Search Results (142)

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

Rotary kiln-based activated carbon production combines high-temperature operation with flammable/reducing gases, carbonaceous dust, and downstream off-gas treatment and acid/base washing, creating complex escalation pathways. This study prioritizes safety improvements by applying classical failure modes and effects analysis (FMEA) and a transparent Fuzzy-FMEA framework to 18 representative failure modes (six each for kiln/activation, acid/base handling, and atmosphere/control). Five experts evaluated Severity, Occurrence, and Detection on a 10-point scale. The fuzzy model used triangular membership functions (L/M/H), a monotonic 27-rule base, Mamdani max–min inference, and centroid defuzzification to compute a continuous fuzzy risk priority number (FRPN, 0–10). Classical FMEA identified dust explosion (RPN = 405), temperature control failure (RPN = 378), and off-gas leakage (RPN = 324) as the highest-ranked risks. Fuzzy-FMEA preserved the top-risk group while more strongly highlighting barrier-related risks, placing off-gas leakage, instrumentation/interlock failure, and electrostatic ignition control alongside dust explosion (FRPN 9.221–9.332). The rankings were strongly correlated (Spearman ρ = 0.871; Kendall τ = 0.752), yet mid-risk items were rearranged (mean |Δrank| = 2.06; max = 5), improving discrimination within tied RPN clusters. The five highest-priority scenarios were reconstructed into actionable engineering packages, including dust and ignition control, off-gas integrity linked to shutdown logic, interlock proof testing and bypass management, and independent protection layers for kiln temperature control. Full article

Background and Objectives: Psychosocial symptoms and oral behaviors can complicate routine dental care, yet available screeners yield multiple separate scores. Explainable artificial intelligence offers a pragmatic way to integrate such multidomain measures into a single, auditable output that can support screening-oriented stratification and standardized documentation (non-diagnostic). Therefore, we aimed to develop an interpretable, deterministic Mamdani fuzzy inference system (FIS) integrating GAD-7, PHQ-9, and OBC-21 into a 0–10 psychobehavioral composite score (PCS) to support screening-oriented stratification and standardized documentation (non-diagnostic). Materials and Methods: Cross-sectional multicenter study in 18 private dental clinics in Romania (October 2024–March 2025; n = 460). A rule-based Mamdani Type-1 FIS was specified a priori (48 rules; triangular membership functions; centroid defuzzification) without supervised training. Internal evaluation assessed coherence across severity strata, robustness to predefined input perturbations (±1 point; ±5%) and membership-function variation (±10%), and benchmarking against linear composites (Z-mean; PCA PC1). Results: Median PCS was 2.30 (IQR 2.03–3.56). PCS correlated with GAD-7 (Spearman ρ = 0.886), PHQ-9 (ρ = 0.792), and OBC-21 (ρ = 0.687) (all p < 0.001), increased monotonically across anxiety and depression severity strata, and was higher in high OBC-21 risk. Robustness was excellent under input perturbations (ICC(3,1) = 0.983 for ±1 point; 0.992 for ±5%) and high under ±10% membership-function variation (ICC(3,1) = 0.959). Concordance with linear baselines was high (Spearman ρ = 0.956 for Z-mean; 0.955 for PCA PC1), with a small systematic nonlinearity at higher scores. Conclusions: PCS provides a fully auditable, rule-based integration of three patient-reported measures with coherent internal behavior and robustness to plausible measurement noise and specification changes. This study reports internal evaluation of a deterministic, rule-based aggregation; external clinical validation against independent outcomes is required before any clinical utility claims. 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

