Search Results (596)

This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R² > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article

This study introduces two hybrid forecasting models that integrate the Marine Predators Algorithm (MPA) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Feed-Forward Neural Networks (FFNN) for short-term Bitcoin price prediction. Daily Bitcoin data from 2022 were converted into supervised time-series structures with multiple input configurations. The proposed hybrid models were evaluated against six well-known metaheuristic algorithms commonly used for training intelligent forecasting systems. The results show that MPA consistently yields lower prediction errors, faster convergence, and more stable optimization behavior compared with alternative algorithms. Both ANFIS-MPA and FFNN-MPA maintained their advantage across all tested structures, demonstrating reliable performance under varying model complexities. All experiments were repeated multiple times, and the hybrid approaches exhibited low variance, indicating robust and reproducible behavior. Overall, the findings highlight the effectiveness of MPA as an optimizer for improving the predictive performance of neuro-fuzzy and neural network models in financial time-series forecasting. Full article

Cellulose nanocrystals (CNCs) have emerged as highly efficient adsorbents for heavy metal removal owing to their biodegradability, wide availability, and rich surface chemistry. Their abundant hydroxyl and other reactive functional groups provide a high density of active sites, significantly enhancing their affinity and adsorption capacity for toxic metal ions such as chromium (VI). The green adsorbent was characterised using FTIR to identify the functional groups. The optimum conditions were pH 6, concentration 140 mg/L, time 120 min, and adsorbent dosage 6 g/L, with a percentage removal of 95%. Deep machine learning was employed to predict the removal capacity of green and biodegradable adsorbents for chromium (VI) removal from wastewater. The findings show that adaptive neuro-fuzzy inference systems effectively model the prediction of Chromium (VI) adsorption. The Levenberg–Marquardt algorithm (LM) was used to train the network through feedforward propagation. In the training dataset, R² was 0.966, Mean Square Error (MSE) 0.042, Absolute average relative error (AARE) 0.053, Root means square error (RMSE) 0.077, and average relative error (ARE) 0.053 for the artificial neural network. The RMSE of 0.021, AARE of 0.015, ARE of 0.01, MSE of 0.017, and R² of 0.998 for the adaptive neuro-fuzzy inference system. These findings confirm the strong adsorption potential of CNCs and the suitability of advanced machine learning models for forecasting heavy metal removal efficiency. Full article

This study presents a comprehensive statewide analysis of pedestrian-involved crashes recorded in Tennessee between 2002 and 2025. We evaluated the influence of roadway, traffic, environmental, and socioeconomic factors on pedestrian crash frequency and severity with substantial components focused on lighting impacts including dark and nighttime. A multi-method analytical framework was implemented, combining descriptive statistics, non-parametric tests, regression analysis, and advanced machine learning techniques including the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the gradient boosting model (XGBoost). Results indicated that dark and nighttime conditions accounted for a disproportionate share of severe crashes—fatal and serious injuries under dark conditions reached over 40%, compared to less than 20% during daylight. The statistical tests revealed statistically significant differences in both total injuries and fatalities between low-speed (≤35 mph) and higher-speed (40–45 mph) corridors. The regression result identified AADT and the number of lanes as the strongest predictors of crash frequency, showing that greater traffic exposure and wider cross-sections substantially elevate pedestrian risk, while terrain and peak-hour traffic exhibited negative associations with severe injuries. The XGBoost model, consisting of 300 trees, achieved R² = 0.857, in which the SHAP analysis revealed that AADT, the roadway functional class, and the number of lanes are the most influential variables. The ANFIS model demonstrated that areas with higher population density and greater proportions of households without vehicles experience more pedestrian crashes. These findings collectively establish how pedestrian crash risks are correlated with traffic exposure, roadway geometry, lighting, and socioeconomic conditions, providing a strong analytical foundation for data-driven safety interventions and policy development. Full article

