The volume begins with a deep learning-based wildlife-poaching detection and monitoring system. This contribution integrates camera traps, drone imagery, acoustic sensing, edge processing, object detection, species recognition, risk mapping, and ranger-response support. The chapter illustrates how artificial intelligence can contribute to wildlife conservation while also considering governance, privacy, ethics, infrastructure limitations, and human involvement.
The second contribution applies artificial intelligence and computer vision to medicinal-plant detection and classification for Ayurvedic applications. Deep-learning techniques, transfer learning, attention mechanisms, and a medicinal-knowledge database are brought together to connect automated plant recognition with traditional healthcare information.
The following chapters broaden the volume into software and commercial applications. An Android-based grocery e-commerce application examines authentication, product management, shopping carts, ordering, digital payments, and cloud-based data management. A full-stack food-delivery platform considers secure payment mechanisms and nutritional analytics, while a SaaS-based project-management and collaboration system demonstrates real-time digital teamwork.
Healthcare forms a substantial part of the collection. YOLO-based brain-tumour detection investigates localization within MRI scans. Another chapter explores explainable deep learning and Grad-CAM for Alzheimer detection using MRI, emphasizing the importance of understanding AI-generated predictions. An intelligent e-healthcare management framework integrates IoT sensing, edge and cloud computing, artificial intelligence, electronic health records, and secure data management. Deep-learning-based diabetic-retinopathy detection further demonstrates the role of computational intelligence in medical-image screening and early diagnosis.
