The monograph “AI in Precision Healthcare: A New Frontier” explores the transformative role of Artificial Intelligence in reshaping healthcare through personalization, prediction, and data-driven decision-making. This work offers a comprehensive overview of the integration of AI technologies into various domains of precision medicine, ranging from diagnostics and therapeutics to patient monitoring and chronic disease management. It also examines the convergence of machine learning, deep learning, and big data
analytics with clinical practices to enable individualized treatment strategies. In addition, the book addresses key ethical, legal, and operational challenges such as data privacy, algorithmic bias, and accountability in AI systems. Through real-world applications, conceptual clarity, and multidisciplinary insights, this monograph serves as a vital resource for academicians, practitioners, and policymakers aiming to understand and enhance AI in modern healthcare systems.

Keywords: Artificial Intelligence, Precision Medicine, Machine Learning, Deep Learning, Diagnostics, Predictive Analytics, Personalized Treatment, Clinical Decision Support, Medical Imaging, Health Informatics, Wearable Devices, Data Privacy, Ethical AI, Healthcare Technology, Risk Prediction, Patient Monitoring

How to Cite this Monograph:

APA Style Citation:

Mohan Kumar, D. S., & Balakrishnan, D. G. (2025). AI in Precision Healthcare: A New Frontier (1st ed.). Jupiter Publications Consortium. https://doi.org/10.47715/978-93-86388-50-6

IEEE Style Citation:

D. S. Mohan Kumar and D. G. Balakrishnan, AI in Precision Healthcare: A New Frontier, 1st ed., Chennai, Tamil Nadu, India: Jupiter Publications Consortium, 2025. doi: 10.47715/978-93-86388-50-6.

Chicago Style Citation:

Mohan Kumar, Dr. S., and Dr. G. Balakrishnan. AI in Precision Healthcare: A New Frontier. Chennai, Tamil Nadu, India: Jupiter Publications Consortium, 2025. https://doi.org/10.47715/978-93-86388-50-6.

MLA Style Citation:

Mohan Kumar, S., and G. Balakrishnan. AI in Precision Healthcare: A New Frontier. Jupiter Publications Consortium, 2025. DOI: https://doi.org/10.47715/978-93-86388-50-6.

Author 1:

Prof. (Dr.) S. Mohan Kumar
M.Tech.[Software Engineering]
Ph.D [CSE-Medical Diagnosis CAD System]
Ph.D [Medical Imaging -Machine Learning]
Post Doctorate Degree D.Sc. [Engineering-DL]
EPLM (IIM-Calcutta)
D.Litt (Honorary)
Dean, Indra Ganesan College of Engineering
Tiruchirappalli, Tamil Nadu, India.

Author 2:

Prof. (Dr.) G. Balakrishnan
M.E .[Computer Science And Engineering]
PSG College of Technology, Coimbatore, India
Ph.D [Computer Science And Engineering]
Universiti Malaysia Sabah, Malaysia
Director (IGI) Syndicate Member (Anna University) Principal, Indra Ganesan College of Engineering
Tiruchirappalli, Tamil Nadu, India.

Advancements in Artificial Intelligence (AI), such as machine learning, natural language processing, computer vision, and cloud computing, are presenting new possibilities in information systems technology by improving the management of data, decision-making processes, interaction interfaces, and cybersecurity. With a focus on an optimised AI information system, this monograph studies the evolution of AI information systems. It addresses data processing, decision-making, and cybersecurity AI optimisations. AI-powered personalisation interfaces, AI-powered trends, AI-infused IoT, quantum computing, and the ethics surrounding AI are addressed in the book as well. Profound case studies make the content relatable for numerous industries to aid researchers, academics, and specialists in
understanding information systems intertwined with the intelligence of AI.

