Over 70 million deaf people worldwide frequently use sign language. However, the challenges of real-time implementation and interpretation have limited automated sign language recognition systems. CNN-based older techniques frequently overlook subtle features that are crucial for differentiating between similar motions. This research examines the recognition of 37 distinct sign language classes, which include A to Z, 0 to 9, and a space. We utilized a dataset of 55,500 photos for this. Our recommended method, ViT-Sign, uses a Tiny version of Vision Transformer. It has only 5.5 million parameters, and we evaluate its performance using accuracy, precision, recall, and F1-score. Our model achieves 99.42\% accuracy, 99.44\% precision, 99.42\% recall, and a 99.42\% F1-score. We also employ Grad-CAM visuals to make it clear that our model concentrates on the correct regions of the hands rather than random background information. Experiments show that Vision Transformers can efficiently record spatial relationships and acquire discriminative representations of gestures. The proposed method can be utilized to create successful communication systems that allow hearing-impaired people to communicate more easily.

While banana is an important crop in the agricultural sector of South Asia, fungal and bacterial leaf diseases continue to cause substantial losses and pose a threat to the livelihoods of farmers in the region. The current method of field diagnosis is mostly manually, which is subjective and slow, inconvenient in the field. This paper proposes a rigorous and explainable deep learning framework for the automated banana leaf disease classification that overcomes three major drawbacks of previous works: 1) the lack of comparison of multiple models in similar experimental setups, 2) the lack of statistical validation, and 3) the absence of a connection between the model prediction and the farmer’s guidance. We test the three architectures: a lightweight Custom CNN, ResNet- 50 and EfficientNet-B0, on the publicly available BananaLSD dataset, based on a standardized preprocessing and augmentation pipeline. Stratified 5-fold cross validation is used for assessing the performance and paired t test is used to check the statistical significance of the difference observed. The best classification accuracy of EfficientNet-B0 is 99.69%, the mean cross-validation accuracy is 99.36%, and the standard deviation is the lowest (0.10), showing very high stability. For transparency, we integrate four explainability methods: Grad-CAM, Grad-CAM++, Score- CAM and Layer-CAM, which result in uniform lesion localized heatmaps that validate biologically meaningful feature focus. Finally, an intelligent advisory module translates the predictions in actionable management recommendations directly deployable by agricultural extension services. The proposed framework is built upon predictive excellence, interpretable decision making, and real-world usability in one seamless pipeline, which is critical for advancing precision agriculture.

• A MOGA-based framework is proposed for healthcare
fraud detection, which simultaneously optimizes classifi
cation performance and feature subset size.
• The framework integrates LIME to enable instance-level
interpretability, facilitating a deeper understanding of
model predictions.
• A comprehensive comparative analysis is conducted to
evaluate model performance and interpretability before
and after feature selection.