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Search Results (188)

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Keywords = adaptive network-based fuzzy inference systems (ANFIS)

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23 pages, 5026 KB  
Article
Vibration Control of Passenger Aircraft Active Landing Gear Using Neural Network-Based Fuzzy Inference System
by Aslı Durmuşoğlu and Şahin Yıldırım
Appl. Sci. 2025, 15(19), 10855; https://doi.org/10.3390/app151910855 - 9 Oct 2025
Viewed by 298
Abstract
Runway surface roughness is recognized as a principal cause of passenger aircraft vibration during taxiing, adversely affecting ride comfort, safety, and even human health. Effective mitigation of such vibrations is therefore essential for improving passenger experience and operational reliability. Previous studies have investigated [...] Read more.
Runway surface roughness is recognized as a principal cause of passenger aircraft vibration during taxiing, adversely affecting ride comfort, safety, and even human health. Effective mitigation of such vibrations is therefore essential for improving passenger experience and operational reliability. Previous studies have investigated passive, semi-active, and intelligent controllers such as PID, H∞, and ANFIS; however, the comprehensive application of a robust adaptive neuro-fuzzy inference system (RANFIS) to active landing-gear control has not yet been addressed. The novelty of this work lies in combining robustness with adaptive learning of fuzzy rules and neural network parameters, thereby filling this critical gap in the literature. To investigate this, a six-degrees-of-freedom aircraft dynamic model was developed, and three controllers were comparatively evaluated: model-based neural network (MBNN), adaptive neuro-fuzzy inference system (ANFIS), and the proposed RANFIS. Performance was assessed in terms of rise time, settling time, peak value, and steady-state error under stochastic runway excitations. Simulation results show that while MBNN and ANFIS provide satisfactory control, RANFIS achieved superior performance, reducing vibration peaks to ≤0.3–1.0 cm, shortening settling times to <1.5 s, and decreasing steady-state errors to <0.05 cm. These findings confirm that RANFIS offers a more effective solution for enhancing comfort, safety, and structural durability in next-generation active landing-gear systems. Full article
(This article belongs to the Special Issue Vibration Analysis of Nonlinear Mechanical Systems)
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22 pages, 4398 KB  
Article
Abrasive Waterjet Machining of r-GO Infused Mg Fiber Metal Laminates: ANFIS Modelling and Optimization Through Antlion Optimizer Algorithm
by Devaraj Rajamani, Mahalingam Siva Kumar and Arulvalavan Tamilarasan
Materials 2025, 18(19), 4480; https://doi.org/10.3390/ma18194480 - 25 Sep 2025
Viewed by 291
Abstract
This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content [...] Read more.
This research proposes an intelligent modeling and optimization strategy for abrasive waterjet machining (AWJM) of magnesium-based fiber metal laminates (FMLs) reinforced with reduced graphene oxide (r-GO). Experiments were designed using the Box–Behnken method, considering waterjet pressure, stand-off distance, traverse speed, and r-GO content as inputs, while kerf taper (Kt), surface roughness (Ra), and material removal rate (MRR) were evaluated as outputs. Adaptive Neuro-Fuzzy Inference System (ANFIS) models were developed for each response, with their critical optimized hyperparameters such as cluster radius, quash factor, and training data split through the dragonfly optimization (DFO) algorithm. The optimized ANFIS networks yielded a high predictive accuracy, with low RMSE and MAPE values and close agreement between predicted and measured results. Four metaheuristic algorithms including particle swarm optimization (PSO), salp swarm optimization (SSO), whale optimization algorithm (WOA), and the antlion optimizer (ALO) were applied for simultaneous optimization, using a TOPSIS-based single-objective formulation. ALO outperformed the others, identifying 325 MPa waterjet pressure, 2.5 mm stand-off, 800 mm/min traverse speed, and 0.00602 wt% r-GO addition in FMLs as optimal conditions. These settings produced a kerf taper of 2.595°, surface roughness of 8.9897 µm, and material removal rate of 138.13 g/min. The proposed ANFIS-ALO framework demonstrates strong potential for achieving precision and productivity in AWJM of hybrid laminates. Full article
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33 pages, 4143 KB  
Article
An Approach for Sustainable Supplier Segmentation Using Adaptive Network-Based Fuzzy Inference Systems
by Ricardo Antonio Saugo, Francisco Rodrigues Lima Junior, Luiz Cesar Ribeiro Carpinetti, Ana Paula Duarte and Jurandir Peinado
Mathematics 2025, 13(19), 3058; https://doi.org/10.3390/math13193058 - 23 Sep 2025
Viewed by 346
Abstract
Due to the globalization of supply chains and the resulting increase in the quantity and diversity of suppliers, the segmentation of suppliers has become fundamental to improving relationship management and supplier performance. Moreover, given the need to incorporate sustainability into supply chain management, [...] Read more.
