The novelty of this research lies in the development of a new battery management system (BMS) for electric vehicles, which utilizes an artificial neural network (ANN) and fuzzy logic-based adaptive droop control theory. This innovative approach offers several advantages over traditional BMS systems, such as decentralized control architecture, communication-free capability, and improved reliability. The proposed BMS control system incorporates an adaptive virtual admittance, which adjusts the value of the virtual admittance based on the current state of charge (SOC) of each battery cell. This allows the connected battery cells to share the load evenly during charging and discharging, which improves the overall performance and efficiency of the electric vehicle. The effectiveness of the proposed control structure was verified through simulation and experimental prototype testing with three linked battery cells. The small signal model testing demonstrated the stability of the control, while the experimental results confirmed the system’s ability to evenly distribute the load among battery cells during charging and discharging. We introduce a unique battery management system (BMS) for electric cars in this paper. Our suggested BMS was implemented and tested satisfactorily on a 100 kWh lithium-ion battery pack. When compared to typical BMS systems, the results show a surprising 15% increase in overall energy efficiency. Furthermore, the adaptive virtual admission function resulted in a 20% boost in battery life. These large gains in energy efficiency and battery longevity demonstrate our BMS’s efficacy and superiority over competing systems. Overall, the proposed BMS represents a significant innovation in the field of electric vehicle battery management. This combination of ANN and adaptive droop control theory based on fuzzy logic provides a highly efficient, reliable, and economical solution for EV battery cell management.

Sliding mode control (SMC) of induction motor is a new concept in the current scenario, as it seeks to improve torque control accuracy and power steering efficiency through the use of pulse width modulation schemes. Furthermore, artificial neural network‐based sliding mode control is applied to a squirrel cage induction motor, which is used in the steering control of automobiles. The artificial neural network (ANN)‐based SMC is more popular due to its robustness and good stability in external parameter variation. Additionally, an SMC and an ANN‐based SMC are employed to compute the torque and flux, improving performance for power steering applications. The performance of the designed model is validated through MATLAB/Simulink and experimental models with different controllers under various operating conditions. The controller has been embedded into a TMS320F28335 controller, and performances have been evaluated. The performance analyses of induction motor using different controllers are performed. The transient performances of induction motor such as delay time, rise time, settling time, and steady‐state error are investigated. The proposed work is analysed by using a mathematical model and implemented in a test‐bench model for validation.

This study introduces a hybrid deep learning model integrating EfficientNet-B3 and Vision Transformer with an Attention Fusion mechanism for automated colon cancer detection using the Kvasir endoscopic dataset. The model leverages EfficientNet-B3’s strength in capturing fine-grained local textures and Vision Transformer’s ability to model global contextual relationships. A multi-head attention-based fusion block harmonizes these features, achieving comprehensive representations and enhanced classification stability. Model optimization was guided by the Matthews Correlation Coefficient (MCC), alongside evaluations of accuracy, F1-score, and Brier Score. Experimental results demonstrate a 96.2% accuracy and an MCC of 0.961, surpassing standalone baselines and existing benchmark architectures. Cross-validation confirmed robust generalization, while Grad-CAM analyses improved interpretability by visualizing salient histopathological regions influencing predictions. Despite slight overfitting tendencies, the model maintained strong performance across all eight image classes. These findings highlight the model’s ability to address limitations of single-architecture approaches by combining local and global feature extraction, offering rapid, objective, and reliable diagnostic support. The proposed framework shows significant promise for integration into computer-aided colonoscopy systems, paving the way for enhanced clinical diagnostics and reduced pathologist workload through AI-driven precision medicine.

This paper presents the results of research and development of capacitive-based sensors of rotating shaft vibration for fault diagnostic systems of powerful turbines and hydro generators. It showed that diagnostic systems with special sensors are the key to increasing the reliability of powerful turbines and hydro generators. The application of sensors in monitoring systems was considered, and the requirements for the sensors used were analyzed. Structures of concentric capacitive-based sensors of rotating shaft vibration based on the measurement of the capacitance value from the distance to the metal surface were proposed. The design scheme was created for determining electrode dimensions of the rotating shaft vibration capacitive-based sensors with concentric electrodes, and analytical dependences were obtained. The calculation results allow the selection of optimal parameters of the active and guard electrodes. Analytical and computer simulation methods determined the response functions of the capacitive sensors. Analytical calculations and simulation results using 3D FEM were used to find the response functions of the sensors. The calculation of the characteristics of the capacitive-based sensors of rotating shaft vibration is presented. The study of the influence of fringe effects was carried out using the obtained results of the modeling and analytical calculations.

The Sustainable Energy Resource integrated with Energy Storage System is deployed inside a microgrid, using a power management method to effectively regulate energy consumption during peak demand. Demand-based energy management measures, such as distributing load and stalling appliance usage amid peak hours are executed. An Integrated Energy Management System (EMS) was proposed employing fuzzy logic as a solution to manage the energy needs of loads in this work. The system effectively used a combination of the hybrid utility grid, photovoltaic (PV), wind, and battery to optimise the utilisation of renewable energy resources for load supply. The implementation of an effective management system is intended to control the energy production of a novel hybrid electrical power system by changes in load. The EMS concomitantly considers power demand, renewable power, and State of Charge (SoC) through the incorporation of fuzzy logic control. Cost analysis employing three optimisation techniques like Firefly, PSO and Genetic Algorithm for the equivalent load profile and sources also conducted. Fuzzy EMS enhances energy management by 41.40% LCOE in comparison to the Firefly Algorithm. It decreases expenses by 24.09% more effectively than the PSO Algorithm. In comparison to the Genetic Algorithm, the Fuzzy EMS demonstrates a 45.02% reduction in LCOE, hence establishing its capacity to provide a more economical energy solution. This technique considers security constraints and makes an intelligent choice of energy sources based on grid electricity costs. Maximising system resilience and making the most effective feasible utilisation of green energy sources are the primary goals.