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.

The need for intelligent and adaptive cyber security solutions is critical due to the ever evolving and complex nature of cyber threats. This monograph reveals the possibilities offered by generative AI models in cybersecurity, particularly in the areas of threat simulation and anomaly detection. In detail, it provides an overview of the present threat landscape and describes how generative models like GANs, VAEs, and Transformers can be used to perform sophisticated emulation, training data synthesis, real-time anomalous behavior detection, and attack detection. The work discusses also explores the design systems, methodologies, and ethics of generative AI model training that define its trustworthiness and governance. With the aid of interdisciplinary case studies and synthesis approaches, the monograph underscores the advanced potentials along with emerging vulnerabilities of employing generative AI in cyber defense operations. I hope this work becomes a starting point for researchers, practitioners, and strategists seeking to understand and aid in intelligent cyber defense leveraging AI.

Keywords: Generative AI, Cybersecurity, Threat Simulation, Anomaly Detection, GANs, VAEs, Transformers, Synthetic Data, Adversarial Attacks, Cyber Threat Intelligence, Behavioral Analysis, AI Ethics, Real-Time Monitoring, Deep Learning, Cyber Defense

How to Cite this Monograph:

Mathivilasini, S., Sridevi, D., & Anandapriya, B. (2025). Generative AI for Cybersecurity: Threat Simulation and Anomaly Detection. Jupiter Publications Consortium. ISBN: 978-93-86388-79-7. DOI: https://www.doi.org/10.47715/978-93-86388-79-7

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.

This monograph provides a comprehensive exploration of deep learning, a transformative technology in the field of artificial intelligence. Beginning with an introduction to the background of machine learning and the rise of deep learning, the text delves into the historical evolution of artificial intelligence and neural networks. It offers a detailed examination of the foundations of deep learning, including neural network basics, architecture, activation functions, and essential techniques like backpropagation and gradient descent. The work then progresses to advanced neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms, including transformers. A significant focus is placed on practical aspects, like training deep models, regularization techniques, optimization strategies, and the utilization of transfer learning with pre-trained models. The monograph also explores the diverse applications of deep learning in fields such as image recognition, natural language processing, and autonomous systems, while addressing the challenges of overfitting, interpretability, and ethical considerations. Looking forward, it examines emerging trends like quantum neural networks, neural architecture search, and the intersection of neuroscience and deep learning. The text concludes with insightful case studies, reflecting on real-world implementations and milestones in deep learning. This work serves as a valuable resource for understanding the current state of deep learning, its practical applications, and future directions.

Keywords: Deep Learning, Machine Learning, Artificial Intelligence, Neural Networks, Backpropagation, Gradient Descent, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Training Models, Regularization, Optimization, Transfer Learning, Image Recognition, Natural Language Processing, Autonomous Systems, Overfitting, Interpretability, Ethical Considerations, Quantum Neural Networks, Neural Architecture Search, Neuroscience.