This study presents a hybrid learning model that integrates MobileNetV2 and Vision Transformer (ViT) with a stacking model to classify maize leaf diseases, addressing the critical need for early detection to improve agricultural productivity and sustainability. Utilising the ‘Corn or Maize Leaf Disease Dataset’ from Kaggle, comprising 4,062 high‐resolution images across five classes (Common Rust, Grey Leaf Spot, Healthy, Northern Leaf Blight, Not Maize Leaf), the model achieves an impressive accuracy of 96.73%. Transfer learning from ImageNet, coupled with data augmentation (rotation, flipping, scaling, brightness adjustment), enhances generalisation, while a 20% dropout rate mitigates overfitting. The key advantage of the hybrid model lies in its ability to combine the strengths of MobileNetV2's localised feature extraction and ViT's global context understanding, enhanced by the stacking model's ability to reduce the weaknesses of either model. Explainable AI techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model‐agnostic Explanations (LIME), and Grad‐CAM, provide transparent insights into model decisions, fostering trust among agricultural stakeholders. Comparative analysis demonstrates the model’s superiority over prior works, with F1‐scores ranging from 0.9276 to 1.0000. Despite minor misclassifications due to visual similarities, the model offers a robust, interpretable solution for precision agriculture.

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.

Economic dispatch (ED) is one of the most important problems in terms of energy planning, management, and operation in power systems. This study presents a snow ablation optimizer (SAO) algorithm developed with the fuzzy-based fitness–distance balance (FFDB) method for solving ED problems in small-, medium- and large-scale electric power systems and determining the optimal operating values of fossil fuel thermal generation units. The FFDB-based SAO algorithm (FFDBSAO) controls early convergence problems through balancing exploration–exploitation and improves the solving of high-dimensional optimization problems. In the light of extensive experimental studies conducted on CEC2020, CEC2022, and classical benchmark test functions, the FFDBSAO2 algorithm has shown superior performance against its competitors. Wilcoxon and Friedman’s statistical analysis results confirm the performance and efficiency of the algorithm. Moreover, the proposed algorithm significantly reduces total fuel cost by optimizing fossil fuel thermal generation units. According to the results, the scalability and robustness of the algorithm make it a valuable tool for solving large-scale optimization problems in the planning of electric power systems.

The exceptional properties of Permanent Magnet Synchronous Motors (PMSMs), including their small construction, high power-torque density, and high efficiency, make them one of the most popular electrical machines. However, the motor structure needs to be optimized for circumstances like boosting the PMSM’s energy efficiency, optimizing output power, and lowering motor weight and cogging torque. This research aims to identify the optimal values for the parameters essential to achieving the most efficient design of a 15 kW PMSM. For this purpose, a new optimization algorithm is proposed. This proposed algorithm is an application of the fuzzy logic-based fitness distance balance design to the gradient-based optimization algorithm (GBO). The developed algorithm is called fuzzy Fitness Distance Balance Gradient-Based Optimization (fFDBGBO) algorithm. The proposed algorithm and various optimization algorithms with effective results in the literature were tested for the fitness function determined to find the optimal values of Embrace, Offset, Skew Width, Magnet Thickness, and slot bottom width (Bs1) of PMSM. The results indicate that the suggested algorithm achieves better performance than rival algorithms in terms of effectiveness. Compared to the GBO algorithm, the proposed fFDBGBO method achieves a 0.2979% increase in efficiency. Compared to other algorithms, the suggested algorithm’s objective function has the highest slot fill factor (49.9995%), yet it still complies with the specified limit value. Relative to the initial design, the motor efficiency achieved with the fFDBGBO algorithm improved from 91.7788% to 93.0366%, while the magnet weight decreased from 2.14837 to 2.09475, and the total motor weight remained approximately the same as 53.2286 kg and 54.0436 kg.

This study presents a deep learning-based framework for the accurate classification of power quality (PQ) disturbances using time-series and environmental data. Four architectures: artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model were implemented and evaluated on a dataset containing key electrical and environmental features such as voltage sag, harmonics, temperature pollution level. Comprehensive preprocessing, including normalization, correlation assessment of skewness and kurtosis analysis, ensured statistical reliability. Among the evaluated models, the hybrid architecture achieved the best performance, with an accuracy of 98.91% and an F1-score of 98.90%, outperforming all standalone approaches. Model interpretability was enhanced using local interpretable model-agnostic explanations (LIME), which identified feature contributions for individual predictions. A comparison with eight recent studies demonstrated competitive or superior performance in both accuracy and explainability. The integration of high-performing hybrid modeling with interpretable AI makes the proposed system well-suited for real-time PQ monitoring in smart grid environments. Future work may incorporate unsupervised and transfer learning methods to improve adaptability across varying grid conditions and data scarcity scenarios.