Choosing sites for solar photovoltaic (PV) power plants in developing countries like India is a crucial task while considering multiple conflicting factors and sub-factors simultaneously. Multi-criteria decision-making (MCDM) is an optimisation method that provides a framework for handling such situations in an intuitionistic fuzzy environment. The complexity and uncertainty associated with the site selection model are dealt with professionally. The Criteria Importance Through Intercriteria Correlation (CRITIC) method is applied to determine the relative importance of the criteria, identifying airflow speed as the most influential factor, followed by humidity ratio, level of dust haze, availability of labour and resources, and ecological effects. This shows that airflow speed plays an important role in the power plant’s efficiency and performance. The Vlse Kriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method is then used to prioritise the alternatives as potential locations for setting up a solar PV power plant in India. A new de-i-fuzzification method based on the relative difference between two real numbers is also proposed. Sensitivity analyses and comparative studies are conducted to assess the robustness and effectiveness of the framework. Overall, the results demonstrate that the proposed framework is useful and effective for optimising site selection for solar power plants in India. Full article

The sustainable transformation of electronics supply chains (ESCs) increasingly relies on effective green supply chain planning under carbon pricing and demand uncertainty. However, prior studies often lack an integrated framework that jointly considers carbon taxation, green technology investment, and profitability—environment trade-offs in forward and reverse supply chains. To address this gap, this study proposes a fuzzy multi-goal optimization model using linear goal programming under progressive carbon taxation. The model incorporates fuzzy demand (triangular fuzzy numbers), carbon emissions, carbon taxes, and green investment costs and is converted into a solvable linear form via a defuzzification-based procedure to simultaneously achieve multiple aspiration levels for economic and environmental objectives. A real-world ESC case validates the model. The results show that carbon taxation and green investments can reduce emissions while maintaining profitability, with total cost and emission sensitivity of ±10–20% across different policies and demand uncertainty settings. The findings support adaptive, policy-aware planning by guiding green investment intensity and forward–reverse logistics decisions to balance cost efficiency and emissions reduction and provide actionable insights for managers facing progressive carbon pricing regulations. Full article

We developed a hybrid system combining deep learning-based recognition with fuzzy inference to enhance the detection, recognition, and identification of maritime targets. In the system, deep learning provides strong feature extraction, while fuzzy logic mitigates uncertainty in low-visibility or occluded conditions. The system uses confidence score, screen ratio, and estimated distance as input and processes them through fuzzy inference with triangular membership functions and center of area defuzzification. This integration improves decision robustness and suppresses input noise. Experimental results demonstrate enhanced stability and reduced misjudgment in dynamic maritime environments, highlighting the applicability of a hybrid deep learning–fuzzy inference systems to intelligent ships and unmanned maritime vehicle sensing tasks. Full article

The selection of a final operating point from a Pareto front set of marine diesel engine configurations relies on the critical task of translating operator priorities into quantitative criterion weights. This study isolates this pivotal weighting step and introduces an operator-defined fuzzy weighting module that maps linguistic importance ratings to normalized weights. This module systematically maps important ratings for Specific Fuel Consumption (SFC), Nitrogen Oxides (NOx), and Particulate Matter (PM) into a set of normalized weights for the Multi-Criteria Decision-Making method. The module’s core is a Mamdani-type fuzzy logic module that utilizes triangular membership functions and centroid defuzzification. These fuzzy weights are integrated with the TriMetric Fusion algorithm to generate a robust consensus ranking. Validation on a Pareto front from a two-stroke diesel engine demonstrates the framework’s efficacy: a Fuel-Economy priority selected a configuration with SFC advantage, while a Strict Environmental Compliance priority correctly identified dual emissions strengths. Furthermore, the system effectively mediated trade-offs in a high-competition scenario. Rank correlation analysis confirmed that while the Pareto front nature of the alternatives leads to inherent similarities in rankings, the fuzzy weights induce significant and logical divergences. Future work will focus on validation with real operator feedback and comparative studies with traditional weighting methods. Full article

