Search Results (610)

Decision-making in cyber defense exercises (CDX) is shaped by technical, emotional, motivational, and collaborative human factors under uncertainty and time pressure. This study proposes a human-centered Adaptive Neuro-Fuzzy Inference System (ANFIS) framework to model and predict Counterfactual Decision Reflection (CDR) outcomes in CDX environments. Two complementary datasets representing technical, emotional, motivational, and teamwork-related dimensions were collected from the international Lithuanian Armed Forces cyber defense exercise Amber Mist 2024 and analyzed using Spearman correlation, 3D regression surface modeling, fuzzy rule extraction, and ANFIS prediction to investigate the relationship between human factors and CDR. The results demonstrated that teamwork, communication, and collaboration have a stronger influence on decision stability than isolated technical competencies. Baseline ANFIS evaluation indicated that triangular membership functions provided the best generalization, while generalized bell functions achieved the lowest training errors. To improve model robustness, multiple synthetic data augmentation methods were evaluated. The augmented ANFIS models substantially improved predictive performance, reducing testing error values significantly. The findings confirm that synthetic-data-enhanced neuro-fuzzy modeling provides an effective and interpretable framework for analyzing human-centered cybersecurity decision-making processes in cyber defense exercises. Full article

The accumulation of waste tyres presents a significant environmental challenge owing to their non-biodegradable nature and limited recycling options. The incorporation of tyre-derived rubber into concrete offers a promising strategy to reduce landfill waste and lower the consumption of natural aggregates. This study presents an integrated experimental and machine learning-based framework for evaluating and predicting the performance of rubberized concrete. M25-grade concrete mixtures were prepared with partial replacement of coarse aggregates by waste tyre rubber at proportions of 0%, 10%, 20%, and 30% by volume. Mechanical performance was assessed through compressive and split-tensile strength tests, whereas durability was evaluated using water absorption measurements. Microstructural characterization was conducted using scanning electron microscopy and X-ray diffraction analysis. In parallel, predictive models based on artificial neural networks, adaptive neuro-fuzzy inference systems, and fuzzy logic were developed and validated using statistical measures. The results showed that increasing rubber content reduced mechanical strength and increased water absorption due to weaker interfacial bonding and higher porosity. Nevertheless, concrete containing a 10% rubber replacement retained approximately 90% of the control strength while maintaining satisfactory durability. The machine learning models demonstrated strong predictive accuracy for estimating concrete properties. Overall, the findings suggest that limited incorporation of waste tyre rubber can contribute to the development of sustainable and low-carbon concrete materials with reduced embodied energy and environmental impact. Full article

This study presents the design and development of a wearable lower-limb exoskeleton system aimed at supporting motion assistance in monoplegia-related conditions. The proposed approach integrates a simplified sensing configuration with a data-driven neuro-fuzzy control framework based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). Motion data are acquired from the healthy limb using bend flex sensors and are used to generate control signals for the actuation of the impaired limb through an Arduino-based embedded platform. The mechanical structure is developed using a lightweight 3D-printed design combined with high-torque DC motors and gear transmission mechanisms. Experimental evaluation conducted under controlled conditions demonstrates that the system is capable of capturing and reproducing fundamental motion patterns, with the ANFIS model providing a consistent mapping between sensor inputs and actuator responses. The obtained results indicate a satisfactory level of performance for motion pattern reproduction, particularly in terms of temporal behavior and transition between movement states. The presented system emphasizes low-cost implementation, computational efficiency, and practical implementation, making it suitable as a proof-of-concept framework for wearable assistive technologies. While the results demonstrate the feasibility of the proposed approach for motion reproduction, further studies involving extended testing and user-specific adaptation are required to assess its potential applicability in real-world scenarios. Full article

Accurate prediction of energy consumption in the future plays an essential role in national energy security and sustainable economic planning in Vietnam. However, the energy consumption data is subject to non-linearity, high fluctuation and complex seasonal variations. To address this problem, this study proposes a novel framework based on an ANFIS model; the proposed models were established by integrating the Denton method, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and meta-heuristic algorithms, namely Particle Swarm Optimization (PSO), the Grey Wolf Optimizer (GWO), and the Whale Optimization Algorithm (WOA). The simulation results demonstrate that the PSO-ANFIS model achieved the best performance, with a Mean Absolute Percentage Error (MAPE) of 4.65% and an $R^2$ score of 0.7275. Based on this result, this study suggests that the PSO-ANFIS model is a promising candidate for forecasting the energy consumption demand in Vietnam. Energy consumption demand will reach 3108.38 billion kWh by 2030. These findings provide a reliable scientific foundation for grid management and strategic policy-making. Full article