In modern industrial automation, optimizing the performance of Programmable Logic Controller (PLC)-based PID controllers is critical for ensuring precise process control. This study presents a novel methodology for the dynamic tuning of built-in Proportional-Integral-Derivative (PID) controllers in PLCs using a hybrid algorithm that combines Particle Swarm Optimization (PSO) and Multiple-Adaptive Neuro-Fuzzy Inference System (MANFIS). Classical PID tuning methods, such as Ziegler–Nichols and Cohen–Coon, have traditionally been employed in industrial control systems. However, these methods often struggle to address the complexities of nonlinear, time-varying, or highly dynamic processes, resulting in suboptimal performance and limited adaptability. To overcome these challenges, the proposed PSO-MANFIS hybrid algorithm leverages the global search capabilities of PSO and the adaptive learning abilities of MANFIS to optimize PID parameters in real-time dynamically. Integrating MATLAB (R2021a) with industrial automation systems via an OPC (OLE for Process Control) server utilizes advanced optimization algorithms within MATLAB to obtain the best possible parameters for the industrial PID controller, enhancing control precision and optimizing production efficiency. This MATLAB-PLC interface facilitates seamless communication, enabling real-time monitoring, data analysis, and the implementation of sophisticated computational tools in industrial environments. Experimental results demonstrate superior performance, with reductions in rise time from 93.01 s to 70.98 s, settling time from 165.28 s to 128.84 s, and overshoot eliminated from 0.0012% to 0% of the controller response compared to conventional tuning. Furthermore, the proposed approach achieves a reduction in Root Mean Square Error (RMSE) by approximately 56% to 74% when compared with the baseline performance. By integrating MATLAB’s computational capabilities with PLC-based industrial automation, this study provides a practical and innovative solution for modern industries, offering enhanced adaptability, precision, and reliability in dynamic control applications, ultimately leading to optimized production outcomes. Full article

Voltage instability and power quality degradation represent critical barriers to the reliable operation of modern wind farm-based microgrids. As the share of distributed wind generation continues to grow, fluctuating wind speeds and variable reactive power demands increasingly challenge grid stability. This study proposes an adaptive decentralized framework integrating a Dynamic Distribution Static Compensator (DSTATCOM) with an Artificial Neuro-Fuzzy Inference System (ANFIS)-based control strategy to enhance dynamic voltage and frequency stability in wind farm microgrids. Unlike conventional centralized STATCOM configurations, the proposed system employs parallel wind turbine modules that can be selectively switched based on voltage feedback to maintain optimal grid conditions. Each turbine is connected to a capacitive circuit for real-time voltage monitoring, while the ANFIS controller adaptively adjusts compensation signals to ensure minimal voltage deviation and reduced harmonic distortion. The framework was modeled and validated in the MATLAB/Simulink R2023a environment using the Simscape Power Systems toolbox. Simulation results demonstrated superior transient response, voltage recovery, and power factor correction compared with traditional PI and fuzzy-based controllers, achieving a total harmonic distortion below 2.5% and settling times under 0.5 s. The findings confirm that the proposed decentralized DSTATCOM–ANFIS approach provides an effective, scalable, and cost-efficient solution for maintaining dynamic stability and high power quality in wind farm based microgrids. Full article

Financial forecasting remains essential for supporting strategic decisions and risk oversight in the banking sector. This study examines whether Adaptive Neuro-Fuzzy Inference Systems (ANFISs) can enhance Earnings per Share (EPS) prediction for European banks by integrating four core financial indicators: Return on Assets, Return on Equity, Capital Ratio, and Profit Margin. Using an annual panel of 25 institutions between 2013 and 2023, we benchmark multiple membership function shapes and granularities to identify robust model configurations. The empirical analysis combines chronological holdout testing with Leave-One-Out cross-validation to evaluate accuracy and stability. Findings highlight a sigmoid-based ANFIS specification with four fuzzy sets per input as the most consistent performer, offering interpretable rules that complement conventional forecasting techniques. Full article

Autonomous vehicles (AVs) promise improved safety and sustainability, yet their sophisticated sensing, computing, and communication systems impose auxiliary power loads of 1.5–3.2 kW, risking an increase of up to 45% in global transport energy demand by 2040 if left unaddressed. Existing energy management strategies fail to jointly optimize propulsion and autonomy subsystems under real-world dynamic traffic, treat ADAS loads as static, and lack statistically rigorous validation. This paper proposes a novel Adaptive Neuro-Fuzzy Inference System (ANFIS)-PID framework that integrates (i) 5 s V2X traffic preview, (ii) online PID gain scheduling, and (iii) energy-aware rule pruning for real-time energy allocation. Validated on a real-world trajectory dataset, the approach consistently reduces fuel consumption by up to 4.4% over pure fuzzy logic, 0.05% over FL-RWOA, 1.16% over FL-GWO, and 2.39% over FL-PSO across 25–100 km segments (paired t-test, p ≤ 0.001 on 50 random segments). Additional benefits include 18% faster transient response and 18% lower inference computational load compared to metaheuristic baselines. Scaled to fleet level, the 0.51 L/100 km average saving equates to over CAD 100 million annual savings in Canada. The hybrid neuro-fuzzy architecture offers a deployable, adaptive solution for sustainable autonomous transportation. Full article

