-boundary-aware fuzzy partitioning raises mean fidelity from a vanilla Wang–Mendel baseline of 0.736 to 0.893 (+10.4 pp excluding the Breast Cancer outlier; +15.7 pp aggregate, both transparently reported); (ii) fired-rule consequent entropy provides a zero-cost rule-level uncertainty proxy (Spearman ρ = 0.420 with model prediction entropy, significant on 11/12 datasets—moderate by Cohen’s convention, with a 4/12 weak-correlation tail; complementary to probability-entropy and margin baselines). Fidelity is statistically equivalent to tree surrogates on classification (TOST p = 0.002, δ = 0.05) at ≈100% coverage. SHAP/LIME are excluded from the formal stability ranking because the perturbation metric measures the wrapped black-box rather than the attribution vector; cross-explainer comparison is reported in grouped form (full-coverage surrogates vs. local-coverage methods). On continuous regression (California Housing fidelity 0.422 vs. TreeSurrogate 0.840) and XOR-type multi-feature interactions, the framework is structurally weaker, addressed by a planned TSK extension. Full article

Agriculture faces increasing pressure to meet global food demands while minimizing environmental harm, yet many current practices remain unsustainable. This study develops the EAGLES framework (Environmental Impact of Agriculture using Life Cycle Assessment and Expert System) to address limitations in current sustainability assessment approaches. Life Cycle Assessment (LCA) evaluates environmental impacts but is limited by data availability and usability, especially for new users in agriculture. The objective of this study is to address this gap by developing the EAGLES framework, which organizes agricultural LCA data within an expert system (knowledge-based, rule-based) structure to guide the application of LCA phases. The knowledge base is developed from Phase 1 datasets reported in previous work and additional datasets developed as part of this study. The rule base uses if–then logic to check if the required data are available and to guide movement across the LCA phases. The framework is designed to support multiple scope types, impact categories, and assessment methods within a single structure. The framework was applied to rice production in Mississippi (2021) to assess marine eutrophication and acidification. The case study results show that the framework enables consistent progression across all LCA phases and produces impact results that can be interpreted using normalization and weighting. A second pathway was applied to assess acidification, demonstrating that the framework can handle multiple impact categories within the same system. By organizing data and linking inventory, impact assessment, and interpretation into a single process, the framework provides a structured and transparent approach for conducting agricultural LCA. Full article

Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty. Full article

Accurate emergency triage is critical for patient safety and efficient resource allocation. Large language models (LLMs) have shown promise in clinical reasoning tasks; however, their predictions may be inconsistent when not grounded in structured clinical knowledge. This study proposes TRAG (Triage Retrieval-Augmented Generation), a domain-specific framework that integrates rule-based knowledge retrieval with LLM reasoning to support Emergency Severity Index (ESI) prediction. TRAG retrieves triage rules encoded as if–then logic from an ESI knowledge base and incorporates them into the model prompt to guide ESI prediction. The framework evaluated multiple LLMs under different retrieval settings (Top-5, Top-10, Top-15) and compared with zero-shot baselines on 100 curated triage cases. Performance was assessed using accuracy, precision, recall, F1-score, and Quadratic Weighted Kappa (QWK). Results show that retrieval-augmented prompting improves classification performance, particularly for lower-performing models. For example, GPT-3.5 accuracy increased from 0.45 to 0.68 and QWK from 0.67 to 0.82 under the Top-15 setting. Improvements were also observed in reducing under-triage in several configurations, while higher-performing models demonstrated more modest and configuration-dependent gains. These findings suggest that integrating structured clinical rules within a retrieval-augmented framework can enhance the consistency and reliability of LLM-based triage prediction. The proposed TRAG framework highlights the potential of combining structured clinical knowledge with generative models to support safer and more interpretable decision-making in emergency care. Full article

The increasing global demand for plant-based meat alternatives, driven by concerns for environmental sustainability, animal welfare, and health, has led to a growing need for reliable food authentication methods. Animal-based and plant-based meat products are visually similar, which poses a challenge for consumers to distinguish them. We developed an electronic nose (e-nose) system with an array of MQ gas sensors (MQ-2, MQ-3, MQ-7, MQ-135, MQ-136, MQ-138), an Arduino MEGA microcontroller, and an LCD for displaying results. A fuzzy logic algorithm was implemented to process sensor data and enable decision-making through membership functions and IF-THEN rule evaluation to classify meat products as either animal meat or plant-based meat. The system performance was validated with 20 independent test samples. Determination accuracy for both categories, as well as the overall accuracy, was assessed using a confusion matrix. The findings demonstrate that the e-nose system can reliably distinguish between animal-based and plant-based meat products. Full article

