Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future DirectionsКНИГИ » СЕТЕВЫЕ ТЕХНОЛОГИИ
Название: Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions Автор: Shashi Kant Gupta, Joanna Rosak-Szyrocka, Amit Mittal, Sanjay Kumar Singh, Olena Hrybiuk Издательство: Bentham Books Год: 2025 Страниц: 336 Язык: английский Формат: pdf (true), epub Размер: 28.8 MB
Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions examines cutting-edge applications, from blockchain-powered IoT innovations in healthcare systems to intelligent health profile management, remote patient monitoring, and healthcare credential verification. Additionally, the book extends its insights into blockchain-enabled IoT applications in smart agriculture, highlighting AI-driven technologies for health management and sustainable practices.
With expert analyses, case studies, and practical guidance, this book offers readers a roadmap for implementing these technologies to improve efficiency, security, and data management in healthcare. It is an invaluable resource for industry professionals, researchers, and students interested in the future of healthcare technology.
The interdisciplinary convergence of computer vision and object detection is pivotal for advancing intelligent image analysis. This research surpasses conventional object recognition methodologies by delving into a more nuanced understanding of images, akin to human visual comprehension. It explores deep learning and established object detection systems such as convolutional neural networks (CNN), Region-based CNN (R-CNN), and you only look once (YOLO). The proposed model excels in real-time object recognition, outperforming its predecessors, as previous systems typically detect only a limited number of objects in an image and are most effective at a distance of 5-6 meters. Uniquely, it employs Google Translate for the verbal identification of detected objects, offering a crucial accessibility feature for individuals with visual impairments. This study integrates computer vision, deep learning, and real-time object recognition to enhance visual perception, providing valuable assistance to those facing visual challenges. The proposed method utilizes the Common Objects in Context (COCO) dataset for image comprehension, employing object detection and object tracking with a deep neural network (DNN). The system's output is converted into spoken words through a text-to-speech feature, empowering visually impaired individuals to comprehend their surroundings effectively. The implementation involves key technologies such as NumPy, OpenCV, pyttsx3, PyWin32, OpenCV-contrib-python, and winsound, contributing to a comprehensive system for computer vision and audio processing. Results demonstrate successful execution, with the camera consistently detecting and labeling 5-6 objects in real time.
Key Features: - Exploration of blockchain and IoT applications in healthcare and agriculture - In-depth case studies and expert analyses - Practical insights into technology challenges and benefits
Readership: Ideal for professionals, researchers, and students in healthcare and technology.
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Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions 
