Search Results (182)

Meteorological data from automatic weather stations (AWS) in tea plantations is critical for agricultural management, but is often compromised by sensor errors and physical implausibilities that traditional quality control (QC) methods fail to detect. This study proposes a novel, meteorologically informed QC framework that mines association rules from long-term ERA5 reanalysis data (2012–2023) using the Apriori algorithm to establish a knowledge base of normal multivariate atmospheric patterns. A comprehensive feature engineering process generated temporal, physical, and statistical features, which were discretized using meteorological thresholds. The mined rules were filtered, prioritized, and integrated with hard physical constraints. The system employs a fuzzy logic mechanism for violation assessment and a weighted anomaly scoring system for classification. When validated on a synthetic dataset with injected anomalies, the method significantly outperformed traditional QC techniques, achieving an F1-score of 0.878 and demonstrating a superior ability to identify complex physical inconsistencies. Application to an independent historical dataset from a Zhenjiang tea plantation (2008–2016) successfully identified 14.6% anomalous records, confirming the temporal transferability and robustness of the approach. This framework provides an accurate, interpretable, and scalable solution for enhancing the quality of meteorological data, with direct implications for improving the reliability of frost prediction and pest management in precision agriculture. Full article

The Internet of Military Things (IoMT) relies on Long-Range Wide Area Networks (LoRaWAN) for low-power, long-range communication in critical applications like border security and soldier health monitoring. However, conventional priority-based flow control mechanisms, which rely on static classification thresholds, lack the adaptability to handle the nuanced, continuous nature of physiological data and dynamic network states. To overcome this rigidity, this paper introduces a novel, domain-adaptive Fuzzy Logic Flow Control (FFC) protocol specifically tailored for LoRaWAN-based IoMT. While employing established Mamdani inference, the FFC system innovatively fuses multi-parameter physiological data (body temperature, blood pressure, oxygen saturation, and heart rate) into a continuous Health Score, which is then mapped via a context-optimised sigmoid function to dynamic transmission intervals. This represents a novel application-layer semantic integration with LoRaWAN’s constrained MAC and PHY layers, enabling cross-layer flow optimisation without protocol modification. Simulation results confirm that FFC significantly enhances reliability and energy efficiency while reducing latency relative to traditional static priority architectures. Seamlessly integrated into the NS-3 LoRaWAN simulation framework, the FFC protocol demonstrates superior performance in IoMT communications. Simulation results confirm that FFC significantly enhances reliability and energy efficiency while reducing latency compared with traditional static priority-based architectures. It achieves this by prioritising high-priority health telemetry, proactively mitigating network congestion, and optimising energy utilisation, thereby offering a robust solution for emergent, health-critical scenarios in resource-constrained environments. Full article

Supercooled liquid water (SLW) in mixed-phase clouds significantly influences precipitation efficiency and aviation safety. However, a comprehensive understanding of its vertical structure has been hampered by a lack of sustained, vertically resolved observations over the North China Plain. This study presents the first systematic analysis of SLW vertical distribution and microphysics in this region, utilizing a year-long dataset (2022) from synergistic ground-based instruments in Beijing. Our retrieval approach integrates Ka-band cloud radar, microwave radiometer, ceilometer, and radiosonde data, combining fuzzy-logic phase classification with a liquid water content inversion constrained by column liquid water path. Key findings reveal a distinct bimodal seasonality: SLW primarily occurs at mid-to-upper levels (4–7.5 km) during spring and summer, driven by convective lofting, while winter SLW is confined to lower altitudes (1–2 km) under stable atmospheric conditions. The temperature-dependent occurrence probability of SLW clouds has an annual maximum at −12 °C. The diurnal variation in SLW in summer shows peaks in the afternoon and at night, corresponding to convective cloud activity. Spring, autumn, and winter do not exhibit strong diurnal variations. Retrieved microphysical properties, including liquid water content and droplet effective radius, are consistent with in situ aircraft measurements, validating our methodology. This analysis provides a critical observational benchmark and offers actionable insights for improving cloud microphysics parameterizations in models and optimizing weather modification strategies, such as seeding altitude and timing, in this water-stressed region. Full article