Diagnosing tilapia diseases in complex aquaculture environments is severely hindered by noisy backgrounds and limited high-quality pathological data. To overcome these bottlenecks, this study presents a two-stage diagnostic framework integrating an enhanced Segment Anything Model (SAM) with an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the first stage, SAM is augmented with a Convolutional Block Attention Module (CBAM) feature adapter and a Region Proposal Network (RPN)-based prompt encoder. This design enables the automated and precise extraction of irregular disease lesions by self-generating spatial prompts, thereby isolating water background noise. In the second stage, clinical color features extracted from the lesion masks are classified using ANFIS. To optimize performance on small-scale datasets, ANFIS parameters are trained via Particle Swarm Optimization (PSO) under a numerically stable One-vs-Rest (OvR) binary cross-entropy loss. Validated on the public dataset “Enhancing Disease Detection in Nile Tilapia”, our method delivers an average segmentation Dice coefficient of 86.2% and a classification accuracy of 93.5%. This hybrid approach demonstrates strong potential as a foundational baseline for the automated monitoring of aquaculture diseases. Full article

Consistent laser cutting quality is one of the problems associated with the nonlinearity of relationships between process parameters and output responses. This problem acquires particular importance when it comes to cutting advanced nanocomposites, which requires precise tuning. Despite the wide adoption of intelligent modelling, few studies have investigated the comparative efficiency of various approaches based on the use of the same dataset. In this research, the effectiveness of three models—Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Fuzzy Logic System (FLS)—was tested on experimental data related to the CO₂ laser cutting of ABS/CNT nanocomposites. Input parameters included laser power and cutting speed, whereas HAZ width, kerf width, and surface electrical resistivity were used as output data. Data was split into training, testing, and validation datasets; models were created using supervised machine learning. Model performance was evaluated using Root Mean Square Error (RMSE). Analysis of results showed that ANN demonstrated acceptable predictive capabilities, yielding correlation coefficients (R) close to 1 (≈0.99) and RMSE values of 0.2956 for HAZ, 0.2061 for kerf width, and 2.3655 for surface electrical resistivity. Prediction by means of FLS was able to identify general tendencies; however, it produced RMSE values of 0.4741 for HAZ, 0.6297 for kerf width, and 1.9258 for surface electrical resistivity. Finally, the ANFIS model proved to be the most reliable model, yielding the lowest RMSE values for HAZ (0.2784), kerf width (0.0450), and surface electrical resistivity (0.0905). In conclusion, this research shows that ANFIS can be used effectively for building models predicting laser cutting processes; therefore, it represents an approach worth using in future investigations in this field. Full article

This study presents a comprehensive symmetry-breaking analysis framework for a twelve-legged Jansen walking robot, integrating finite element analysis (FEA) with adaptive neuro-fuzzy inference system (ANFIS) surrogate modeling. A systematic dataset of 210 cases was generated by combining 21 single- and multi-leg failure scenarios across 10 load levels (20–200 N) on the PLA-based 3D-printed prototype. Two novel dimensionless metrics are introduced: the Resilience Index (RI), quantifying the proportional stress increase relative to the baseline, and the Asymmetry Index (AI), measuring leg-reaction force distribution imbalance. Results identify a clear fault-tolerance threshold between two- and four-leg failures: single-leg failures remain at LOW risk (RI < 0.20), while three-leg asymmetric failures (S18) reach CRITICAL level (RI = 1.13, ~97% of PLA yield strength). A hybrid machine learning framework is proposed, applying ANFIS to maximum stress (R² = 0.817) and safety factor (R² = 0.936) predictions, while reserving FEA tables for bimodal outputs. The ANFIS surrogate achieves approximately 10⁶× speedup over FEA (262.6 μs vs. 5–8 min), enabling real-time fault diagnosis and digital twin applications. The framework is generalizable to other multi-legged robotic systems requiring fault-tolerance evaluation. Full article

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article

The vertical landing of quadrotor unmanned aerial vehicles (UAVs) involves highly transient impact dynamics that generate significant vibrations on the UAV body, particularly under uncertain touchdown conditions such as uneven terrain, asymmetric ground contact, and high-impact landing. In this study, a robust semi-active vibration control framework is proposed for a quadrotor UAV equipped with a four-point soft landing gear system. The UAV is modeled as a three-degree-of-freedom rigid body including heave, pitch, and roll motions, while each landing gear leg is represented by an equivalent spring-damper mechanism with adaptively controllable damping characteristics. To evaluate the effectiveness of the proposed framework, PID (Proportional–Integral–Derivative), GA-PID (Genetic Algorithm-Based Proportional–Integral–Derivative), Fuzzy–PID (Fuzzy Logic-Based Proportional–Integral–Derivative), and ANFIS-PID (Adaptive Neuro-Fuzzy Inference System-Based Proportional–Integral–Derivative) controllers are comparatively investigated under five different landing scenarios. The nonlinear touchdown dynamics are implemented in the MATLAB/Simulink environment using a state-space-based simulation model. The results demonstrate that intelligent adaptive control methods significantly improve landing stability and vibration attenuation compared to the conventional PID controller. Among all methods, the ANFIS-PID controller achieved the best overall performance. Under the most severe landing condition, the peak vertical displacement was reduced from 0.114 m to 0.025 m, while the maximum pitch and roll angles decreased from approximately 11° to nearly 2°. Additionally, the settling time was reduced from nearly 10 s to below 3 s. Full article