Background: Most individuals develop smoking habits in adolescence, highlighting the need for a smoking prevention program targeted at this age group. The use of “Implementation Intentions” (If-Then plans) about how to refuse a cigarette combined with anti-smoking messages has been shown to be effective in the UK. However, there is a scarcity of data regarding school-based smoking prevention interventions among adolescents available to countries with high tobacco consumption rates, like Greece. Objectives: To describe the cultural adaptation procedure and the evaluation protocol for the school-based “Implementation Intentions” program aimed at reducing tobacco use susceptibility among Greek adolescents aged 13–16 in school settings. Methods: The present study is part of the EU-funded FRESHAIR4Life Program. We will use a mixed-methods approach with a pre- and post-intervention design in six conveniently selected secondary schools in Heraklion, Crete, Greece, to measure the intervention’s Reach, Effectiveness, Adoption, Implementation, and Maintenance using the RE-AIM framework. The study plans to involve three Master Trainers (MTs), 20–25 school teachers (to be trained by the MTs), and approximately 480 students. Participating schools will receive the “Implementation Intentions” intervention, which is based on a goal-setting technique where individuals commit to perform a particular behavior when a specific context arises. The study will consist of five sequential phases: Phase I involves training three Master Trainers (MTs) using the International Primary Care Respiratory Group (IPCRG’s) Teach-the-Teacher (TtT) curriculum, specifically focused on the implementation of our intervention. In Phase II, workshops will be held to co-create and culturally adapt the intervention. Phase III will involve teachers trained by MTs on delivering the intervention. In Phase IV, teachers will deliver the intervention among students in their schools. Data will be collected pre- and post-intervention through surveys, session logs, fidelity observations, feedback forms, and follow-up interviews or focus groups (Phase V). Quantitative data will be analyzed descriptively and by using paired t-tests and multiple linear regression analyses, while qualitative data will undergo thematic analysis. Discussion: The study protocol’s potential benefits extend beyond educating Greek adolescents on the risks associated with smoking. Active participation will empower and motivate young people to make informed, healthy choices. We expect the results could help create more effective, context-specific interventions, support policy changes aimed at decreasing the prevalence of adolescent smoking in Crete, Greece, and potentially be used by other countries as well. Full article

Accurate classification of breast cancer histopathology images is essential for early diagnosis and effective clinical management. However, conventional deep learning models often exhibit performance degradation under limited labeled data and lack interpretability, which restricts their clinical applicability. To address these challenges, we propose GAFR-Net, a robust and interpretable Graph Attention and Fuzzy-Rule Network designed for histopathology image classification under scarce supervision (defined here as less than 10% labeled data). GAFR-Net constructs a similarity-driven graph to model inter-sample relationships and employs a multi-head graph attention mechanism to capture complex relational representations among heterogeneous tissue structures. Meanwhile, a differentiable fuzzy-rule module integrates intrinsic topological descriptors—such as node degree, clustering coefficient, and label consistency—into explicit and human-readable diagnostic rules. This architecture establishes transparent IF–THEN inference mappings that emulate the heuristic reasoning process of clinical experts, thereby enhancing model interpretability without relying on post-hoc explanation techniques. Extensive experiments conducted on three public benchmark datasets—BreakHis, Mini-DDSM, and ICIAR2018—demonstrate that GAFR-Net consistently surpasses state-of-the-art methods across multiple magnifications and classification settings. These results highlight the strong generalization capability and practical potential of GAFR-Net as a trustworthy decision-support framework for weakly supervised medical image analysis. Full article

Reconfigurable logic is crucial for future adaptive computing, but is challenging to realize with conventional complementary metal-oxide-semiconductor technology due to the limited field-effect characteristics of the fundamental silicon devices. Two-dimensional materials offer a promising platform, yet enhancing their functional versatility requires novel operational mechanisms. Here, we demonstrate a single WSe₂/h-BN/graphene heterojunction capable of dynamically switching between distinct logic functions—XNOR and IMP (implication gate or “IF-THEN” gate)—simply by modulating the drain-source voltage. At a low bias of 0.3 V, the carrier distribution is governed by capacitive coupling, realizing an XNOR gate. Increasing the bias to 3 V activates Fowler–Nordheim tunneling between the graphene floating gate and the drain, enabling IMP logic operation. The interplay and voltage-induced transition between these two physical mechanisms underpin the device’s multifunctional capability. This work introduces a novel operational strategy for two-dimensional material-based reconfigurable logic, providing a pathway toward compact, adaptive hardware for post-CMOS computing. Full article