Major depressive disorder (MDD) is a prevalent mental health condition that requires accurate and objective diagnostic tools. Electroencephalogram (EEG) signals provide valuable insights into brain activity and have been widely studied for mental disorder classification. In this study, we propose a novel DML + ANFIS framework that integrates deep metric learning (DML) with an adaptive neuro-fuzzy inference system (ANFIS) for the automated diagnosis of major depressive disorder (MDD) using EEG time series signals. Time–frequency features are first extracted from raw EEG signals using the short-time Fourier transform (STFT) and the continuous wavelet transform (CWT). These features are then embedded into a low-dimensional space using a DML approach, which enhances the inter-class separability between MDD and healthy control (HC) groups in the feature space. The resulting time–frequency feature embeddings are finally classified using an ANFIS, which integrates fuzzy logic-based nonlinear inference with deep metric learning. The proposed DML + ANFIS framework was evaluated on a publicly available EEG dataset comprising MDD patients and healthy control (HC) subjects. Under subject-dependent evaluation, the STFT-based DML + ANFIS and CWT-based models achieved an accuracy of 92.07% and 98.41% and an AUC of 97.28% and 99.50%, respectively. Additional experiments using subject-independent cross-validation demonstrated reduced but consistent performance trends, thus indicating the framework’s ability to generalize to unseen subjects. Comparative experiments showed that the proposed approach generally outperformed conventional deep learning models, including Bi-LSTM, 2D CNN, and DML + NN, under identical experimental conditions. Notably, the DML module compressed 1280-dimensional EEG features into a 10-dimensional embedding, thus achieving substantial dimensionality reduction while preserving discriminative information. These results suggest that the proposed DML + ANFIS framework provides an effective balance between classification performance, generalization capability, and computational efficiency for EEG-based MDD diagnosis. Full article

Accurate classification of wafer map defect patterns is crucial for enhancing yield in semiconductor manufacturing. To address the problem of deep learning model over-fitting to label noise present in real industrial data, this study proposes a fuzzy logic-based framework for identifying both single and composite-type defect patterns. To demonstrate the robustness of our approach, we utilized the public dataset WM-811K and developed a Fuzzy Inference System (FIS) that leverages quantitative metrics such as the Center Zone Density (CZD). Data quality was also improved through preprocessing steps, including resolving class imbalances and refining labels via expert review. The performance of the proposed FIS was evaluated against a quantitative feature-based neural network, an FIS-neural network hybrid, and a CNN model. Experimental results showed that in single-pattern classification, the proposed FIS model achieved the highest accuracy of 99.20%, followed by the feature-based neural network (91.63%), the FIS-neural network hybrid model (88.55%), and the CNN (81.06%). These results prove that the proposed FIS approach maintains high classification accuracy while offering the advantages of interpretability and rule-based adjustability. This framework presents a practical solution that can effectively integrate domain knowledge to reduce the risk of overfitting in data environments with imperfect labels. Full article

Although Artificial Intelligence (AI) has achieved notable performance, particularly in medicine, the structural opacity leading to the black-box phenomenon inhibits interpretability, thus necessitating a balance (Symmetry) between performance and transparency. Specifically, in the medical domain, effective diagnosis requires that high predictive performance be symmetrically counterbalanced by sufficient trust and explainability for clinical practice. Existing visualization techniques like Grad-CAM can highlight attention regions but provide limited insight into the reasoning process and often focus on irrelevant areas. To address this limitation, we propose a Fuzzy Attention Rule (FAR) model that extends fuzzy inference to MRI (Magnetic Resonance Imaging) image classification. The FAR model applies pixel-level fuzzy membership functions and logical operations (AND, OR, AND + OR, AND × OR) to generate rule-based attention maps, enabling explainable and convolution-free feature extraction. Experiments on Kaggle’s Brain MRI and Alzheimer’s MRI datasets show that FAR achieves comparable accuracy to Resnet50 while using far fewer parameters and significantly outperforming MLP. Quantitative and qualitative analyses confirm that FAR focuses more precisely on lesion regions than Grad-CAM. These results demonstrate that fuzzy logic can enhance both the explainability and reliability of medical AI systems without compromising performance. Full article

Astronomy provides an exceptional context for developing data literacy, critical thinking, and computational skills in education. This paper presents a project-based learning (PBL) framework that integrates exploratory data analysis (EDA), fuzzy logic, and explainable artificial intelligence (XAI) to teach students how to extract and interpret scientific knowledge from real astronomical data. Using open-access resources such as NASA’s JPL Horizons and ESA’s Gaia DR3, together with Python libraries like Astroquery and Plotly, learners retrieve, process, and visualize dynamic datasets of comets, asteroids, and stars. The methodology follows the full data science pipeline, from acquisition and preprocessing to modeling and interpretation, culminating with the application of the FAS-XAI framework (Fuzzy-Adaptive System for Explainable AI) for pattern discovery and interpretability. Through this approach, students can reproduce astronomical analyses and understand how data-driven methods reveal underlying physical relationships, such as orbital structures and stellar classifications. The results demonstrate that combining EDA with fuzzy clustering and explainable models promotes deeper conceptual understanding and analytical reasoning. From an educational perspective, this experience highlights how inquiry-based and computationally rich activities can bridge the gap between theoretical astronomy and data science, empowering students to see the Universe as a laboratory for exploration, reasoning, and discovery. This framework thus provides an effective model for incorporating artificial intelligence, open data, and reproducible research practices into STEM education. Full article