Due to the globalization of supply chains and the resulting increase in the quantity and diversity of suppliers, the segmentation of suppliers has become fundamental to improving relationship management and supplier performance. Moreover, given the need to incorporate sustainability into supply chain management, criteria based on economic, environmental, and social performance have been adopted for evaluating suppliers. However, few studies present sustainable supplier segmentation models in the literature, and none of them make it possible to predict individual supplier performance for each TBL dimension in a non-compensatory manner. Moreover, none of them permits the use of historical performance data to adapt the model to the usage environment. Given this, this study aims to propose a decision-making model for sustainable supplier segmentation using an adaptive network-based fuzzy inference system (ANFIS). Our approach combines three ANFIS computational models in a tridimensional quadratic matrix, based on diverse criteria associated with the triple bottom line (TBL) dimensions. A pilot application of this model in a sugarcane mill was performed. We implemented 108 candidate topologies using the Neuro-Fuzzy Designer of the MATLAB® software (R2014a). The cross-validation method was applied to select the best topologies. The accuracy of the selected topologies was confirmed using statistical tests. The proposed model can be adopted for supplier segmentation processes in companies that wish to monitor and/or improve the sustainability performance of their suppliers. This study may also be helpful to researchers in developing computational solutions for segmenting suppliers, mainly regarding ANFIS modeling and providing appropriate topological parameters to obtain accurate results. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks, 2nd Edition)
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43 pages, 2944 KB  
Article
A Novel Approach to SPAM Detection in Social Networks-Light-ANFIS: Integrating Gradient-Based One-Sided Sampling and Random Forest-Based Feature Clustering Techniques with Adaptive Neuro-Fuzzy Inference Systems
by Oğuzhan Çıtlak, İsmail Atacak and İbrahim Alper Doğru
Appl. Sci. 2025, 15(18), 10049; https://doi.org/10.3390/app151810049 - 14 Sep 2025
Viewed by 681
Abstract
With today’s technological advancements and the widespread use of the Internet, social networking platforms that allow users to interact with each other are increasing rapidly. The popular social network X (formerly Twitter) has become a target for malicious actors, and spam is one [...] Read more.
With today’s technological advancements and the widespread use of the Internet, social networking platforms that allow users to interact with each other are increasing rapidly. The popular social network X (formerly Twitter) has become a target for malicious actors, and spam is one of its biggest challenges. The filters employed by such platforms to protect users struggle to keep up with evolving spam techniques, the diverse behaviors of platform users, the dynamic tactics of spam accounts, and the need for updates in spam detection algorithms. The literature shows that many effective solutions rely on computationally expensive methods that are limited by dataset constraints. This study addresses the spam challenges of social networks by proposing a novel detection framework, Light-ANFIS, which combines ANFIS with gradient-based one-side sampling (GOSS) and random forest-based clustering (RFBFC) techniques. The proposed approach employs the RFBFC technique to achieve efficient feature reduction, yielding an ANFIS model with reduced input requirements. This optimized ANFIS structure enables a simpler system configuration by minimizing parameter usage. In this context, dimensionality reduction enables a faster ANFIS training. The GOSS technique further accelerates ANFIS training by reducing the sample size without sacrificing accuracy. The proposed Light-ANFIS architecture was evaluated using three datasets: two public benchmarks and one custom dataset. To demonstrate the impact of GOSS, its performance was benchmarked against that of RFBFC-ANFIS, which relies solely on RFBFC. Experiments comparing the training durations of the Light-ANFIS and RFBFC-ANFIS architectures revealed that the GOSS technique improved the training time efficiency by 38.77% (Dataset 1), 40.86% (Dataset 2), and 38.79% (Dataset 3). The Light-ANFIS architecture has also achieved successful results in terms of accuracy, precision, recall, F1-score, and