This study proposes an experimental data-driven hybrid prediction framework for hydraulic turbine performance using a Particle Swarm Optimization-enhanced Extreme Learning Machine (PSO-ELM). The performance of three hydraulic turbines, namely Pelton, Kaplan, and Francis turbines, was experimentally investigated under different jet-opening and guide-vane conditions. The experimental results showed that the Pelton turbine (PT) achieved its highest efficiency at low jet opening, whereas the Kaplan and Francis turbines performed better at higher guide-vane openings. The measured data includes 36 tests, which were then used to evolve and evaluate hybrid ML models for predicting hydraulic power and efficiency. Jet-opening or guide-vane position (25%, 50%, 75% and 100%) and rotational speed were used as input variables, while brake power and efficiency were used as output variables. The proposed PSO-ELM model was compared with other optimized ELM models, including Genetic Algorithms Extreme Learning Machine (GA-ELM), Differential Evolution Extreme Learning Machine (DE-ELM), and Whale Optimization Algorithm Extreme Learning Machine (WOA-ELM), as well as Particle Swarm Optimization–Adaptive Neuro-Fuzzy Inference System (PSO-ANFIS) and Particle Swarm Optimization–Multi-Layer Perceptron (PSO-MLP) models. The suggested method presents a hopeful structure for tackling the difficulties linked to performance evaluation, thus enabling a more dependable and effective use of energy resources. The main findings validate that a PSO-based structure reaching an R² value of 0.997 is more efficient in predictive tool performance optimization for hydropower systems. Full article

Grid-connected photovoltaic (PV) systems integrated into industrial and institutional buildings are critical components of sustainable built environments, where accurate real-time power estimation underpins smart energy management, demand–supply balancing, and reduced dependence on the utility grid. This study develops and validates an Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting of the flexible power point (FPP) in a 117.76 kWp rooftop PV plant serving a technical workshop facility in northwestern Algeria. The proposed model uses environmental inputs (solar irradiance, ambient temperature, module temperature) and electrical inputs (load power, grid power) acquired from a supervisory monitoring infrastructure to predict the PV system’s FPP under real operating conditions in the built environment. A dataset of 24,479 valid samples spanning 85 distinct calendar days (1 May to 24 July 2025) was collected and preprocessed through cleaning, filtering, and feature-specific normalization. To ensure rigorous out-of-sample evaluation, three complementary validation strategies were implemented: (S1) a random day-based split (60 train/11 test days), (S2) a strictly chronological 70/15/15% split (50/11/10 days), and (S3) an external 14-day hold-out (11–24 July 2025) excised before any training, tuning or model selection step. Statistical analysis reveals strong nonlinear dependence of PV power on solar irradiance and module temperature, with correlations $r\approx 0.93$ between irradiance and module temperature, $r\approx 0.82$ between irradiance and PV power, and $r\approx 0.95$ between load and grid power, highlighting the importance of accurate predicting for facility-level energy management. The ANFIS model achieves $R^2=0.9992$, RMSE $=653.62$ W and MAE $=276.90$ W on the random-split test set; $R^2=0.9998$, RMSE $=325.40$ W and MAE $=119.17$ W on the chronological test set and $R^2=0.9997$–$0.9998$, RMSE $=363.45$–$408.50$ W on the external 14-day hold-out that was never seen during training. Comparative experiments with k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and a Deep Neural Network show that ANFIS is the only model maintaining sub-700 W RMSE on every split, whereas all five benchmarks degrade sharply under chronological and external evaluation (e.g., SVM 2225 → 5198 W; Decision Tree 7440 → 8058 W; DNN 1576 → 2576 W). The persistence of test/external RMSE below the training RMSE on data never used during model construction empirically rules out data leakage as a cause of the high accuracy. These results demonstrate that the proposed, interpretable neuro-fuzzy framework offers a robust and accurate tool for PV power estimation in building-integrated systems, supporting smart energy management and improved performance of energy-intensive built environments. Full article

