The integration of the Internet of Things (IoT) into healthcare has led to significant advancements in patient monitoring and care through Wireless Body Area Networks (WBANs). However, these innovations also present substantial challenges in ensuring the security and privacy of sensitive medical data. This research explores the various security threats and vulnerabilities associated with WBANs and IoT-based healthcare systems, proposing novel solutions to enhance data protection. The study introduces the ECG Hummingbird Algorithm, designed for secure data transmission in WBANs, and the Modified Feather Lightweight Block (MFLB) Cipher, which offers a lightweight encryption solution suitable for resource-constrained IoT devices. Experimental results demonstrate the effectiveness of these algorithms in mitigating security risks while maintaining system efficiency. This work contributes to the growing field of cybersecurity in healthcare,
offering practical approaches to safeguarding patient information in an increasingly interconnected world.

Keywords: Internet of Things, IoT, healthcare, Wireless Body Area Networks, WBAN, security, privacy, ECG Hummingbird Algorithm,
Modified Feather Lightweight Block Cipher, encryption, data protection.

How to Cite this Monograph:

Sivasangari, A. (2024). Security and Privacy in IoT based Healthcare (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-62-9, DOI: www.doi.org/10.47715/978-93-86388-62-9

“AI and Healthcare: Monograph” presents an in-depth analysis of the integration of Artificial Intelligence (AI) into the healthcare sector. This book offers a comprehensive examination of AI’s role in revolutionizing various aspects of healthcare, from diagnostics and treatment to patient care and medical research. It begins by establishing a foundational understanding of AI and healthcare, including historical perspectives and future trends. The book then delves into the technical aspects of AI, such as machine learning and natural language processing, and their applications in healthcare. Special emphasis is given to personalized medicine, showcasing how AI contributes to advancements in genomic research and tailored treatments. Ethical, privacy, and regulatory considerations form a critical part of the discussion, highlighting the challenges and responsibilities of integrating AI in sensitive and impactful healthcare settings. The book also addresses the limitations and challenges of AI in healthcare, providing a balanced view of its potential and pitfalls. Finally, it looks ahead to the future of AI in healthcare, considering its role in upcoming health crises and its impact on healthcare providers, patients, and policy.

Keywords: AI, Healthcare, Machine Learning, Personalized Medicine, Ethical Considerations, Privacy, Future Trends, Diagnostics, Treatment, Medical Research

The convergence of deep learning and healthcare marks a pivotal moment in the evolution of both fields. “Deep Learning in Healthcare” is a comprehensive exploration of the profound impact of artificial intelligence (AI) and deep learning on healthcare. This monograph delves into fundamental deep learning concepts, practical tools and frameworks, and transformative applications across healthcare domains. From predictive models aiding in disease detection to precise medical image analysis, clinical text data processing, and the integration of wearable devices for remote monitoring, deep learning has revolutionized every facet of healthcare.
However, this transformation is accompanied by challenges related to ethics, privacy, and regulation. Ethical considerations, privacy concerns, and regulatory frameworks are integral to AI and healthcare. Real-world case studies within this monograph provide insights into successful implementations and lessons learned from failed projects, offering guidance, best practices, and recommendations.
In the concluding chapter, we contemplate the road ahead. The future promises boundless innovation, where AI and deep learning continue to enhance medical practice and global well-being. Collaboration, dedication, and a commitment to ethical principles will shape a future where deep learning elevates healthcare.

Keywords: Deep Learning, Healthcare, Artificial Intelligence, Predictive Models, Medical Imaging, Clinical Data, Wearable Devices, Ethical Considerations, Privacy, Regulation, Real-World Applications, Innovation, Collaboration.