Rajneesh Panwar

The integration of blockchain technology and Internet of Things (IoT) computing has emerged as a promising approach for secure and decentralized data management. Here we explore integrating the blockchain with IoT computing to enhance data security, integrity, and privacy. By leveraging the immutability, transparency, and distributed consensus of blockchain, the study aims to investigate how this integration can overcome the limitations of traditional centralized data management approaches in the IoT ecosystem. The chapter conducts a comprehensive literature review to identify the key advantages and challenges associated with integrating blockchain and IoT computing. It examines real-world use cases, implementation strategies, and the security and privacy considerations involved in the integration. Through this exploration, we uncover these two techniques to foster a new era of data management that prioritizes security, transparency, and autonomy.

The integration of generative artificial intelligence (AI) into metaverse-enabled healthcare industries is catalyzing a profound transformation in the way healthcare is delivered, experienced, and optimized. This chapter explores the multifaceted impact of generative AI within the immersive and interconnected realm of the Metaverse on healthcare systems, services, and patient outcomes. Generative AI, driven by powerful deep learning algorithms and neural networks, is revolutionizing medical data generation, diagnostics, and personalized treatment. This abstract discusses how generative AI-driven avatars and virtual healthcare environments are enhancing patient engagement and facilitating remote consultations, transcending geographical barriers. Moreover, generative AI is pivotal in the creation of lifelike medical simulations and predictive modeling for disease prevention. However, this transformation also raises ethical concerns surrounding data privacy, security, and the potential biases embedded in AI algorithms.

The integration of artificial intelligence (AI) in education is reshaping classroom dynamics and fostering a social impact on teaching and learning. This chapter explores the historical evolution of classroom dynamics, from traditional settings to 21st-century learning environments, highlighting the pivotal role of AI in this transformative journey. Understanding AI in education involves delving into its historical overview and current landscape. The research investigates AI's influence on teacher support and development, encompassing AI-assisted lesson planning, teacher training, and enhanced classroom management. The conclusion reflects on the future directions and trends, emphasizing the global potential of AI to democratize education, address language barriers, and provide inclusive learning experiences. The study envisions a future where AI optimally contributes to creating a more equitable, interconnected, and culturally sensitive educational landscape globally.

The internet of medical things (IoMT) has emerged as a transformative force in the intersection of healthcare and smart cities. This chapter delves into the scaling challenges, smart healthcare applications, and the critical aspect of data integrity within the IoMT framework in the context of smart cities. As connected medical devices become integral components of urban health ecosystems, ensuring scalability while maintaining data integrity becomes paramount. The chapter explores the evolving landscape of IoMT in smart cities, emphasizing challenges associated with device proliferation, interoperability, and the dynamic nature of healthcare data. Additionally, it highlights innovative smart healthcare applications empowered by IoMT and addresses strategies for preserving data integrity to uphold the reliability of healthcare systems in urban environments. Request access from your librarian to read this chapter's full text.

The healthcare industry is undergoing a profound transformation driven by the integration of medical robotics and AI leading to remarkable advancements in diagnosis and treatment. However, as the industry embraces these high-tech innovations, it faces a burgeoning challenge in ensuring the security of medical information throughout its lifecycle. This chapter provides an in-depth analysis of the security challenges in storing and exchanging medical data within this rapidly evolving healthcare landscape, with a specific focus on the emerging role of AI-assisted diagnostics and medical robotics. The utilization of AI in diagnostic procedures offers unprecedented precision and speed, but it also raises significant security concerns. Threats to patient data, confidential records, and the functionality of AI systems have intensified, demanding a robust security framework. This chapter explores the vulnerabilities associated with AI-assisted diagnostics and discusses the potential consequences of security breaches, which may extend to misdiagnoses and harmful interference in robotic surgeries. Request access from your librarian to read this chapter's full text.

This chapter explores waste-to-energy (WtE) solutions empowered by the integration of internet of things (IoT) and machine learning (ML) for sustainable power generation in smart cities. By leveraging IoT sensors, real-time data acquisition optimizes waste management processes, and ML algorithms enhance operational efficiency. The potential impact of these technologies on WtE's future includes predictive maintenance, waste sorting automation, and adaptive energy production. The role of WtE in smart cities extends to decentralized energy generation, integrated waste management, and fostering circular economy principles. This study calls for further research and the adoption of sustainable practices to propel WtE as a key component in the future energy landscape of smart and resilient urban environments.