Electrolysis and biological processes, such as fermentation and microbial electrolysis cells, offer efficient hydrogen production alongside wastewater treatment. This study presents a novel microbial electrolyzer (ME) comprising a titanium dioxide (TiO₂) anode, a nickel–iron–carbon (NiFeC) cathode, and a cellulose nanocrystal proton exchange membrane (CNC-PEM) designed to generate hydrogen from palm oil mill effluent (POME). The system is powered by a 12 V electric double-layer organic supercapacitor (EDLOSC) integrated with a ZnO/PVA-based solar thin film. Power delivery to the TiO₂-NiFeC-PEM electrolyzer is optimized using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Laboratory-scale pilot tests demonstrated effective degradation of POME’s organic content, achieving a hydrogen yield of approximately 60%. Additionally, the nano-structured ZnO/CuO–ZnO/PVA solar film facilitated stable power supply, enhancing in situ hydrogen production. These results highlight the potential of the EDLOSC-encased ZnO/PVA-powered electrolyzer as a sustainable solution for hydrogen generation and industrial wastewater treatment. Full article

The present study proposes an integrated real-time energy management strategy (EMS) that combines an adaptive neuro-fuzzy inference system (ANFIS) with dynamic programming (DP) to enhance the energy efficiency and system durability of fuel cell electric commercial vehicles (FCECVs). Firstly, a comprehensive DP framework was established to optimize the EMS offline, which simultaneously considers power allocation and automated manual transmission (AMT) gear-shifting to minimize hydrogen consumption (HC). Then, the DP framework was employed to determine optimal power allocation patterns of the FCECVs under various initial state-of-charge (SOC) battery conditions. Based on the DP results, a novel real-time EMS integrating ANFIS with DP solution was developed to formulate an efficient fuzzy inference system (FIS), where the ANFIS model was trained using the particle swarm optimization (PSO) algorithm. The proposed ANFIS-DP EMS was evaluated through extensive simulations under stochastic driving cycles, with performance comparisons against both the DP method and conventional charge-depleting and charge-sustaining (CD-CS) strategies. The experimental results demonstrate that the ANFIS-DP maintains efficient FCS operation across diverse driving conditions while effectively controlling the rate of power change within optimal ranges. Compared to the CD-CS strategy, the proposed method achieves a substantial 14.98% reduction in HC, approaching the performance of DP (only 5.40% higher). Most notably, the ANFIS-DP strategy demonstrates remarkable computational efficiency improvements, outperforming DP by 96.13% and CD-CS by 22.05%. These findings collectively validate the effectiveness of our proposed approach in achieving real-time energy management optimization for FCECVs. Full article

Accurate offshore wind speed forecasting is crucial for ensuring stable energy production and safe offshore operations. However, the strong nonlinearity, non-stationarity, and chaotic behavior of offshore wind speed series make precise prediction extremely difficult. To overcome these difficulties, a two-stage synergistic prediction framework is proposed. In the first stage, a multi-strategy Black-winged Kite Algorithm (MBKA) is designed, incorporating quantum population initialization, improved migration behavior, and oppositional–mutual learning to reinforce global optimization performance under complex coastal conditions. On this basis, an entropy-driven adaptive Variational Mode Decomposition (VMD) method is implemented, where MBKA optimizes decomposition parameters using envelope entropy as the objective function, thereby improving decomposition robustness and mitigating parameter sensitivity. In the second stage, the denoised intrinsic mode functions are used to train an adaptive Neuro-Fuzzy Inference System (ANFIS), whose membership function parameters are optimized by MBKA to enhance nonlinear modeling capability and prediction generalization. Finally, the proposed framework is evaluated using offshore wind speed data from two coastal regions in Shanghai and Fujian, China. Experimental comparisons with multiple state-of-the-art models demonstrate that the MBKA–VMD–ANFIS framework yields notable performance improvements, reducing RMSE by 57.14% and 30.68% for the Fujian and Shanghai datasets, respectively. These results confirm the effectiveness of the proposed method in delivering superior accuracy and robustness for offshore wind speed forecasting. Full article