Breast cancer poses a global health risk and requires precision and accessibility in diagnostic measures. Ultrasound imaging is vital for breast lesion identification due to its safety, cost-effectiveness, and real-time capabilities. This paper presents a new fuzzy system architecture that utilizes ultrasound-based radiomics features to classify breast cancers. In order to ensure uniformity and consistency in shape-based characteristics limited to tumors, we calculate parameters such as elongation, compactness, spherical disproportion, and volumetrics following IBSI recommendations. We employ a hierarchical fuzzy system tree to handle high-dimensional data space and to identify the most discriminative characteristics. The selected features are incorporated into a modular fuzzy logic design that promotes transparency and maintains an auditable decision history according to clinical interpretability. Our framework enables the more accurate classification of breast cancer while addressing the beliefs and values prevalent in clinical applications. Tested on an independent set of data, the model achieved high accuracy of 99.60%, with low overfitting and strong generalization. To enhance its generalizability, we validated it on an internal dataset, attaining a sensitivity of 93.65%, a specificity of 99.24%, an AUC of 0.996, and an 18% reduction in unnecessary biopsies, as demonstrated through decision curve analysis, demonstrating substantial clinical utility across various settings. The findings confirm the system’s ability to identify intricate radiomic patterns linked to cancer. Due to its computing efficiency, it may be executed in real time during routine screening. The proposed radiomics-based fuzzy classification framework may offer a clinically beneficial approach for differentiating benign from malignant breast lesions. Explainability is enhanced with user-friendly artifacts for clinicians, including ranking IF-THEN rules and counterfactuals, all of which were validated in usability trials that demonstrated increased trust among radiologists compared to other technologies. Enhanced differentiation in the classification of various lesion types will decrease unnecessary biopsies. This approach integrates radiomics features with transparent and interpretable fuzzy logic to deliver enhanced predictors and a comprehensible framework for users, including physicians, to facilitate decision-making. This approach advances precision medicine standards through the early detection of lesions using more specific and systematic diagnostic instruments. Full article

The drying process in indirect solar dryers is strongly influenced by rapidly changing ambient conditions, resulting in highly nonlinear and dynamic system behavior. Accurate modeling is therefore essential for performance evaluation, process optimization, and reliable prediction of the drying chamber temperature, which plays a key role in ensuring efficient moisture removal while preserving the nutritional and sensory quality of dried products. In this study, a fuzzy logic–based modeling approach using the Mamdani inference system is developed to predict the drying chamber temperature over a wide range of operating conditions. Experimental measurements were carried out with solar radiation varying from 400 to 950 W/m² and ambient temperature ranging from 20 to 50 °C, covering both static and dynamic system responses. The fuzzy model employs solar radiation and ambient temperature as input variables, represented by five and three triangular membership functions, respectively, while the drying chamber temperature is defined as the output variable using five triangular membership functions (T1–T5). The Mamdani inference system consists of 15 “if–then” rules, and centroid defuzzification is applied to obtain crisp output values. Model validation across the investigated operating range demonstrates a strong agreement between predicted and experimental temperatures. For example, at a solar radiation of 700 W/m² and an ambient temperature of 46 °C, the predicted chamber temperature is 50.9 °C compared to a measured value of 51.0 °C, while at 750 W/m² and 50 °C, the predicted temperature of 52.0 °C closely matches the experimental value of 51.8 °C. Statistical evaluation yields RMSE = 0.38 °C, MAE = 0.29 °C, and R² = 0.997, demonstrating effective temperature tracking capability within the tested operating range. These results show that the Mamdani fuzzy logic approach can effectively represent the thermal behavior of an indirect solar dryer within the tested operating range. The proposed model also provides a promising basis for the future development of real-time intelligent control strategies aimed at improving energy efficiency and product quality. Full article

Soil liquefaction remains a critical geotechnical hazard during earthquakes, posing significant risks to infrastructure and urban resilience. Traditional empirical methods, while practical, often fall short in capturing complex parameter interactions and providing interpretable outputs. This study presents an interpretable machine learning (IML) framework for soil liquefaction assessment using Rough Set Theory (RST) to generate a transparent, rule-based predictive model. Leveraging a standardized SPT-based case history database, the model induces IF–THEN rules that relate seismic and geotechnical parameters to liquefaction occurrence. The resulting 25-rule set demonstrated an accuracy of 86.2% and strong alignment (93.8%) with the widely used stress-based semi-empirical model. Beyond predictive performance, the model introduces scenario maps and parameter interaction diagrams that elucidate key thresholds and interdependencies, enhancing its utility for engineers, planners, and policymakers. Notably, the model reveals that soils with high fines content can still be susceptible to liquefaction under strong shaking, and that epicentral distance plays a more direct role than previously emphasized. By balancing interpretability and predictive strength, this rule-based approach advances site-adaptive, explainable, and technically grounded liquefaction assessment—bridging the gap between traditional methods and intelligent decision support in geotechnical engineering. Full article

