Search Results (93)

We developed an augmented Sungka board that integrates traditional Filipino gameplay with embedded sensor technology. Each pit is equipped with load cell sensors and HX711 analog-to-digital converters to accurately detect marble distribution and movement in real time. A Raspberry Pi 4 serves as the central controller, handling sensor data acquisition, game state processing, rule enforcement, and output display through a liquid crystal display. The system enables automatic score tracking, move validation, and real-time board updates without altering the physical structure or rules of Sungka. A rule-based decision algorithm using fuzzy logic and heuristic search evaluates possible moves in constant time, allowing seamless real-time interaction. Across 10,000 simulated games, the algorithm achieved win rates of 84.9% against random, 77.7% against greedy, and 56.3% against exact-match strategies, with statistically consistent performance. By combining reliable hardware sensing with intelligent decision support, the proposed system enhances engagement while preserving the cultural authenticity of Sungka. Full article

Humanitarian logistics faces challenges such as conflicting objectives, severe uncertainty, temporal dynamics, and the need for interpretable decisions. This research presents an integrated decision-making framework that simultaneously considers fuzzy uncertainty, system dynamics, and adaptive decision logic. Operational uncertainties are modeled using triangular fuzzy numbers and a dynamic representation of the system allows for continuous updating of decisions over time. Computational results based on simulated data show that the proposed framework is capable of generating stable, diverse, and interpretable solutions. An improvement in the average quality of the Pareto front of more than 5% and a reduction in the distance from the reference front of about 30% are observed compared to non-adaptive approaches. Also, stability and dynamic behavior analyses show that the decisions are robust to changing environmental conditions and parameters and have high adaptability. These features make the proposed framework a reliable tool for decision support in relief operations. Full article

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article

As digital technology becomes increasingly integral to modern industries, the risks posed by cyber threats, including malware, ransomware, and insider attacks, continue to rise, jeopardizing critical infrastructure including renewable energy system. The world is more vulnerable to sophisticated cyberattacks due to its reliance on smart grids and IoT-enabled renewable energy systems. Without specialized digital forensic frameworks, incident response and critical infrastructure resilience are limited. This research examines the pivotal role of digital forensics in defending renewable energy system against the growing wave of cyber threats. The study highlights the significance of digital forensics in enhancing incident response, evidence collection, and forensic analysis capabilities. Through detailed case studies, it investigates the implementation strategies of digital forensics to identify, track, and mitigate cyber risks. To address this objective, this study proposes a comprehensive and adaptive cybersecurity framework that integrates digital forensics and fuzzy multi-criteria decision-making to enhance cyber resilience in renewable energy systems. Drawing on relevant case studies, the research demonstrates how the integration of digital forensics with fuzzy logic supports dynamic threat evaluation and risk mitigation. Comparative analysis show that the proposed framework outperforms traditional methods in terms of detection accuracy, response time, and adaptability to evolving threat landscapes. Key contributions include: (1) a structured digital forensics-based cybersecurity model tailored to renewable energy systems, (2) application of fuzzy Analytical Hierarchy Process (AHP) for multi-criteria threat evaluation, and (3) policy-oriented recommendations for stakeholders to reinforce national cyber resilience in line with energy transition. The findings underscore the need for a cohesive cybersecurity strategy grounded in advanced decision-support systems to protect the future of sustainable energy. Full article

Reliable assessment of bridge technical condition is a key challenge in infrastructure management due to uncertainty, subjectivity, and heterogeneity inherent in inspection-based data. Traditional deterministic evaluation methods often fail to capture the gradual nature of structural deterioration and the complex interactions between bridge components. This study proposes a hybrid methodology that integrates fuzzy logic and artificial neural networks (ANNs) to quantify the overall technical condition of bridge structures using long-term inspection data. A comprehensive dataset, derived from real bridge inspection reports collected over more than 15 years across various regions of Ukraine, served as the basis for model development. Five key input parameters—substructure condition, superstructure condition, deck condition, overall structural condition, and channel and channel protection condition—were employed to compute an integrated Bridge Condition Assessment indicator using a Mamdani-type fuzzy inference system. The resulting fuzzy-based indicator was subsequently used as the target variable for training ANN models. To ensure optimal predictive performance and training stability, Bayesian Optimization was applied for systematic hyperparameter tuning. Model performance was evaluated using standard regression metrics, including MSE, MAE, MAPE, and the coefficient of determination (R²). The results demonstrate that the proposed approach enables accurate approximation of the fuzzy-based Bridge Condition Assessment indicator, with MAPE values as low as 0.2% and R² exceeding 0.982 for the best-performing model. The hybrid framework effectively combines interpretability and scalability, providing a decision-support framework based on fuzzy logic and surrogate modeling for automated fuzzy-based bridge condition assessment, maintenance prioritization, and integration into digital asset management systems. Full article