AUC performance metrics. The proposed architecture has obtained scores of 0.98748, 0.98821, 0.99091, 0.98956, and 0.98664 in Dataset 1; 0.98225, 0.97412, 0.99043, 0.98221, and 0.98233 in Dataset 2; and 0.98552, 0.98915, 0.98720, 0.98818, and 0.98503 in Dataset 3 for these performance metrics, respectively. The Light-ANFIS architecture has been observed to demonstrate performance above existing methods when compared with methods in studies using similar datasets and methodologies based on the literature. Even in Dataset 1 and Dataset 3, it achieved a slightly better performance in terms of confusion matrix metrics compared to current deep learning (DL)-based hybrid and fusion methods, which are known as high-performance complex models in this field. Additionally, the proposed model not only exhibits high performance but also features a simpler configuration than structurally equivalent models, providing it with a competitive edge. This makes it a valuable for safeguarding social media users from harmful content. Full article
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29 pages, 4733 KB  
Article
Water Quality Index (WQI) Forecasting and Analysis Based on Neuro-Fuzzy and Statistical Methods
by Amar Lokman, Wan Zakiah Wan Ismail, Nor Azlina Ab Aziz and Anith Khairunnisa Ghazali
Appl. Sci. 2025, 15(17), 9364; https://doi.org/10.3390/app15179364 - 26 Aug 2025
Viewed by 958
Abstract
Water quality is crucial to the economy and ecology because a healthy aquatic eco-system supports human survival and biodiversity. We have developed the Neuro-Adapt Fuzzy Strategist (NAFS) to improve water quality index (WQI) forecasting accuracy. The objective of the developed model is to [...] Read more.
Water quality is crucial to the economy and ecology because a healthy aquatic eco-system supports human survival and biodiversity. We have developed the Neuro-Adapt Fuzzy Strategist (NAFS) to improve water quality index (WQI) forecasting accuracy. The objective of the developed model is to achieve a balance by improving prediction accuracy while preserving high interpretability and computational efficiency. Neural networks and fuzzy logic improve the NAFS model’s flexibility and prediction accuracy, while its optimized backward pass improves training convergence speed and parameter update effectiveness, contributing to better learning performance. The normalized and partial derivative computations are refined to improve the model. NAFS is compared with ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS), and current machine learning (ML) models such as LSTM, GRU, and Transformer based on performance evaluation metrics. NAFS outperforms ANFIS and ANN, with MSE of 1.678. NAFS predicts water quality better than ANFIS and ANN, with RMSE of 1.295. NAFS captures complicated water quality parameter interdependencies better than ANN and ANFIS using principal component analysis (PCA) and Pearson correlation. The performance comparison shows that NAFS outperforms all baseline models with the lowest MAE, MSE, RMSE and MAPE, and the highest R2, confirming its superior accuracy. PCA is employed to reduce data dimensionality and identify the most influential water quality parameters. It reveals that two principal components account for 72% of the total variance, highlighting key contributors to WQI and supporting feature prioritization in the NAFS model. The Breusch–Pagan test reveals heteroscedasticity in residuals, justifying the use of non-linear models over linear methods. The Shapiro–Wilk test indicates non-normality in residuals. This shows that the NAFS model can handle complex, non-linear environmental variables better than previous water quality prediction research. NAFS not only can predict water quality index values but also enhance WQI estimation. Full article
(This article belongs to the Special Issue AI in Wastewater Treatment)
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32 pages, 7668 KB  
Article
Hybrid CNN-Fuzzy Approach for Automatic Identification of Ventricular Fibrillation and Tachycardia
by Azeddine Mjahad and Alfredo Rosado-Muñoz
Appl. Sci. 2025, 15(17), 9289; https://doi.org/10.3390/app15179289 - 24 Aug 2025
Cited by 1 | Viewed by 630
Abstract
Ventricular arrhythmias such as ventricular fibrillation (VF) and ventricular tachycardia (VT) are among the leading causes of sudden cardiac death worldwide, making their timely and accurate detection a critical task in modern cardiology. This study presents an advanced framework for the automatic detection [...] Read more.