The Internet of Medical Things (IoMT) interconnects medical devices, software applications, and healthcare services through the internet to enable the transmission and analysis of health data. IoMT facilitates seamless patient care and supports real-time clinical decision-making. The IoMT faces substantial security threats due to limited device resources, high device interconnectivity, and a lack of standardization. In this paper, we present an Intrusion Detection System (IDS) called An Empirical Distribution Ranking-Based Fuzzy J48 Classifier for Multiclass Intrusion Detection in IoMT Networks (EDR-FJ48) to distinguish between regular traffic and multiple types of security threats. The proposed IDS is built upon the J48 decision tree algorithm and is designed to detect a wide range of attacks. To ensure the protection of medical devices and patient data, the system incorporates a fuzzy IF-THEN rule inference module. In our approach, fuzzy rules are formulated based on the fuzzified values of selected features, which capture the statistical behavior of the input observations. These rules enable interpretable and transparent decision-making and are applied before the final classification step. We thoroughly evaluated our methodology through extensive simulations using three publicly available datasets, such as WUSTL-EHMS-2020, CICIoMT2024, and ECU-IoHT. The results exhibit exceptional accuracy rates of 99.68%, 98.71%, and 99.43%, respectively. A comparative analysis against state-of-the-art models in the existing literature, based on metrics including accuracy, precision, recall, F1-score, and time complexity, reveals that our proposed method achieves superior results. This evidence suggests that our method constitutes a robust solution for mitigating security threats in IoMT networks. Full article

An upgrade to the quasi-Newton (QN) family of methods for solving unconstrained optimization problems is proposed. This research focuses on a detailed investigation of the Barzilai and Borwein (BB) gradient methods. The upgrade involves the use of neutrosophic logic to determine an additional parameter that will be incorporated into an appropriate step size for the BB iterations. Unlike previous research, which incorporated neutrosophic concepts into gradient methods by using only two objective-function values to calculate the input parameter during the neutrophication phase, this study determines the input parameter using three consecutive objective-function values. The main idea is to use appropriately defined membership functions to perform neutrosophication and de-neutrosophication. The set of if–then rules is based on two or more successive values of the objective function. This strategy also directly influences the design of the newly proposed method. Numerical comparisons demonstrate superior performance of the proposed methods with respect to Dolan–Moré performance profiles including the number of iterations, central processing unit (CPU) time, and number of function evaluations. Furthermore, experimental results confirm that the proposed algorithms can be effectively applied to image restoration tasks, particularly for image denoising, where they achieve competitive reconstruction quality and stable convergence behavior. Full article

Folk festivals and other intangible cultural heritage have received widespread attention, and their socio-cultural value can be used to promote tourism, strengthen local identity, and build city brands. However, it remains unclear how these intangible cultural heritage festivals transform their multi-dimensional and multi-configuration material characteristics into economic benefits and image enhancement. This study proposes a practical decision-making framework aimed at understanding how different festival design and governance strategies can work synergistically under different cultural conditions. Based primarily on a literature review and expert questionnaire survey, this study identified six stable materialized practice modules: productization, spatialization, experientialization, digitalization, branding/communication, and co-creation governance. At the same time, this framework also incorporates two other conditional intervention properties: classicism and novelty. The interactions between these modules shape people’s understanding of intangible cultural heritage festivals. Subsequently, this study used a multimodal national dataset that included official statistics, industry reports, e-commerce and social media data, questionnaires, and expert ratings to construct module scores and cultural attributes for 167 festival case studies. Through rough set analysis (RSA), this study simplifies the attributes and extracts clear “if-then” rules, establishing a configurational causal relationship between module configuration and classic/novel conditions to form high economic benefits and enhance local image. The findings of this study reveal a robust core built around spatialization, digitalization, and co-creative governance, with brand promotion/communication yielding benefits depending on the specific context. This further confirms that classicism reinforces the legitimacy and effectiveness of rituals/spaces and governance pathways, while novelty amplifies the impact of digitalization and immersive interaction. In summary, this study constructs an integrated and easy-to-understand process that links indicators, weights, and rules, and provides operational support for screening schemes and resource allocation in festival event combinations and venue brand governance. Full article

In data mining and exploration, outliers are specific and infrequent data that require special attention, as they may reveal potentially hazardous information. Detecting outliers can support, e.g., identification fraudulent credit card usage or unauthorized access to transactions, even hacking banking systems, etc. The paper proposes a definition of outlier in terms of fuzzy representations of expert knowledge and its application to detect outliers. The approach proposed has the potential to enhance the performance of outlier detection in various fields, including finance and banking data storage and analysis. By “enhance” we mean that the intention of the new method is to cooperate with known numerical methods, e.g., LOF, rather than supersede or deprecate them. The usefulness of the method is proven via providing new outlying observations for given datasets using input data expressed in an imprecise, linguistic manner. Full article