As an important component of the global fishery economy, the crab breeding and processing industry faces the dual challenges of sustainable development and technological upgrading. This paper first systematically analyzes the regional distribution and core biological characteristics of major global economic crab species, laying a foundation for the targeted design of processing technologies and equipment. Secondly, based on advances in crab processing technology, the industry is categorized into two systems: live crab processing and dead crab processing. Live crab processing has formed a full-chain technological system of “fishing–temporary rearing–depuration–grading–packaging”. Dead crab processing focuses on high-value utilization: high-pressure processing enhances the quality of crab meat; liquid nitrogen quick-freezing combined with modified atmosphere packaging extends shelf life; and biological fermentation and enzymatic hydrolysis facilitate the green extraction of chitin from crab shells. In terms of intelligent equipment application, sensor technology enables full coverage of aquaculture water quality monitoring, precise classification during processing, and vitality monitoring during transportation. Automation technology reduces labor costs, while fuzzy logic algorithms ensure the process stability of crab meat products. The integration of the Internet of Things (IoT) and big data analytics, combined with blockchain technology, enables full-link traceability of the “breeding–processing–transportation” chain. In the future, cross-domain technological integration and multi-equipment collaboration will be the key to promoting the sustainable development of the industry. Additionally, with the support of big data and artificial intelligence, precision management of breeding, processing, logistics, and other links will realize a more efficient and environmentally friendly crab industry model. Full article

The detection of exoplanets in space-based photometry relies on identifying periodic transit signatures in stellar light curves. The Kepler Threshold Crossing Events (TCE) catalog collects all periodic dimming signals detected by the pipeline, while the Kepler Objects of Interest (KOI) catalog provides vetted dispositions (CONFIRMED, CANDIDATE, FALSE POSITIVE). However, the pathway from raw TCE detections to KOI classifications remains ambiguous in many borderline cases. We introduce FAS-XAI, a framework that integrates Fuzzy C-Means (FCM) clustering, supervised learning, and explainable AI (XAI) to improve transparency in exoplanet candidate classification. FCM applied to TCE parameters (period, duration, depth, and SNR) reveals three meaningful regimes in the transit-signal space and quantifies ambiguity through fuzzy memberships. Linking these clusters to KOI dispositions highlights a progressive consolidation of confirmed planets within the high-SNR, medium-duration regime. A supervised XGBoost classifier trained on KOI labels and augmented with fuzzy memberships achieves strong performance (Accuracy = 0.73, Macro F1 = 0.69, ROC–AUC = 0.855), clearly separating CONFIRMED and FALSE POSITIVE objects while appropriately reflecting the transitional nature of CANDIDATES. SHAP, LIME, and ELI5 provide consistent global and local attributions, identifying period, duration, depth, SNR, and fuzzy ambiguity as the key explanatory features. Finally, stellar parameters from Kepler DR25 validate the physical plausibility of the detected regimes, demonstrating that FAS-XAI captures astrophysically meaningful patterns rather than purely statistical structures. Overall, the framework illustrates how fuzzy logic and explainable AI can jointly enhance the interpretability and scientific rigor of exoplanet vetting pipelines. Full article

The widespread dissemination of misleading news presents serious challenges to public discourse, democratic institutions, and societal trust. Misleading-news classification (MNC) has been extensively studied through deep neural models that rely mainly on semantic understanding or large-scale pretrained language models. However, these methods often lack interpretability and are computationally expensive, limiting their practical use in real-time or resource-constrained environments. Existing approaches can be broadly categorized into transformer-based text encoders, hybrid CNN–LSTM frameworks, and fuzzy-logic fusion networks. To advance research on MNC, this study presents a lightweight multimodal framework that extends the Fuzzy Deep Hybrid Network (FDHN) paradigm by introducing a linguistic and behavioral biometric perspective to MNC. We reinterpret the FDHN architecture to incorporate linguistic cues such as lexical diversity, subjectivity, and contradiction scores as behavioral signatures of deception. These features are processed and fused with semantic embeddings, resulting in a model that captures both what is written and how it is written. The design of the proposed method ensures the trade-off between feature complexity and model generalizability. Experimental results demonstrate that the inclusion of lightweight linguistic and behavioral biometric features significantly enhance model performance, yielding a test accuracy of 71.91 ± 0.23% and a macro F1 score of 71.17 ± 0.26%, outperforming the state-of-the-art method. The findings of the study underscore the utility of stylistic and affective cues in MNC while highlighting the need for model simplicity to maintain robustness and adaptability. Full article