Ship lock zones represent bottlenecks and a particular challenge for authorities managing vessel traffic. Traditionally, the control strategy of such systems has relied heavily on the subjective judgment, experience, and tacit knowledge of ship lock operators. To address the inherent uncertainty and imprecision associated with these subjective assessments, fuzzy logic and fuzzy set theory have been adopted as appropriate mathematical frameworks. In this work, the control strategy and the Fuzzy Decision Support System (FDSS) of a single-chamber ship lock designed for two vessels on a two-way waterway are analyzed and modeled. The input data is generated based on a synthesized dataset reflecting the annual schedule of vessel arrivals. The software is based on proposals and suggestions of experienced ship lock operators, and it is further validated through vessel traffic simulations. Moreover, the development of an appropriate Supervisory Control and Data Acquisition (SCADA) system integrated with a Programmable Logic Controller (PLC) is detailed, providing the necessary infrastructure for real-time deployment of the fuzzy control algorithm. The proposed control system represents an original contribution and offers practical applications both as a decision-support tool for real-time lock management and as a training platform for novice or less experienced operators. Full article

Digital transformation is increasingly central to the development of transport systems, yet current research offers limited guidance on how digital maturity levels shape operational risk. Existing digital maturity models primarily support benchmarking and planning, but rarely integrate structured risk assessment. This study addresses this gap by proposing a framework that links digital maturity with the systematic identification and prioritisation of digital transformation risks. A Digital Maturity-Based Risk Assessment Framework (DMRisk-TS) is developed, classifying risks into three categories. Probability and severity are evaluated using fuzzy logic, while contextual modifiers account for the maturity gap and system coverage. The approach is demonstrated using a real tram transport system and the DMM-TRAM model. The analysis shows that risk profiles differ markedly across maturity levels. Low-maturity environments generate operational risks related to insufficient or non-integrated information. Transitioning between levels introduces implementation and data-integration risks. At high maturity levels, new systemic risks emerge, including error propagation, cyber vulnerabilities, and over-reliance on automated processes. DMRisk-TS offers a meaningful basis for understanding and managing risks in transport systems. The framework supports the prioritisation of mitigation actions, informs decisions on advancing maturity, and highlights that higher digitisation creates new classes of systemic risk. Full article

The coffee leaf miner (Leucoptera coffeella) is a major pest of coffee crops and can cause significant economic losses. Early monitoring is essential to support decision-making for its control. This study aimed to evaluate the potential of fuzzy logic for detecting leaf miner infestation using a 2.5-year historical series of Sentinel-2A satellite images processed on the Google Earth Engine platform. Field monitoring of coffee leaf miner infestation was carried out at the EPAMIG Experimental Field, located in São Sebastião do Paraíso, Minas Gerais, Brazil. The period evaluated was from September 2022 to April 2025. Vegetation indices were calculated using the Google Earth Engine platform, and a database was built with eight indices (NDVI, EVI, GNDVI, SR, IPVI, NDMI, MCARI, and CLMI) along with coffee leaf miner infestation data. Principal Component Analysis (PCA) was applied to reduce data dimensionality and identify the most relevant indices for distinguishing infested from healthy plants, explaining 90.9% of the total variance in the first two components (PC1 and PC2). The indices CLMI, IPVI, GNDVI, and MCARI showed the greatest contribution to class separation. A fuzzy inference model was implemented based on the mean index values and validated through performance metrics. The results indicated an overall accuracy of 79.1%, a sensitivity (recall) of 86.6%, a specificity of 66.6%, an F1-score of 0.838, a Kappa coefficient of 0.545, and an area under the curve (AUC) of 0.766. These findings confirm the potential of integrating orbital spectral data via Google Earth Engine with fuzzy logic analysis as an efficient tool, contributing to the adoption of more sustainable monitoring practices in coffee farming. The fuzzy logic system received as input the spectral values derived from Sentinel-2A imagery, specifically the indices identified as most relevant by the PCA (CLMI, IPVI, GNDVI, and MCARI). These indices were computed and integrated into the inference model through processing routines developed in the Google Earth Engine platform, enabling a direct connection between satellite-derived spectral patterns and the detection of coffee leaf miner infestation. Full article