This study proposes a hybrid portfolio optimization framework integrating financial network analysis, Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and GA for asset allocation in BIST. The empirical analysis focuses on constituent firms within the BIST 30, BIST 50, and BIST 100 indices using daily stock market data covering the period 2000–2025. Financial network centrality indicators and technical analysis variables were employed to identify structurally influential assets and model nonlinear investment decision dynamics under market uncertainty. The ANFIS framework was utilized to capture complex relationships between technical indicators and portfolio allocation decisions, while Genetic Algorithms optimized portfolio weights under return maximization and downside-risk minimization constraints. To reduce overfitting risk, Principal Component Analysis (PCA) and K-fold cross-validation procedures were implemented during model training. The proposed framework was additionally evaluated using out-of-sample backtesting over the 2021–2024 period and compared against benchmark portfolio strategies, including Equal Weight and Minimum Variance portfolios. Empirical findings indicate that the ANFISGA framework achieved superior risk-adjusted performance, higher Sharpe and Sortino ratios, and lower maximum drawdown during volatile market conditions. The study contributes to the portfolio optimization literature by integrating financial network indicators with adaptive fuzzy decision systems and evolutionary optimization techniques within an emerging market context. The proposed framework is intended primarily as an adaptive portfolio decision-support system rather than a purely predictive forecasting model. Full article

Rural regions of Uzbekistan experience continuing issues of energy access because of poor grid networks and variable renewable sources. The solution is small-scale wind turbines and energy storage. But the wind speeds and load demand are variable, and thus this solution needs intelligent control systems to perform its best. This paper is an attempt to design an adaptive neuro-fuzzy inference system (ANFIS) controller to control a small wind power system with a battery storage unit. The controller will be intelligent to control the flow of power between the wind turbine, battery, and local loads. A model of MATLAB/Simulink is created to simulate the reaction of the system to various wind and load conditions. The simulation results indicate that the ANFIS controller improves voltage regulation, reduces power fluctuations, and enhances battery charge–discharge performance compared to the conventional PI controller. Environmental variability is effectively responded to by the system, making it more reliable and energy-efficient. ANFIS control and wind–battery microgrid integration provides a feasible and expandable off-grid electrification solution to remote areas. This strategy promotes the renewable energy ambitions of Uzbekistan and offers an example of smart microgrid implementation in other resource-limited rural areas. The next steps would be towards practical applications and hardware verification. Full article

This paper addresses key limitations in transmission–distribution (T&D) co-simulation for resiliency, including fragmented modeling, high complexity, synchronization issues, weak renewable control, and data access constraints. A unified co-simulation framework is proposed to optimize microgrid formation and operation in high-penetration renewable systems, improving resiliency while reducing costs and network losses. The developed co-simulation platform enables modular, conflict-free synchronization between transmission and distribution networks without additional handshake software, allowing independent data transfer and seamless co-optimization. The technique assists in transmission and distribution dynamic coordination, supports economic dispatch, and performs three-phase optimal power flow (OPF). An Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for load forecasting and optimization modeling, enabling fast convergence and computational efficiency. The framework supports both grid-connected and islanded modes, including dynamic islanding, reconnection, and load prioritization. Case studies using IEEE 14-Bus transmission with 15-Bus and modified unbalanced 123-Bus distribution systems validate the approach. Results show up to a 68% reduction in operating costs and significant reductions in loss, demonstrating improved resilience, scalability, and secure data exchange for modern power systems. Full article

Underwater gliders are suited for long-duration oceanographic observation, but their endurance is bounded by onboard energy capacity. An overlooked source of energy loss is the attitude control system, which repeatedly repositions the internal moving mass to hold the desired pitch angle throughout each gliding cycle. Conventional PID and manually tuned fuzzy controllers continue driving the actuator after pitch convergence and adapt poorly to nonlinear buoyancy variations at depth. To address this, we propose an ANFIS (Adaptive Neuro-Fuzzy Inference System)-based pitch control strategy for a 1000 m-class underwater glider. A nonlinear 6-DOF dynamic simulator incorporating experimentally derived power models for the buoyancy engine and attitude controller was validated up to 100 bar. A 13-rule Sugeno-type fuzzy inference system was optimized through ANFIS hybrid learning using approximately 5500 samples from PID steady-state data. Simulation results show energy savings of 57.05% over PID and 4.98% over a manually tuned fuzzy controller, with no degradation in tracking accuracy. Sea trials confirm a reduction in moving mass displacement under real disturbance conditions, providing qualitative evidence consistent with the simulation results. Further quantitative validation of the energy reduction effect through free-running sea trials remains as future work. Full article