Ventricular arrhythmias such as ventricular fibrillation (VF) and ventricular tachycardia (VT) are among the leading causes of sudden cardiac death worldwide, making their timely and accurate detection a critical task in modern cardiology. This study presents an advanced framework for the automatic detection of critical cardiac arrhythmias—specifically ventricular fibrillation (VF) and ventricular tachycardia (VT)—by integrating deep learning techniques with neuro-fuzzy systems. Electrocardiogram (ECG) signals from the MIT-BIH and AHA databases were preprocessed through denoising, alignment, and segmentation. Convolutional neural networks (CNNs) were employed for deep feature extraction, and the resulting features were used as input for various fuzzy classifiers, including Fuzzy ARTMAP and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Among these classifiers, ANFIS demonstrated the best overall performance. The combination of CNN-based feature extraction with ANFIS yielded the highest classification accuracy across multiple cardiac rhythm types. The classification performance metrics for each rhythm type were as follows: for Normal Sinus Rhythm, precision was 99.09%, sensitivity 98.70%, specificity 98.89%, and F1-score 98.89%. For VF, precision was 95.49%, sensitivity 96.69%, specificity 99.10%, and F1-score 96.09%. For VT, precision was 94.03%, sensitivity 94.26%, specificity 99.54%, and F1-score 94.14%. Finally, for Other Rhythms, precision was 97.74%, sensitivity 97.74%, specificity 99.40%, and F1-score 97.74%. These results demonstrate the strong generalization capability and precision of the proposed architecture, suggesting its potential applicability in real-time biomedical systems such as Automated External Defibrillators (AEDs), Implantable Cardioverter Defibrillators (ICDs), and advanced cardiac monitoring technologies. Full article
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28 pages, 2541 KB  
Article
Photovoltaic Farm Power Generation Forecast Using Photovoltaic Battery Model with Machine Learning Capabilities
by Agboola Benjamin Alao, Olatunji Matthew Adeyanju, Manohar Chamana, Stephen Bayne and Argenis Bilbao
Solar 2025, 5(2), 26; https://doi.org/10.3390/solar5020026 - 6 Jun 2025
Cited by 1 | Viewed by 878
Abstract
This study presents a machine learning-based photovoltaic (PV) model for energy management and planning in a microgrid with a battery system. Microgrids integrating PV face challenges such as solar irradiance variability, temperature fluctuations, and intermittent generation, which impact grid stability and battery storage [...] Read more.
This study presents a machine learning-based photovoltaic (PV) model for energy management and planning in a microgrid with a battery system. Microgrids integrating PV face challenges such as solar irradiance variability, temperature fluctuations, and intermittent generation, which impact grid stability and battery storage efficiency. Existing models often lack predictive accuracy, computational efficiency, and adaptability to changing environmental conditions. To address these limitations, the proposed model integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a multi-input multi-output (MIMO) prediction algorithm, utilizing historical temperature and irradiance data for accurate and efficient forecasting. Simulation results demonstrate high prediction accuracies of 95.10% for temperature and 98.06% for irradiance on dataset-1, significantly reducing computational demands and outperforming conventional prediction techniques. The model further uses ANFIS outputs to estimate PV generation and optimize battery state of charge (SoC), achieving a consistent minimal SoC reduction of about 0.88% (from 80% to 79.12%) over four different battery types over a seven-day charge–discharge cycle, providing up to 11 h of battery autonomy under specified load conditions. Further validation with four other distinct datasets confirms the ANFIS network’s robustness and superior ability to handle complex data variations with consistent accuracy, making it a valuable tool for improving microgrid stability, energy storage utilization, and overall system reliability. Overall, ANFIS outperforms other models (like curve fittings, ANN, Stacked-LSTM, RF, XGBoost, GBoostM, Ensemble, LGBoost, CatBoost, CNN-LSTM, and MOSMA-SVM) with an average accuracy of 98.65%, and a 0.45 RMSE value on temperature predictions, while maintaining 98.18% accuracy, and a 31.98 RMSE value on irradiance predictions across all five datasets. The lowest average computational time of 17.99s was achieved with the ANFIS model across all the datasets compared to other models. Full article
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21 pages, 1493 KB  
Article
An Assistive System for Thermal Power Plant Management
by Aleksa Stojic, Goran Kvascev and Zeljko Djurovic
Energies 2025, 18(11), 2977; https://doi.org/10.3390/en18112977 - 5 Jun 2025
Viewed by 597
Abstract
The estimation of available active power in coal-fired thermal power plant units involves considerable complexity and remains a critical task for plant operators. To avoid compromising system stability, operators often operate the thermal unit below its full capacity. To address this issue, the [...] Read more.
