As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in decision-making methods. In addition, existing models are also heavy and inefficient. A lightweight framework for fault diagnosis in EV drive motors is presented with the aid of Recursive Feature Elimination with Cross-Validation (RFE-CV), parameter optimization, and in-depth preprocessing. We further optimize the models and their combination to a hybrid Soft Voting Classifier. These techniques were applied to a dataset of 40,040 data entries that had been simulated by a Variable Frequency Drive (VFD) model. We evaluated eight machine learning models, and our proposed Soft Voting Classifier has the highest test accuracy of 94.52% and a Kappa score of 0.9210 on diagnostic performance. Also, the model has minimal memory usage and low inference latency. In addition, Local Interpretable Model-Agnostic Explanations (LIME) were used to improve transparency and gain an understanding of decisions made through the Soft Voting Classifier. Also, the framework was validated by an additional real-world dataset, thereby further confirming its robustness and consistency in performance for different conditions, which indicates the generalizability of the framework in real-world applications. RFE-CV is found to be very effective in feature selection and helps to construct a lightweight and cost-effective ensemble voting model for enhancing fault diagnosis for EV Drive Motors, overcoming its unsatisfactory transparency, accuracy, and computational efficiency. Finally, it contributes to the development of safer and more reliable EV systems through the development of models supervised on fewer features to give the computing time that is a little lighter without compromising its diagnostic performance.

Magnetic resonance imaging (MRI) is a critical clinical tool that requires precise and reliable interpretation for effective brain tumor diagnosis and timely treatment planning. Deep learning methods have advanced automated tumor classification greatly in the last few years, but many of the current methods are still challenged by a lack of interpretability, a lack of testing on region-focused data, and a lack of model robustness testing. Such limitations reduce clinical trust and limit the practice of automated diagnostic systems. To address these challenges, this study proposes an interpretable deep learning model for classifying brain tumors using the PMRAM dataset, which is a Bangladeshi brain MRI collection containing four categories: glioma, meningioma, pituitary tumor, and normal brain.. The proposed pipeline combines image preprocessing and feature enhancement methods, and then it trains a series of squeeze-and-excitation (SE)-enhanced convolutional neural networks such as VGG19, DenseNet201, MobileNetV3-Large, InceptionV3, and EfficientNetB3. The SE-enhanced EfficientNetB3 performed best, with 98.70% accuracy, 98.77% precision, 98.70% recall, and 98.70% F1-score. Cross-validation also demonstrated stable performance, with a mean accuracy of 96.89%. The model also exhibited efficient inference with low GPU memory consumption, enabling predictions in about 2–4 s per MRI image. Grad-CAM++ and saliency maps were used to improve the transparency of the results, and it was found that the network was concentrated on the clinically significant parts of the tumor, which affected the model predictions. Further robustness analysis and cross-dataset testing are additional evidence of the generalization possibility of the model. An online application was also implemented to allow real-time prediction and visual explanation of brain tumors. Overall, the proposed framework offers a precise, interpretable, and promising solution to automated brain tumor classification using MRI images.

Solar panels are becoming very essential in providing sustainable energy but they are usually affected by defects on the surface like dust, snow, bird droppings, physical damages and electrical faults which interfere with their performance. These faults must be identified accurately and in a timely manner to enhance energy efficiency, lower the maintenance cost, and supplement the traditional manual methods of inspection which are labor-intensive, time-consuming and subject to human errors in judgment. The most common methods, such as traditional CNNs and hybrid architectures tend to be less accurate, less explainable and cannot be properly evaluated to be deployed in real time. In order to overcome these issues, we proposed SwinDeiTViT an ensemble model that fuses Swin-Tiny and DeiT-Small vision transformers using a soft voting layer. The framework uses sophisticated preprocessing and data augmentation in order to increase feature visibility and model generalization. SwinDeiTViT reaches an overall accuracy of 99.31%, cross-validation accuracy of 98.41%, F1-scores of approximately 1.0 and a Cohen’s Kappa of 0.9915, with low inference latency that is compatible with edge deployment on a varied set of 826 solar panel images expanded to 4320 samples. Also, Grad-CAM visualizations provide transparent interpretability by highlighting regions contributing to predictions.The proposed framework can provide a functional, operational solution to real-time monitoring of solar panels, which is useful to enhance manual inspection, and be more robust, reliable, and explainable compared to a baseline model.

In clinical diagnosis, the automatic detection of anomalies using sophisticated deep learning (DL) architectures has become a highly sought-after and essential capability. This domain includes two distinct categories of pathology images: whole slide images (WSIs) and images consisting of individual patches. In state-of-the-art research, deep learning architectures have been validated using several public pathology datasets. Although the models demonstrated state-of-the-art performance on public datasets, further investigation is required to assess their performance on a private pathology dataset in practical cases. This study introduces a segmentation-based image quality analysis methodology that employs pretrained encoders (MobileNetV2, DenseNet121, VGG16, and ResNet50) with the baseline U-Net model. Before the training and validation phases, the images were preprocessed using the QuPath pathology tool, where they were resized and augmented to improve their suitability for the task. To evaluate the models, we used a private pathology dataset collected at the Samsung Medical Center of a Korean hospital, which consisted of whole slide images. It has three defects: bubble, tissue fold, and pen mark. Four different metrics were used to evaluate the performance of the models. The intersection over union (IoU) and highlight are introduced to gauge the performance of state-of-the-art models on real-life sample data. The experimental results, derived from a comprehensive analysis, revealed that the U-Net with the ResNet50 encoder model achieved an average accuracy of 98.17%, IoU of 98.07%, Dice score of 99.96%, and Jaccard distance of 0.0197 on the test split of all WSIs. Furthermore, we compared the model’s performance with that of the U-Net baseline architecture and pretrained architectures, wherein the U-Net with the ResNet50 encoder outperformed the U-Net model for the whole slide image dataset.