In recent years, we have globally impacted with the COVID-19 which was very uncommon and highly contagious and dangerous respiratory illness, demanding early detection for effective containment and further spread. In this research we proposed an innovative methodology that utilizes images of X-Ray for early prediction and early detection of COVID-19. By employing transfer learning with a pretrained neural network, coupled with the Nearest Neighbour method for optimization and processing of data, the methodology achieves a remarkable accuracy of 96.37%. This accuracy surpasses other available model based on different deep learning models, like ResNet-50, AlexNet & VGG for COVID-19 identification & detection using X-ray images. Use of X-ray images are now preferably used modality for identification & detection of COVID-19, given its widespread utilization and effectiveness. However, manual treatment & examination using X-ray images is very challenging, specifically in the field which is facing a limitation of skilled medical staff. The application of deep learning models has shown promise and effective results in automating the diagnosis for early identification of COVID-19 with help of images of X-ray. Overall aim of our study is to design a computer aided convolutional neural network for early detection and identification of COVID-19. The suggested architecture, Covid-R, is specifically developed for prediction and analysis for COVID-19 cases using X-ray images. The designed model was trained using a combination of publicly available COVID-19 image datasets and X-ray images directly collected from hospitals. These images comprise a mix of patients exhibiting both COVID-19 and non- COVID-19 symptoms. It firmly believes that this study holds significant potential in alleviating the workload of frontline radiologists, expediting patient diagnosis and treatment, and facilitating pandemic control efforts.

This book chapter explores the crucial aspects of security and privacy considerations in cloud-based data processing solutions for sensitive data. As organizations increasingly leverage cloud computing for their data processing needs, concerns regarding the protection of sensitive information have become paramount. The chapter discusses the challenges and potential threats associated with cloud-based data processing, highlighting the importance of implementing robust security measures to safeguard sensitive data. The chapter delves into various security and privacy considerations that must be addressed when adopting cloud-based data processing solutions. It covers topics such as data encryption, access control mechanisms, secure data transmission, and secure storage. Additionally, it examines the role of authentication and authorization mechanisms, as well as the importance of auditing and monitoring activities to ensure compliance with data protection regulations.

This chapter explores the transformative potential of integrating computer vision, artificial intelligence (AI), and the Internet of Things (IoT) in healthcare. We examine the opportunities and challenges of implementing these technologies in healthcare settings, including their potential to improve patient outcomes, enhance healthcare delivery, and reduce costs. We also consider the ethical and regulatory considerations that must be addressed when deploying these technologies, including privacy concerns and issues related to data ownership and control. Finally, we outline future directions for research and development in this area, including the need for interdisciplinary collaboration between computer science, healthcare, and regulatory experts to realize the full potential of these transformative technologies. Computer vision, AI, and IoT are rapidly evolving fields that have the potential to revolutionize healthcare. By leveraging these technologies, healthcare providers can collect and analyze vast amounts of data, enabling them to identify patterns and make more informed decisions about patient care. This can lead to earlier and more accurate diagnoses, more personalized treatment plans, and better patient outcomes. However, there are also significant challenges associated with the integration of these technologies in healthcare. For example, there are concerns about the accuracy and reliability of AI algorithms, as well as the potential for bias in their decision-making processes. Additionally, there are regulatory and ethical considerations related to data privacy, ownership, and control that must be addressed to ensure that these technologies are deployed in a responsible and ethical manner. Despite these challenges, the potential benefits of integrating computer vision, AI, and IoT in healthcare are too great to ignore. In the future, we can expect to see continued advances in these technologies, along with increased interdisciplinary collaboration between computer scientists, healthcare providers, and regulatory experts. By working together, we can unlock the full transformative potential of these technologies and improve healthcare outcomes for patients around the world. One promising area of application for computer vision, AI, and IoT in healthcare is remote patient monitoring. By using wearable devices and sensors, healthcare providers can collect real-time data on patient health and behavior, allowing them to detect and respond to potential issues before they become serious. This can lead to improved patient outcomes and reduced healthcare costs by avoiding unnecessary hospitalizations and emergency room visits. Another area of potential application is drug discovery and development. By using AI to analyze large datasets, researchers can identify promising drug candidates more quickly and accurately than traditional methods. This can lead to faster development of new drugs and therapies and ultimately improve patient outcomes. Overall, the integration of computer vision, AI, and IoT in healthcare holds great promise for improving patient outcomes, enhancing healthcare delivery, and reducing costs. However, realizing this potential will require continued investment in research and development, as well as careful attention to ethical and regulatory considerations. By working together, researchers, healthcare providers, and policymakers can ensure that these transformative technologies are deployed in a responsible and effective manner.

The integration of artificial intelligence and the internet of things (AIoT) in education has ushered in a paradigm shift, transforming traditional learning environments and revolutionizing educational technology. This chapter explores the multifaceted impact of AIoT on education, encompassing personalized learning experiences, intelligent tutoring systems, and the creation of smart educational spaces. By leveraging AI algorithms and IoT-connected devices, educators can tailor instructional content to individual student needs, fostering a dynamic and adaptive learning environment. The interconnectivity of devices enables real-time data collection, facilitating insights into student performance, engagement, and preferences. Moreover, AIoT contributes to the development of smart classrooms equipped with interactive technologies, enhancing collaboration and active participation. As we navigate this transformative era, the potential of AIoT in education holds promise for optimizing educational outcomes and shaping a future where technology seamlessly integrates with the learning experience.