Description
Book Details
Title: Mathematics and Statistics in the Era of Artificial Intelligence and Data Analytics
Type: Peer Reviewed Book
Editors: Dr. L. Sivakami and Dr. S. Lakshmipriya
Publisher: Cogniverse Press, Jorhat, Assam, India
First Edition: August 2026
ISBN: 978-81-68073-07-4 (Print Edition)
e-ISBN: 978-81-68073-08-1 (Digital Edition)
DOI: https://doi.org/10.5281/zenodo.22070466
Published By: Cogniverse Press, Nakari Gaon, Borigaon Siding, Jorhat – 1, Assam, India
Cover Designing: Cogniverse Press Digital Team
Editors
- Dr. L. Sivakami – Associate Professor & Head, Department of Mathematics & Statistics, Faculty of Science & Humanities, SRM Institute of Science & Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India
- Dr. S. Lakshmipriya – Assistant Professor Grade I, Department of Mathematics and Statistics, Faculty of Science and Humanities, SRM Institute of Science & Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India
Preface
Mathematics and Statistics in the Era of Artificial Intelligence and Data Analytics presents a comprehensive collection of contemporary research at the intersection of mathematics, statistics, artificial intelligence, and data analytics.
The volume addresses Artificial Intelligence and Computational Intelligence, including neural networks, deep learning, fuzzy logic, intelligent systems, evolutionary computing, swarm intelligence, and reinforcement learning. These areas demonstrate the advancement of intelligent computing and its applications in industrial automation, robotics, healthcare diagnostics, autonomous systems, and digital transformation.
The book also discusses Data Analytics and Big Data Technologies, with emphasis on data preprocessing, data quality assessment, exploratory data analysis, big data analytics, visualization techniques, and interpretation of complex datasets. These methodologies support the transformation of raw data into actionable knowledge across diverse application domains.
Several chapters explore Mathematical and Statistical Modeling in AI Applications, covering areas such as healthcare analytics, financial modeling, engineering optimization, industrial applications, environmental sustainability, and climate data analytics.
Emerging research directions are represented through topics including Explainable Artificial Intelligence (XAI), quantum computing, graph theory, network analytics, and large language models. The volume also addresses Ethics, Security, and Responsible AI, including privacy-preserving data analytics, fairness, transparency, bias mitigation, governance frameworks, and ethical considerations in AI-driven systems.
The book concludes with perspectives on Future Directions and Emerging Research, including AI-driven scientific discovery, mathematical challenges in next-generation artificial intelligence, interdisciplinary research opportunities, and future trends in data analytics and intelligent systems.
The editors believe that this volume will serve as a valuable reference for researchers, faculty members, postgraduate students, doctoral scholars, industry professionals, and policymakers working across mathematics, statistics, computer science, artificial intelligence, data science, engineering, economics, healthcare, and related interdisciplinary fields.
The editors sincerely thank all contributing authors, reviewers, the publisher, colleagues, research collaborators, institutions, and family members for their valuable support in preparing this volume.
Dr. L. Sivakami
Dr. S. Lakshmipriya
Editors
Key Themes
- Mathematical foundations of artificial intelligence
- Artificial intelligence and computational intelligence
- Machine learning and mathematical frameworks
- Graph theory, domination, and graph coloring
- Data analytics and big data technologies
- Explainable Artificial Intelligence and trustworthy AI
- AI applications in cybersecurity and finance
- Mathematical and statistical modeling
- Healthcare analytics and intelligent prediction
- Optimization algorithms for machine learning
- Advanced AI and data science
- Emerging research in AI and interdisciplinary applications
Table of Contents
Chapter 1. A Hybrid Adaptive Parameter – Robust Numerical Framework for Singularly Perturbed Fractional Convection – Diffusion Equations Involving the Caputo – Katugampola Fractional Derivative
Authors: J. Christ Jennifer, Dr. S. Sendhamizh Selvi
Page: 1
Chapter 2. Location 2 Domination Number in Acyclic Graph
Author: Venkatesan A
Page: 16
Chapter 3. Graph Domination in Artificial Intelligence and Network Analytics: Mathematical Concepts and Emerging Applications
Authors: Siddharthan Rajeshkanna, Jenitha Ganesan
Page: 25
Chapter 4. Mathematical Foundations of Artificial Intelligence: Models, Methods, and Applications
Authors: Abirami Muthukumarasamy Karuppavelu, Jenitha Ganesan
Page: 39
Chapter 5. Graph Coloring Algorithms and Their Applications in Artificial Intelligence and Data Analytics
Authors: Keerthana E, Jenitha G
Page: 52
Chapter 6. Machine Learning Approaches for Hemodynamic Analysis of Non-Newtonian Blood Flow
Authors: Yokeshwari Ravi, Balaganesan Palanivelu
Page: 66
Chapter 7. Convergence-Guaranteed Optimization Algorithms for Machine Learning: A Regularized Gradient Perspective
Author: Santosh Kumar
Page: 79
Chapter 8. Explainable Artificial Intelligence (XAI) in Cybersecurity: Enhancing Trust, Transparency, and Intelligent Threat Detection
Authors: Dr. S. Jayasree, Dr. T. Sujatha Jayakrishnan, Dr. M. Kannan
Page: 104
Chapter 9. Human-Centered Explainable Artificial Intelligence: Designing Trustworthy, Ethical and User-Centric AI Systems
Authors: Dr. T. Sujatha Jayakrishnan, Dr. S. Jayasree, Dr. M. Kannan
Page: 129
Chapter 10. Explainable Artificial Intelligence in Finance: Enhancing Transparency, Trust, and Responsible Decision-Making
Authors: Dr. M. Kannan, Dr. K.R. Ananthapadmanaban, Dr. S. Jayasree, Dr. T. Sujatha Jayakrishnan
Page: 152
Chapter 11. Machine Learning & Mathematical Frameworks
Authors: S. D. Bhourgunde, M. G. Shrigan
Page: 175
Chapter 12. Advanced Topics in AI & Data Science
Authors: M. G. Shrigan, S. D. Bhourgunde
Page: 192
Chapter 13. A Stability-Guaranteed Micro-Movement Feature Framework for Early Parkinson’s Disease Prediction Using Spiral and Wave Drawings
Author: S. Thalapathiraj
Page: 217
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