Accurate and interpretable classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. This study proposes a hybrid deep learning model that integrates convolutional neural networks (CNNs) with a Vision Transformer (ViT) to jointly capture local texture patterns and global contextual features. The fusion architecture is evaluated on two publicly available datasets: BreakHis and the invasive ductal carcinoma (IDC) dataset. Results demonstrate that the ViT+CNN model consistently outperforms standalone CNN and ViT models, achieving state-of-the-art accuracy while maintaining robustness across datasets. To assess the feasibility of deployment in real-world clinical scenarios, we benchmark inference latency and memory usage under both standard and edge-constrained environments. Although the fusion model has higher computational cost, its latency remains within acceptable thresholds for real-time diagnostic workflows. Furthermore, we enhance interpretability by combining Grad-CAM with attention rollout, allowing for transparent visual explanation of the model’s decisions. The findings support the clinical potential of hybrid transformer-convolutional models for scalable, reliable, and explainable medical image analysis.

Latest developments in Artificial Intelligence (AI) and big data gave rise to Artificial Intelligent agents like Open AI's ChatGPT, which has recently become the fastest growing application since Facebook and WhatsApp. ChatGPT has demonstrated its ability to impact students' classroom learning experience and exam outcomes. However, there is evidence that ChatGPT provides biased and erroneous information, yet students use ChatGPT in academic tasks. Therefore, an accurate understanding of ChatGPT user perception is crucial. This study has analyzed 247 Reddit top posts related to the educational use of ChatGPT from a prominent subreddit called "ChatGPT" for user perception analysis. Descriptive statistics, sentiment analysis using NLP techniques, and LDA topic modeling were used for analysis to gather a contextual understanding of the data. Results show that the majority of the users took a neutral viewpoint. However, there was more positive perception than negative regarding the usefulness of ChatGPT in education.