The estimation of available active power in coal-fired thermal power plant units involves considerable complexity and remains a critical task for plant operators. To avoid compromising system stability, operators often operate the thermal unit below its full capacity. To address this issue, the aim of this paper is to facilitate the process of estimating the maximum active electrical power by applying an assistive system based on ANFIS (Adaptive Neuro-Fuzzy Inference System), a method that combines the strengths of neural networks and fuzzy logic. Since the generated electric energy is directly linked to the amount of thermal energy produced, the analysis is focused on the boiler combustion process. It has been shown that the key factors in this process are the coal mills and their achievable capacity, as well as the calorific value of coal. Therefore, the proposed assistive system is based on the estimation of the available capacity of each active mill, which is then combined with the estimated calorific value of the coal to determine the achievable active electrical power of the unit. The conducted analysis and experiments confirm the validity of this approach. Full article
(This article belongs to the Special Issue Energy, Electrical and Power Engineering: 4th Edition)
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24 pages, 3894 KB  
Article
Fault Detection in Gearboxes Using Fisher Criterion and Adaptive Neuro-Fuzzy Inference
by Houssem Habbouche, Tarak Benkedjouh, Yassine Amirat and Mohamed Benbouzid
Machines 2025, 13(6), 447; https://doi.org/10.3390/machines13060447 - 23 May 2025
Cited by 2 | Viewed by 488
Abstract
Gearboxes are autonomous devices essential for power transmission in various mechanical systems. When a failure occurs, it can lead to an inability to perform the required functions, potentially resulting in a complete shutdown of the mechanism and causing significant operational disruptions. Consequently, deploying [...] Read more.
Gearboxes are autonomous devices essential for power transmission in various mechanical systems. When a failure occurs, it can lead to an inability to perform the required functions, potentially resulting in a complete shutdown of the mechanism and causing significant operational disruptions. Consequently, deploying expert methods for fault detection and diagnosis is crucial to ensuring the reliability and efficiency of these systems. Artificial intelligence (AI) techniques show promise for fault diagnosis, but their accuracy can be hindered by noise and manufacturing imperfections that distort mechanical signatures. Thorough data analysis and preprocessing are vital to preserving these critical features. Validating approaches through numerical simulations before experimentation is essential to identify model limitations and minimize risks. A hybrid approach, combining AI and physics-based models, could provide a robust solution by leveraging the strengths of both domains: AI for its ability to process large volumes of data and physics-based models for their reliability in modeling complex mechanical behaviors. This paper proposes a comprehensive diagnostic methodology. It starts with feature extraction from time-domain analysis, which helps identify critical indicators of gearbox performance. Following this, a feature selection process is applied using the Fisher criterion, which ensures that only the most relevant features are retained for further analysis. These selected features are then employed to train an Adaptive Neuro-Fuzzy Inference System (ANFIS), a sophisticated approach that combines the learning capabilities of neural networks with the reasoning abilities of fuzzy logic. The proposed methodology is evaluated using a dataset of gear faults generated through energy simulations based on a six-degree-of-freedom (6-DOF) model, followed by a secondary validation on an experimental dataset. Full article
(This article belongs to the Section Electrical Machines and Drives)
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24 pages, 4894 KB  
Article
Improving Offshore Wind Speed Forecasting with a CRGWAA-Enhanced Adaptive Neuro-Fuzzy Inference System
by Yingjie Liu and Fahui Miao
J. Mar. Sci. Eng. 2025, 13(5), 908; https://doi.org/10.3390/jmse13050908 - 3 May 2025
Viewed by 505
Abstract
Accurate forecasting of offshore wind speed is crucial for the efficient operation and planning of wind energy systems. However, the inherently non-stationary and highly volatile nature of wind speed, coupled with the sensitivity of neural network-based models to parameter settings, poses significant challenges. [...] Read more.
