The dining sector in developing countries faces numerous challenges, including inefficiencies in order handling, resource management, and ensuring food quality and customer privacy. Traditional methods often lead to delays, errors, and dissatisfaction. This paper proposes a quick-witted, intelligent order-handling system utilizing the Internet of Things (IoT) to address these challenges and enhance the overall dining experience. We present a comprehensive approach to developing and implementing an IoT-based automated order-handling system tailored to restaurants' specific needs and challenges in developing countries, highlighting the importance of technology in enhancing operational efficiency and customer satisfaction. The proposed automated secure order-handling system using IoT demonstrates significant potential for improving efficiency and customer satisfaction in the dining sector. By addressing common problems through advanced technology, this system offers a sustainable solution that enhances the dining experience while ensuring food orders' validity, quality, and privacy. We analyzed the potential impact of implementing such a system in developing countries, focusing on economic and operational benefits.

Early and accurate detection of skin cancer, particularly melanoma, is critical for improving survival rates and treatment outcomes. This study introduces SkinScanNet, a CNN-based deep learning model that classifies skin lesions into benign and malignant categories. The model demonstrates robust performance, achieving a test accuracy of around 94%, with precision, recall, and F1-scores of 95.45%, 89.94%, and 92.56%, respectively. Gradient-weighted class activation mapping (Grad-CAM++), an explainable artificial intelligence (XAI) technique, is integrated into the model to generate class-discriminative heatmaps, highlighting key regions influencing predictions. This enhances transparency, enabling clinicians to validate and interpret model decisions. The system addresses key challenges in artificial intelligence (AI)-driven diagnostics by combining high accuracy with interpretability, offering a reliable and explainable tool for real-world clinical applications. Future directions include expanding dataset diversity and optimizing the model for broader generalizability and reduced false negatives.

Heart attacks continue to be one of the primary causes of death globally, highlighting the critical need for advanced predictive models to improve early diagnosis and timely intervention. This study presents a comprehensive machine learning (ML) approach to heart attack prediction, integrating multiple datasets from diverse sources to construct a robust and accurate predictive model. The research employs a stacking ensemble model, which combines the strengths of individual ML algorithms to improve overall performance. Extensive data preprocessing steps were carefully undertaken to preserve the dataset's integrity and maintain its quality. The results demonstrate a superior accuracy of 97.48%, significantly outperforming state-of-the-art approaches. The high level of accuracy indicates the model’s potential effectiveness in the clinical setting for early detection of heart attack and prevention. However, the proposed model is influenced by the quality and diversity of the integrated datasets, which could affect its generalizability across broader populations. Challenges encountered during the model's development include optimizing hyperparameters for multiple classifiers, ensuring data preprocessing consistency, and balancing computational efficiency with model interpretability. The results underscore the pivotal contribution of advanced ML approaches in revolutionizing the management of cardiovascular attack. By addressing the complexities and variabilities inherent in heart attack prediction, the work provides a pathway towards more effective and personalized cardiovascular disease management strategies, demonstrating the transformative potential of ML in healthcare.

Research on methods that use images to identify and categorize plant diseases is expanding as a field of study. These programs help find plant illnesses as they happen. Fungal, bacterial, and viral infections are the most harmful to plants. Five major tomato diseases have been classified in this study: Bacterial Spot, Black Mold, Gray Spot, Late Blight, Powdery Mildew, and healthy tomato plant leaf. The classification is done by extracting color, shape, and texture features from healthy and unhealthy tomato plant images. Following the segmentation phase, the feature extraction method is carried out. Features extracted from segmented pictures are used as inputs to a classification algorithm. These five categories were used to finalize the illness categorization system. The variety of five kinds of tomato images yielded an overall 98% classification accuracy. Furthermore, various research gaps have been identified to achieve a more open approach to detecting tomato diseases.