The integration of large language models (LLMs) and fuzzy metrics offers new possibilities for improving automated grading in programming education. While LLMs enable efficient generation and semantic evaluation of programming assignments, traditional crisp grading schemes fail to adequately capture partial correctness and uncertainty. This paper proposes a grading framework in which LLMs assess student solutions according to predefined criteria and output fuzzy grades represented by trapezoidal membership functions. Defuzzification is performed using the centroid method, after which fuzzy distance measures and fuzzy C-means clustering are applied to correct grades based on cluster centroids corresponding to linguistic performance levels (poor, good, excellent). The approach is evaluated on several years of real course data from an introductory programming course with approximately 800 students per year called “Programski jezici i strukture podataka” in the first year of studies of multiple study programs at the Faculty of Technical Sciences, University of Novi Sad, Serbia. Experimental results show that direct fuzzy grading tends to be overly strict compared to human grading, while fuzzy metric correction significantly reduces grading deviation and improves alignment with human assessment, particularly for higher-performing students. Combining LLM-based semantic analysis with fuzzy metrics yields a more nuanced, interpretable, and adaptable grading process, with potential applicability across a wide range of educational assessment scenarios. Full article

This study develops a fuzzy linear multi-objective programming (FLMOP) model to optimize Taiwan’s online food delivery (OFD) systems by jointly considering time, cost, quality, and carbon emissions (TCQCE) under strict Hazard Analysis and Critical Control Point (HACCP) safety constraints. By integrating fuzzy set theory with triangular fuzzy numbers (TFN) and employing centroid defuzzification, this model effectively addresses uncertainties in delivery time, cost, and quality. Empirical results demonstrate that controlled delivery-time extension and order batching reduce carbon emissions by 20%, maintain food quality at 89.3%, and lower delivery costs by 15% under large-scale operations. Statistical validation (p = 0.002) and sensitivity analysis confirm robustness and low variability. Comparative benchmarking highlights FLMOP’s superiority over mixed-integer linear programming (MILP) and genetic algorithms/non-dominated sorting genetic algorithm II (GA/NSGA-II), achieving higher hypervolume (0.904 vs. 0.836 and 0.743) and near-optimal solutions within 11 s, making it suitable for real-time decision-making. This study establishes a benchmark for sustainable last-mile OFD and offers practical guidelines for Taiwan’s OFD platforms. Full article

The research is centered on how India’s top-tier IT companies—the “Big Six” of TCS, Infosys, HCLTech, Wipro, Cognizant, and Tech Mahindra—are integrating sustainability in their digitally driven operations, platforms, and business models. The study employs a mixed methodology, combining critical case study analysis with Fuzzy Delphi validation to assess triangular fuzzy numbers, centroid-based defuzzification, and consensus thresholds. The study explores how AI, big data, analytics, and digital marketing influence environmentally sustainable consumption behaviors within global ecosystems. Results show that, despite limited consumer control, these companies shape sustainability-related behavior indirectly through backend systems, digital platforms, and algorithmic logic—known as “invisible architecture”. This study confirms six main sustainability factors through expert consensus. Noteworthy among those are Digital Infrastructure for Sustainability, Platform Logic for Behavioral Change, and AI-Enabled Analytics and Recommendations. Thematic cross-case results reveal both the promise and ethical challenges of digital sustainability, including the prevalence of greenwashing and risks of overconsumption. Full article

This paper presents a practical and auditable methodology for prioritizing drinking water hazards based on fuzzy preference relations (FPR). The method is based on additive pairwise comparisons of tap water quality parameters, which are aggregated (median) into a complete preference matrix. For each parameter, a Fuzzy Priority Index (FPI) was determined as the average “advantage” over the others. The FPI values were mapped to five fuzzy priority levels (very low–very high) using triangular/trapezoidal membership functions, followed by a defuzzification process using the centroid of singletons (COGS) method. The final step is to map the categories to operational actions, ensuring a clear transition from assessment to decision (from routine monitoring to immediate intervention). The method was demonstrated on nine parameters that are relevant for regulatory (WHO/DWD) and operational purposes: As, Pb, THM, NO₃⁻, Hg, Cr, Mn, Cu, Fe. Thirty-six pairwise assessments were determined, which, after aggregation, formed fuzzy relations. The resulting ranking (FPI) is: As (0.76) > Pb (0.70) > THM (0.64) > NO₃⁻ (0.56) > Hg (0.50) > Cr (0.43) > Mn (0.36) > Cu (0.30) > Fe (0.25). Fuzzy categorization assigned As, Pb, THM to the High level, NO₃⁻, Hg, Cr to Medium, and Mn, Cu, Fe to Low, with the Score reflecting the “proximity” of higher levels. The approach is transparent, replicable, and supports sensitivity analysis. The combination of FPI with fuzzy categorization and a decision map transforms expert knowledge and uncertainty into prioritized, actionable steps for water safety management. Full article