This article presents the development of a modular software suite for automated analysis of scientific publications in PDF format. The system integrates vectorization, clustering, topic modelling, dimensionality reduction, and fuzzy logic to combine both formal (vector-based) and semantic (topic-based) approaches. Interactive 3D visualization supports intuitive exploration of thematic clusters, allowing users to highlight relevant documents and adjust analytical parameters. Validation on a maritime safety case study confirmed the system’s ability to process large publication collections, identify relevant sources, and reveal underlying knowledge structures. Compared to established frameworks such as PRISMA or Scopus/WoS Analytics, the proposed tool operates directly on full-text content, provides deeper thematic classification, and does not require subscription-based databases. The study also addresses the limitations arising from data bias and reproducibility issues in the semantic interpretability of safety-critical decision-making systems. The approach offers practical value for organizations in safety-critical domains—including transportation, energy, cybersecurity, and human–machine interaction—where rapid access to thematically related research is essential. Full article

Divisible residuated lattices and MTL algebras are algebraic structures connected with algebras in t-norm-based fuzzy logics, being examples of residuated lattices. They are an important topic in the study of fuzzy logic. The purpose of this paper is to investigate and give classifications of these types of algebras. From computational considerations, we analyze the structure of these residuated lattices of small size n (2 $\le n\le$ 5), and we give summarizing statistics. To extend these results to higher sizes, we used a computer and a constructive algorithm for generating all finite residuated lattices. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

This study aimed to evaluate the effectiveness of a multimodal trust assessment approach that integrated electroencephalography (EEG) and self-report questionnaires compared with unimodal methods within the context of lower-limb rehabilitation training. Twenty-one mobility-impaired participants performed tasks using handrails, walkers, and stairs. Synchronized EEG, questionnaire, and behavioral data were collected. EEG trust scores were derived from the alpha-beta power ratio, while subjective trust was assessed via questionnaire. An adaptive neuro-fuzzy inference system was used to fuse these into a composite score. Analyses included variance, correlation, and classification consistency against behavioral ground. Results showed that EEG-based scores had higher dynamic sensitivity (Spearman’s ρ = 0.55) but greater dispersion (Kruskal–Wallis H-test: p = 0.001). Questionnaires were more stable but less temporally precise (ρ = 0.40). The fused method achieved stronger behavioral correlation (ρ = 0.59) and higher classification consistency (κ = 0.69). Cases with discordant unimodal results revealed complementary strengths: EEG captured real-time neural states despite motion artifacts, while questionnaires offered contextual insight prone to bias. Multimodal fusion through fuzzy logic mitigated the limitations of isolated assessment methods. These preliminary findings support integrated measures for adaptive rehabilitation monitoring, though further research with a larger cohort is needed due to the small sample size. Full article

Fuzzy logic has been applied to a wide range of problems, including process control, object recognition, image and signal processing, prediction, classification, decision-making, optimization, and time series analysis. These apply to solar energy systems. Though experts in renewable energy prefer fuzzy logic techniques, their contribution to the decision-making process of solar energy systems lies in the possibility of illustrating risk factors and introducing the concepts of linguistic variables of data from solar energy applications. In solar energy systems, the primary beneficiaries and audience of the fuzzy logic techniques are solar energy policy makers, as it concerns decision-making models, ranking of criteria or weights, and assessment of the potential location of the installation of solar energy plants, depending on the case. In a real-world scenario, fuzzy logic allows easy and efficient controller configuration in a non-linear control system, such as a solar panel. This study attempts to review the role and contribution of fuzzy logic in solar energy based on its applications. The findings from the review revealed that the fuzzy logic application identifies and detects faults in solar energy systems as well as in the optimization of energy output and the location of solar energy plants. In addition, fuzzy model (predicting), hybrid model (simulating performance), and multi-criteria decision-making (MCDM) are components of fuzzy logic techniques. As the review indicated, these are useful as a solution to the challenges of solar energy systems. Importantly, the integration and incorporation of fuzzy logic and neural networks should be recommended for the efficient and effective performance of solar energy systems. Full article