Accurate forecasting of offshore wind speed is crucial for the efficient operation and planning of wind energy systems. However, the inherently non-stationary and highly volatile nature of wind speed, coupled with the sensitivity of neural network-based models to parameter settings, poses significant challenges. To address these issues, this paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by CRGWAA. The proposed CRGWAA integrates Chebyshev mapping initialization, an elite-guided reflection refinement operator, and a generalized quadratic interpolation strategy to enhance population diversity, adaptive exploration, and local exploitation capabilities. The performance of CRGWAA is comprehensively evaluated on the CEC2022 benchmark function suite, where it demonstrates superior optimization accuracy, convergence speed, and robustness compared to six state-of-the-art algorithms. Furthermore, the ANFIS-CRGWAA model is applied to short-term offshore wind speed forecasting using real-world data from the offshore region of Fujian, China, at 10 m and 100 m above sea level. Experimental results show that the proposed model consistently outperforms conventional and hybrid baselines, achieving lower MAE, RMSE, and MAPE, as well as higher R2, across both altitudes. Specifically, compared to the original ANFIS-WAA model, the RMSE is reduced by approximately 45% at 10 m and 24% at 100 m. These findings confirm the effectiveness, stability, and generalization ability of the ANFIS-CRGWAA model for complex, non-stationary offshore wind speed prediction tasks. Full article
(This article belongs to the Section Ocean Engineering)
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23 pages, 4798 KB  
Article
Rating the Impact of Risks in Banking on Performance: Utilizing the Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)
by Riyadh Mehdi, Ibrahim Elsiddig Ahmed and Elfadil A. Mohamed
Risks 2025, 13(5), 85; https://doi.org/10.3390/risks13050085 - 30 Apr 2025
Cited by 1 | Viewed by 2688
Abstract
This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few [...] Read more.
This study aims to rate the impact of the three major risks (credit, capital adequacy, and liquidity) on three financial performance measures (return on equity (ROE), earnings per share (EPS), and price-earnings ratio (PER)). This study stands out as one of the few in its field, and the only one focusing on banks in the Middle East and Africa, to employ the adaptive neural network-based fuzzy inference system (ANFIS) that combines neural networks and fuzzy logic systems. The significance of this study lies in its comprehensive coverage of major risks and performance variables and its application of highly technical, sophisticated, and precise AI techniques (ANFIS). The main findings indicate that credit risk, as measured by the non-performing loans (NPL) has significant impact on both ROE and EPS. Liquidity risk comes second in importance for ROE and EPS, with the loan-deposit ratio (LDR) being the dominant component. In contrast, liquidity risk is the most significant determinant of PER, followed by capital adequacy. Our results also show that CAR, LDR, and NPL are the most significant risk components of capital adequacy, liquidity, and credit risks, respectively. The study contributes to business knowledge by applying the ANFIS technique as an accurate predictor of risk rating. Future research will explore the relationship between risks and macroeconomic indicators and differences among countries. Full article
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27 pages, 2910 KB  
Article
Underwater Digital Twin Sensor Network-Based Maritime Communication and Monitoring Using Exponential Hyperbolic Crisp Adaptive Network-Based Fuzzy Inference System
by Bala Anand Muthu and Claudia Cherubini
Water 2025, 17(9), 1324; https://doi.org/10.3390/w17091324 - 28 Apr 2025
Cited by 1 | Viewed by 1310
Abstract
The underwater conditions of the coastal ecosystem require careful monitoring to anticipate potential environmental hazards. Moreover, the unique characteristics of the marine underwater environment have presented numerous challenges for the advancement of underwater sensor networks. Current studies have not extensively integrated Digital Twins [...] Read more.