Keywords:

Artificial Intelligence, Information Systems, Machine Learning, Data Management, Decision Support Systems, Cybersecurity, Natural Language Processing, Cloud Computing, AI-driven Innovations, Emerging Technologies

How to Cite this Book:

Kanya, N., Rajavarman, V. N., & Pavan, S. (2025). AI-driven innovations in information systems. Jupiter Publications Consortium. https://doi.org/10.47715/978-93-86388-55-1

This book, “Mastering Lecturette, GD & WAT: Essential Techniques for Persuasive Articulation,” is a comprehensive guide aimed at empowering aspirants preparing for the Services Selection Board (SSB) and similar high-stakes assessments. It offers structured techniques and strategies to master critical communication aspects, including Lecturettes, Group Discussions (GD), and the Word Association Test (WAT). The chapters are meticulously organized to address key areas such as impactful openings, motivational transitions, thought fillers, vocabulary enhancement, and effective conclusions. Additionally, the book provides insights into reframing negative words constructively and maintaining composure under pressure. With practical examples, contextual relevance, and actionable tips, this guide equips readers to demonstrate leadership, emotional resilience, and clarity of thought. Designed to inspire confidence and refine articulation skills, it is an indispensable resource for anyone aiming to excel in high-pressure evaluation scenarios.

Keywords: Lecturette, Group Discussion, Word Association Test, SSB preparation, public speaking, articulation skills, leadership qualities, thought fillers, transitions, impactful communication.

Book Citation: Magesh, S., & Martina, E. F. (2025). Mastering Lecturette GD and WAT : Essential Techniques for Persuasive Articulation (1st ed.). Jupiter Publications Consortium, Chennai, Tamil Nadu, India. ISBN: 978-93-86388-89-6, DOI: www.doi.org/10.47715/978-93-86388-89-6

The evolution of search engines from simple keyword-based systems to more sophisticated semantic-based models marks a significant advancement in the field of information retrieval. This monograph presents a comprehensive study on the development of a Semantic-Based Pattern Search Engine, designed to enhance the accuracy and relevance of search results by understanding the contextual and semantic relationships within data. The research encompasses the creation of various engines, including a concept search engine, topic search engine, affective term detection engine, and emotion prediction engine. Additionally, the application of these technologies in online opinion mining is explored, demonstrating their practical value. The findings of this research contribute to the broader understanding of semantic search and offer valuable insights for future advancements in the domain.

Keywords: Semantic-Based Pattern Search Engine, Concept Search Engine, Topic Search Engine, Affective Term Detection, Emotion Prediction, Online Opinion Mining, Information Retrieval, Semantic Search, Contextual Search

How to Cite:

Ajitha, P. (2024). SEMANTIC BASED PATTERN SEARCH ENGINE (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-47-6, DOI: www.doi.org/10.47715/978-93-86388-47-6

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

The monograph “Optimizing Routing Protocols for Energy Conservation in UWSN” explores advanced strategies to address the critical issue of energy efficiency in Underwater Wireless Sensor Networks (UWSNs). These networks have significant potential in various applications, such as aquatic life management, pollution monitoring, and coastal surveillance, but are often limited by high energy consumption. This work proposes innovative routing protocols that aim to conserve energy, extend the operational lifetime of the network, and enhance its reliability. The research encompasses a detailed study of UWSN architecture, acoustic propagation, and specialized routing protocols. The proposed energy-efficient strategies are thoroughly analyzed and validated through extensive experimentation, providing practical solutions and valuable insights for researchers and practitioners. The findings of this monograph contribute to the advancement of sustainable and efficient UWSNs, offering a robust framework for future developments in this field.

Keywords: UWSN, energy efficiency, routing protocols, underwater communication, acoustic propagation, network lifetime, environmental monitoring, sustainable networks

How to cite this Monograph:

Gomathi, R. M. (2024). OPTIMIZING ROUTING PROTOCOLS FOR ENERGY CONSERVATION IN UWSN (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-77-3, DOI: www.doi.org/10.47715/978-93-86388-77-3

This book provides an in-depth exploration of computational data analytics within the broader context of data science. It covers the fundamental concepts, methodologies, and tools that define the field, while also delving into advanced statistical and machine learning techniques tailored for large datasets. The text is structured to facilitate understanding of both theoretical principles and practical applications, bridging the gap between data analysis and real-world challenges. Ethical considerations, privacy, and data governance are emphasized to ensure readers are aware of the responsibilities that come with handling data. Each chapter is enriched with case studies that illustrate the application of computational data analytics in various domains such as healthcare, finance, and environmental studies. The book concludes with a forward-looking discussion on the future of data analytics, highlighting emerging trends and technologies.

Keywords: computational data analytics, data science, machine learning, statistical methods, big data, ethical considerations, real-world applications

Keywords: cybersecurity, artificial intelligence, machine learning, threat intelligence, malware detection, network security, incident response, security analytics, compliance, application security, cloud security.

“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.