Efficient water management is a critical challenge for modern agriculture, particularly in the context of increasing climate variability and limited freshwater resources. This study presents a comparative field-based evaluation of two fuzzy-logic-based irrigation decision support systems for vineyard management: a Mamdani-type controller with expert-defined rules and a Takagi–Sugeno system designed to enable automated learning from ultra-local historical field data. Both systems integrate soil moisture sensing, short-term forecasting, and weather predictions to provide optimized irrigation recommendations. The evaluation combines counterfactual simulations with a bootstrap-based statistical analysis to assess water use efficiency, soil moisture control, and robustness to environmental variability. The comparison highlights distinct strengths of the two approaches, revealing trade-offs between water conservation and crop stress mitigation, and offering practical insights for the design and deployment of intelligent irrigation management solutions. 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

Ensuring operational continuity in modern industrial systems requires maintenance strategies that are both mature and adaptive to uncertainty. This study introduces and validates the Integrated Maintenance Maturity Model (IMMM), a multidimensional framework that combines reliability, safety, resilience, flexibility, and sustainability into a structured maturity assessment approach. Building on the conceptual foundations of maintenance maturity modeling, the IMMM is enhanced with fuzzy logic to address uncertainty, incorporate expert knowledge, and enable nuanced evaluations. A fuzzy inference system based on Mamdani logic was developed to integrate linguistic variables, apply rule-based reasoning, and defuzzify results into maturity scores. The model also includes additional parameters, such as technology adaptability and resource efficiency, to reflect real-world operational complexity. The applicability of the proposed framework was demonstrated through a case study in the automotive sector, where the fuzzy IMMM identified maturity gaps, supported decision-making, and provided strategic recommendations for advancing maintenance practices. Results confirm the model’s effectiveness in enhancing system dependability, adaptability, and sustainability under uncertainty. This work contributes to the development of predictive, uncertainty-aware maintenance maturity models and offers a practical tool for organizations seeking to strengthen operational resilience while aligning with long-term sustainability goals. Full article

In the realm of sustainable public transportation, the integration of intelligent electric bus propulsion systems represents a novel and promising approach to reducing environmental impact—particularly through the mitigation of NOx emissions and overall exhaust pollutants. This emerging technology underscores the growing need for advanced drive control architectures that ensure not only operational safety and reliability but also compliance with increasingly stringent emissions standards. The present article introduces an innovative analysis of energy-optimized dual-drive electric propulsion systems, with a specific focus on their potential for real-world application in emission-conscious urban mobility. A detailed dynamic model of a dual-drive electric bus was developed in MATLAB Simulink, incorporating a Fuzzy Logic-based decision-making algorithm embedded within the Transmission Control Unit (TCU). The proposed control architecture includes a torque-limiting safety strategy designed to prevent motor overspeed conditions, thereby enhancing both efficiency and mechanical integrity. Furthermore, the system architecture enables supervisory override of the Fuzzy Inference System (FIS) during critical scenarios, such as gear-shifting transitions, allowing adaptive control refinement. The study addresses the unique control and coordination challenges inherent in dual-drive systems, particularly in relation to optimizing gear selection for reduced energy consumption and emissions. Key areas of investigation include maximizing efficiency along the motor torque–speed characteristic, maintaining vehicular dynamic stability, and minimizing thermally induced performance degradation. The thermal modeling approach is grounded in integral formulations capturing major loss contributors including copper, iron, and mechanical losses while also evaluating convective heat transfer mechanisms to improve cooling effectiveness. These insights confirm that advanced thermal management is not only vital for performance optimization but also plays a central role in supporting long-term strategies for emission reduction and clean, efficient public transportation. Full article