The underwater conditions of the coastal ecosystem require careful monitoring to anticipate potential environmental hazards. Moreover, the unique characteristics of the marine underwater environment have presented numerous challenges for the advancement of underwater sensor networks. Current studies have not extensively integrated Digital Twins with underwater sensor networks aimed at monitoring the marine ecosystem. Consequently, this study proposes a decision-making framework based on Underwater Digital Twins (UDTs) utilizing the Exponential Hyperbolic Crisp Adaptive Network-based Fuzzy Inference System (EHC-ANFIS). The process begins with the initialization and registration of an Underwater Autonomous Vehicle (UAV). Subsequently, data are collected from the sensor network and relayed to the UDT model. The optimal path is determined using Adaptive Pheromone Ant Colony Optimization (AP-ACO) to ensure efficient data transmission. Following this, data compression is achieved through the Sliding–Huffman Coding (SHC) algorithm. The Twisted Koblitz Curve Cryptography (TKCC) method is employed to enhance data security. Additionally, an Anomaly Detection System (ADS) is trained, which involves collecting and pre-processing sensor network data. A Radial Chart is then utilized for effective visualization. Anomalies are detected using the CosLU-Variational Shake-Long Short-Term Memory (CosLU-VS-LSTM) approach. For standard data, decision-making based on the UDT model is conducted using EHC-ANFIS, with a fuzzification duration of 21,045 milliseconds. Finally, alerts are dispatched to the Maritime Alert Command Centre (MACC). This approach enhances maritime communication and monitoring along coastal areas, with specific reference to the Coromandel Coast, thereby contributing to the protection of the coastal ecosystem. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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23 pages, 3932 KB  
Article
A Predictive Model for the Shear Capacity of Ultra-High-Performance Concrete Deep Beams Reinforced with Fibers Using a Hybrid ANN-ANFIS Algorithm
by Hossein Mirzaaghabeik, Nuha S. Mashaan and Sanjay Kumar Shukla
Appl. Mech. 2025, 6(2), 27; https://doi.org/10.3390/applmech6020027 - 4 Apr 2025
Cited by 3 | Viewed by 935
Abstract
Ultra-high-performance concrete (UHPC) has attracted considerable attention from both the construction industry and researchers due to its outstanding durability and exceptional mechanical properties, particularly its high compressive strength. Several factors influence the shear capacity of UHPC deep beams, including compressive strength, the shear [...] Read more.
Ultra-high-performance concrete (UHPC) has attracted considerable attention from both the construction industry and researchers due to its outstanding durability and exceptional mechanical properties, particularly its high compressive strength. Several factors influence the shear capacity of UHPC deep beams, including compressive strength, the shear span-to-depth ratio (λ), fiber content (FC), vertical web reinforcement (ρsv), horizontal web reinforcement (ρsh), and longitudinal web reinforcement (ρs). Considering these factors, this research proposes a novel hybrid algorithm that combines an adaptive neuro-fuzzy inference system (ANFIS) with an artificial neural network (ANN) to predict the shear capacity of UHPC deep beams. To achieve this, ANN and ANFIS algorithms were initially employed individually to predict the shear capacity of UHPC deep beams using available experimental data for training. Subsequently, a novel hybrid algorithm, integrating an ANN and ANFIS, was developed to enhance prediction accuracy by utilizing numerical data as input for training. To evaluate the accuracy of the algorithms, the performance metrics R2 and RMSE were selected. The research findings indicate that the accuracy of the ANN, ANFIS, and the hybrid ANN-ANFIS algorithm was observed as R2 = 0.95, R2 = 0.99, and R2 = 0.90, respectively. This suggests that despite not using experimental data as input for training, the ANN-ANFIS algorithm accurately predicted the shear capacity of UHPC deep beams, achieving an accuracy of up to 90.90% and 94.74% relative to the ANFIS and ANN algorithms trained on experimental results. Finally, the shear capacity of UHPC deep beams predicted using the ANN, ANFIS, and the hybrid ANN-ANFIS algorithm was compared with the values calculated based on ACI 318-19. Subsequently, a novel reliability factor was proposed, enabling the prediction of the shear capacity of UHPC deep beams reinforced with fibers with a 0.66 safety margin compared to the experimental results. This indicates that the proposed model can be effectively employed in real-world design applications. Full article
(This article belongs to the Topic Advances on Structural Engineering, 3rd Edition)
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19 pages, 9018 KB  
Article
Estimation of Welding Current with Adaptive Neuro Fuzzy Inference System (ANFIS): Utilization of Arc Light Signal Emitted in the Arc Welding Process
by Yalçın Kanat, Yaşar Birbir and Gazi Büyüktaş
Appl. Sci. 2025, 15(7), 3824; https://doi.org/10.3390/app15073824 - 31 Mar 2025
Cited by 1 | Viewed by 894
Abstract
The main purpose of this study is to estimate the welding current using the arc light signal emitted during the welding process. Traditionally, welding operators determine this current from the arc light based on their visual perception. This study shows that, using artificial [...] Read more.