Amine-based CO₂ absorption is a leading technology for post-combustion carbon capture, but solvent degradation remains a critical barrier to its long-term sustainability. Degradation reduces capture efficiency, increases solvent make-up costs, and generates environmentally harmful by-products, undermining the viability of carbon capture as a sustainable climate mitigation strategy. This study applies advanced machine learning techniques—Artificial Neural Networks (ANN), Random Forest (RF), XGBoost, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)—to predict thermal and oxidative degradation of amine solvents under varying operating conditions. Experimental datasets for piperazine-based mixtures and tertiary amines were used to train and validate predictive models with high statistical accuracy. The results demonstrate that machine learning can reliably forecast degradation behaviour, reducing dependence on resource-intensive experimental campaigns and enabling more sustainable CO₂ capture systems. By improving solvent stability assessment and process monitoring, this work contributes to the development of more resilient, cost-effective, and environmentally responsible carbon capture technologies, directly supporting global sustainability and climate change mitigation goals. Full article

This paper presents a high-performance sensorless control strategy for induction motors using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for rotor speed estimation, eliminating the need for mechanical sensors. The ANFIS approach leverages stator voltages and currents, reducing costs and complexity. The motor is controlled via Indirect Stator Field Orientation Control (ISFOC) with a three-level Neutral–Point–Clamped (NPC) inverter employing Space Vector Modulation (SVM). Symmetry in the motor’s magnetic structure and SVM’s switching patterns enhances control precision, stability, and efficiency while minimizing harmonic distortion. Simulation results validate the proposed ANFIS-based estimator’s superior performance compared to a MRAS-based Luenberger observer under various operating conditions, demonstrating accurate speed tracking and robustness against load disturbances. Full article

This research utilizes a modified cellulose nanocrystal composite as an adsorbent to remove cadmium (II) through a column study. A fixed-bed column was used to remove cadmium (II) at room temperature using varying process factors, such as pH (4–8), bed height (3–9 cm), flow rate (3–7 mL/min), and concentration (10–20 mg/L). According to these findings, cadmium (II) breakthrough occurred more quickly at lower bed heights, higher flow rates, and higher cadmium (II) concentrations. The Thomas model is the most appropriate kinetic model. Deep learning models, such as the adaptive neuro-fuzzy inference model with two algorithms (backpropagation and least squares estimation), were effectively used to model the effectiveness of cadmium (II) removal in aqueous solutions via modified cellulose nanocrystals. To compare the model’s predicted results with experimental data, statistical approaches were employed, including calculating the coefficient of determination (R²) and mean square error (MSE). The ANFIS model used to predict cadmium (II) adsorption via modified cellulose nanocrystals had a strong correlation value of 0.997 for least squares estimation (LSE) and 0.999 for the gradient descent (backpropagation) method, indicating the effectiveness of the trained model in predicting the cadmium (II) adsorption process. Full article

Cultural heritage conservation faces escalating challenges from environmental threats and resource constraints, necessitating innovative preservation strategies that balance predictive accuracy with interpretability. This study presents a hybrid neuro-fuzzy framework addressing critical gaps in heritage conservation practice through sequential integration of feedforward neural networks (FF-NNs) and Mamdani-type fuzzy inference systems (MFISs). The system processes multi-sensor data (temperature, vibration, pressure) through a two-stage architecture: an FF-NN for pattern recognition and an MFIS for interpretable decision-making. Evaluation on 1000 synthetic heritage building monitoring samples (70% training, 30% testing) demonstrates mean accuracy of 94.3% (±0.62%), precision of 92.3% (±0.78%), and recall of 90.3% (±0.70%) across five independent runs. Feature importance analysis reveals temperature as the dominant fault detection driver (60.6% variance contribution), followed by pressure (36.7%), while vibration contributes negatively (−2.8%). The hybrid architecture overcomes the accuracy–interpretability trade-off inherent in standalone approaches: while the FF-NN achieves superior fault detection, the MFIS provides transparent maintenance recommendations essential for conservation professional validation. However, comparative analysis reveals that rigid fuzzy rule structures constrain detection capabilities for borderline cases, reducing recall from 96% (standalone FF-NN) to 47% (hybrid system) in fault-dominant scenarios. This limitation highlights the need for adaptive fuzzy integration mechanisms in safety-critical heritage applications. Full article