Asset maintenance management is critical in industries such as petrochemicals and oil and gas (O&G), where complex, interdependent systems heighten failure risks. Maintenance costs represent a significant portion of operational expenditures, emphasizing the need for effective risk-based strategies. A considerable gap exists in integrating uncertainty modelling into both criticality assessment and maintenance planning. Existing approaches often neglect combining expert-driven assessments with optimization models, limiting their applicability in real-world scenarios where cost-effective and risk-informed decision-making is crucial. Maintenance inefficiencies due to suboptimal asset selection result in substantial financial and safety-related consequences in asset-intensive industries. This study presents a framework integrating Reliability-Centered Maintenance (RCM) principles with fuzzy logic and decision-support methodologies to optimise maintenance portfolios for offshore O&G assets, particularly focusing on corrosion management. The framework evaluates asset criticality through comprehensive FMEA, employing MCDM and fuzzy logic to enhance maintenance planning and extend asset lifespan. A case study on offshore asset corrosion management demonstrates the framework’s effectiveness, selecting 60% of highly critical assets for maintenance, compared to 10% by current industry practices. This highlights the potential risk reduction and prevention of critical failures that might otherwise go unnoticed, providing actionable insights for asset integrity managers in the O&G sector. Full article

Early and accurate detection of pneumonia from chest X-ray images is essential for improving treatment and clinical outcomes. Medical imaging datasets often exhibit class imbalance and uncertainty in feature extraction, which complicates conventional classification methods and motivates the use of advanced approaches combining deep learning and fuzzy logic. This study proposes a hybrid approach that combines deep learning architectures (VGG16, EfficientNetV2, MobileNetV2, ResNet50) for feature extraction with fuzzy logic-based classifiers, including Fuzzy C-Means, Fuzzy Decision Tree, Fuzzy KNN, Fuzzy SVM, and ANFIS (Adaptive Neuro-Fuzzy Inference System). Feature selection techniques were also applied to enhance the discriminative power of the extracted features. The best-performing model, ANFIS with MobileNetV2 features and Gaussian membership functions, achieved an overall accuracy of 98.52%, with Normal class precision of 97.07%, recall of 97.48%, and F1-score of 97.27%, and Pneumonia class precision of 99.06%, recall of 98.91%, and F1-score of 98.99%. Among the fuzzy classifiers, Fuzzy SVM and Fuzzy KNN also showed strong performance with accuracy above 96%, while Fuzzy Decision Tree and Fuzzy C-Means achieved moderate results. These findings demonstrate that integrating deep feature extraction with neuro-fuzzy reasoning significantly improves diagnostic accuracy and robustness, providing a reliable tool for clinical decision support. Future research will focus on optimizing model efficiency, interpretability, and real-time applicability. Full article

Nowadays, one of the key areas of sustainable agriculture is increasing animal welfare. However, in the absence of generally accepted measurement criteria and systems, measuring animal welfare can be considered a subjective area that makes measuring animal welfare complex. As a result, both increasing welfare and making intervention decisions are not clear for farm management. In our research, we develop a fuzzy logic-based decision support system that is able to handle the subjectivity arising from determining animal welfare. During focus group interviews, experts pointed out that animal welfare assessment systems do not provide adequate support in decision-making. However, the integration of different benchmarks (past, best values and competitors) and the triangular membership functions assigned to them in the assessment significantly supports decision-making. The models were tested with data collected with the Welfare Quality Assessment System of three dairy farms (Austrian, Hungarian, and Slovak). In our result the models show different assessment results; therefore, an aggregate assessment model was created by aggregating the results of the models. The aggregate model incorporates the value judgments and importance of the different models by applying the Choquet integral, thereby providing a more accurate assessment according to the criteria that meet the expectations of decision-makers. Our research shows that animal welfare assessment systems should be based on fuzzy logic and the application of multi-criteria benchmarks until standards reduce the uncertainty in measuring animal welfare levels. Full article