Thoracic diseases, including pneumonia, tuberculosis, lung cancer, and others, pose significant health risks and require timely and accurate diagnosis to ensure proper treatment. Thus, in this research, a model for thorax disease classification using Chest X-rays is proposed by considering deep learning model. The input is pre-processed by resizing, normalizing pixel values, and applying data augmentation to address the issue of imbalanced datasets and improve model generalization. Significant features are extracted from the images using an Enhanced Auto-Encoder (EnAE) model, which combines a stacked auto-encoder architecture with an attention module to enhance feature representation and classification accuracy. To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. Finally, the disease classification is performed using the novel Improved Swin Transformer (IMSTrans) model, which is designed to efficiently process high-dimensional medical image data and achieve superior classification performance. The proposed EnAE + ChWO+IMSTrans model for thorax disease classification was evaluated using extensive Chest X-ray datasets and the Lung Disease Dataset. The proposed method demonstrates enhanced Accuracy, Precision, Recall, F-Score, MCC and MAE of 0.964, 0.977, 0.9845, 0.964, 0.9647, and 0.184 respectively indicating the reliable and efficient solution for thorax disease classification.

Crop disease detection plays a crucial role in modern agriculture by enabling timely interventions to address diseases that directly impact crop yields and global food security. This research aims to explore a novel deep-learning approach for detecting crop diseases. We utilize the EfficientNetB0 architecture, specifically designed to extract intricate features from large datasets, making it well-suited for identifying various crop diseases. The model is fine-tuned through transfer learning to adapt to the specific characteristics of crop diseases, enhancing its classification performance. Using a dataset of approximately 87,000 images across 38 classes, our findings demonstrate that this model is highly effective in detecting subtle patterns and features associated with crop diseases. It achieved 99.51% test accuracy with a loss of 0.0165, underscoring its practical efficacy. Such a model could be applied in precision agriculture field monitoring systems, assisting farmers in managing their crops more effectively. Early detection of crop diseases provides an advantage to farmers, allowing them to respond quickly to minimize losses. This not only supports early detection but also contributes directly to sustainable disease management and yield protection. Models like EfficientNetB0 empower farmers to grow crops more efficiently and maximize their yield potential, as the world will need to increase food production by 70% by 2050 to accommodate a growing population. This research underscores the importance of utilizing advanced algorithms based on deep learning to identify crop diseases and highlights the transformative impact of technology on modern agriculture. Integrating the EfficientNetB0 model into agricultural workflows represents a significant step toward building resilient and sustainable food systems.

Accurate and interpretable solar power forecasting is critical for effectively integrating Photo-Voltaic (PV) systems into modern energy infrastructure. This paper introduces a novel two-stage hybrid framework that couples deep learning-based time series prediction with generative Large Language Models (LLMs) to enhance forecast accuracy and model interpretability. At its core, the proposed SolarTrans model leverages a lightweight Transformer-based encoder-decoder architecture tailored for short-term DC power prediction using multivariate inverter and weather data, including irradiance, ambient and module temperatures, and temporal features. Experiments conducted on publicly available datasets from two PV plants over 34 days demonstrate strong predictive performance. The SolarTrans model achieves a Mean Absolute Error (MAE) of 0.0782 and 0.1544, Root Mean Squared Error (RMSE) of 0.1760 and 0.4424, and R² scores of 0.9692 and 0.7956 on Plant 1 and Plant 2, respectively. On the combined dataset, the model yields an MAE of 0.1105, RMSE of 0.3189, and R² of 0.8967. To address the interpretability challenge, we fine-tuned the Flan-T5 model on structured prompts derived from domain-informed templates and forecast outputs. The resulting explanation module achieves ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum scores of 0.7889, 0.7211, 0.7759, and 0.7771, respectively, along with a BLEU score of 0.6558, indicating high-fidelity generation of domain-relevant natural language explanations.

The detection of maize plant leaf diseases is a critical aspect of agricultural management, necessitating accurate and efficient methodologies. The field of maize plant leaf disease detection encounters several challenges that hinder the development of robust and effective solutions. One prominent challenge is the diversity of disease indicators, wherein various pathogens can cause similar symptoms, making it challenging to differentiate between diseases accurately. Additionally, limited annotated datasets pose a constraint, hindering the training of deep learning models and potentially leading to suboptimal generalization. The dynamic nature of plant growth and environmental conditions further complicates disease detection, as the appearance of symptoms may vary at different stages of plant development. Another challenge lies in achieving a balance between accuracy and computational efficiency, especially in real-time applications, as many existing models struggle to provide rapid and precise results simultaneously. This paper addresses prevailing challenges in disease detection by introducing a customized CenterNet approach, incorporating DenseNet-65 as its foundational architecture. Common issues encountered in traditional disease detection models are addressed through our proposed approach, which demonstrates superior performance in both disease classification and precise localization of affected regions. A comparative analysis is conducted against conventional and contemporary methodologies, emphasizing the innovation and competitiveness of our model in advancing maize plant leaf disease detection. We evaluated our approach on a standard dataset CD&S and attained a classification accuracy of 98.62%. This contribution not only expands the current understanding of disease detection in agriculture but also offers a practical and efficient solution for improved maize crop management.