This study introduces a Fuzzy–AHP–based analytical model for the quantitative assessment of authenticity loss in adaptive reuse practices, addressing a persistent gap in heritage research—the lack of reproducible mathematical frameworks capable of linking authenticity evaluation with sustainability indicators. Unlike previous studies that approach authenticity conceptually or qualitatively, this research develops a hybrid decision-support system that translates both intangible and tangible heritage attributes into measurable linguistic variables, enabling systematic and comparable authenticity assessments. The model was applied to ten traditional houses in Alanya, Türkiye, representing different adaptive reuse types (residential, cultural, commercial, and touristic). A total of 17 experts contributed to the Analytic Hierarchy Process (AHP) weighting stage, producing a Consistency Ratio of 0.0156 (<0.10), and 8 experts provided scoring inputs for the fuzzy system. The fuzzy inference system was implemented in MATLAB R2023a, incorporating seven main criteria and three subcriteria, nine input variables, five linguistic categories, and a rule base of 3400 fuzzy rules. Membership functions were defined within the 0–100 numerical range, and the centroid defuzzification method was used to compute final authenticity values. Model reliability was confirmed through Kendall’s W = 0.87, demonstrating strong inter-rater agreement. Results show that buildings retaining their original residential function achieved the highest authenticity scores (Final Score ≈ 86), while structures converted into boutique hotels or restaurants exhibited substantial authenticity losses (Final Score range: 25–45), especially within Group 2 criteria (environment, function, spirit, and intangible cultural heritage). This divergence illustrates a sustainability paradox: although adaptive reuse prolongs building life cycles and reduces embodied carbon, it may simultaneously undermine cultural sustainability when authenticity is significantly compromised. The proposed Fuzzy–AHP authenticity model provides a replicable, transparent, and empirically validated tool for evaluating the effects of functional transformation within a sustainability framework. By quantifying the relationship between adaptive reuse types and authenticity retention, the study contributes to sustainable heritage management research and supports the implementation of SDG 11—Sustainable Cities and Communities. Full article

One of the sources of risk inherent to construction projects is the quality of spatial data. Damage to buried pipes and cables often causes accidents, delays, or stoppages of construction works. Fuzzy logic is a method for studying the risk. It is employed to describe complex or poorly defined phenomena that can hardly be characterised with probabilistic methods. The article proposes a method for assessing the risk of damaging underground utilities based on a fuzzy inference engine. The author first defined linguistic variables and assigned them values based on risk factors. The membership functions for the linguistic variables were modelled using expert judgement. Then, the author determined qualitative fuzzy sets with the rule base. Finally, the values were converted into crisp values. The defuzzification technique employed was the centre of gravity. The proposed method can assess the risk of damage to underground utilities for spatial data exhibiting diverse quality classes. It will be employed to generate large-scale risk maps. The proposed fuzzy logic solution is an effective and appropriate tool for assessing the risk of damage to underground utilities arising from the quality of subsurface data. It should not be regarded as a universal substitute for PRA (Probabilistic Risk Assessment) but as a complementary methodology that is particularly well-suited to risk assessment in data-poor environments characterised by epistemic uncertainty and reliance on qualitative expert judgement. Full article

This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications. Full article