The main purpose of this study is to estimate the welding current using the arc light signal emitted during the welding process. Traditionally, welding operators determine this current from the arc light based on their visual perception. This study shows that, using artificial intelligence techniques, welding current can be automatically estimated through arc light and can also be useful for monitoring of the process and detecting its disturbances. For this purpose, initially, a data acquisition system is designed to synchronize the movement of the light sensor with the electrode holder. The electrode welding machine is set to different maximum current levels, and two electrodes with different diameters are used at each level. During the welding process, the arc light and current signals are acquired simultaneously. The obtained data are filtered and aligned by cross-correlation. For the ANFIS (adaptive neuro-fuzzy inference system) model, the arc light is defined as the input and the current as the output. The estimation results of ANFIS are further improved through filtering, shifting, and current-limiting processes. The maximum cross-correlation values for training and testing data are 0.9587, 0.9598, 0.9565, and 0.9323, respectively, while the R-squared values are 0.7033, 0.7640, 0.6449, and 0.5853. Compared with the artificial neural network (ANN) model, it is observed that the ANFIS model provides better prediction results. The results confirm that arc light signals can be effectively used for welding current prediction. Therefore, the proposed approach can contribute to the development of intelligent welding systems and quality welding processes by reducing operator dependency. Full article
(This article belongs to the Section Additive Manufacturing Technologies)
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24 pages, 4748 KB  
Article
Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach
by Daniyal Durmuş Köksal, Yeşim Ahi and Mladen Todorovic
Agronomy 2025, 15(3), 703; https://doi.org/10.3390/agronomy15030703 - 14 Mar 2025
Cited by 5 | Viewed by 1500
Abstract
Estimating the quality of treated wastewater is a complex, nonlinear challenge that traditional statistical methods struggle to address. This study introduces a hybrid machine learning approach to predict key effluent parameters from an advanced biological wastewater treatment plant and assesses the reuse potential [...] Read more.
Estimating the quality of treated wastewater is a complex, nonlinear challenge that traditional statistical methods struggle to address. This study introduces a hybrid machine learning approach to predict key effluent parameters from an advanced biological wastewater treatment plant and assesses the reuse potential of treated wastewater for irrigation. Three artificial intelligence (AI) models, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic-Mamdani (FLM), were applied to three years of daily inlet and outlet water quality data. Fuzzy Logic was employed to predict the usability potential of treated wastewater, with ANFIS categorizing quality parameters and ANN-based high-performance models (low MSE, 74–99% R2) applied in the fuzzy inference system. The qualitative reuse potential of treated wastewater for agricultural irrigation ranged from 69% to 72% based on the best-performing model. It was estimated that treated wastewater could irrigate approximately 35% of a 20,000-hectare agricultural area. By integrating machine learning models, this research enhances the accuracy and interpretability of wastewater quality predictions, providing a reliable framework for sustainable water resource management. The findings support the optimization of wastewater treatment processes and highlight AI’s role in advancing water reuse strategies in agriculture, ultimately contributing to improved irrigation efficiency and environmental conservation. Full article
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