Description
Book Details
Title: Mathematical Foundations for Intelligent Engineering Systems
Type: Peer Reviewed Edited Volume
Editor-in-Chief: Dr. Saroj Yadav
Co-Editors: Dr. Anjani Kumar Shukla, Dr. Mahesh Kumar Jayaswal
Publisher: Cogniverse Press, Jorhat, Assam, India
First Edition: June 2026
DOI: 10.5281/zenodo.21128091
ISBN: 978-81-688286-6-7 (Print Edition)
e-ISBN: 978-81-688286-7-4 (Digital Edition)
Cover Designing: Cogniverse Press Digital Team
Copyright: © Authors
Editorial Board
- Editor-in-Chief: Dr. Saroj Yadav – MIT Art Design and Technology University, Pune, India
- Co-Editor: Dr. Anjani Kumar Shukla – Lovely Professional University, Punjab, India
- Co-Editor: Dr. Mahesh Kumar Jayaswal – MIT Art Design and Technology University, Pune, India
Publisher Information
Cogniverse Press
Nakari Gaon, Borigaon Siding, Jorhat – 1, Assam, India
Website: cogniversepress.com
Email: cogniversepress@gmail.com
Preface
Mathematical Foundations for Intelligent Engineering Systems has been developed to bridge the gap between classical mathematical theory and its modern applications in intelligent engineering. The volume presents mathematical concepts in a structured, accessible, and application-oriented manner, enabling students, educators, and researchers to appreciate both the theoretical foundations and their practical significance in emerging technological domains.
Special emphasis is placed on mathematical principles that support artificial intelligence, machine learning, computational intelligence, signal processing, intelligent control systems, and related interdisciplinary fields. The book demonstrates how mathematical reasoning forms the backbone of intelligent engineering solutions and modern computational technologies.
The content has been carefully prepared to meet the needs of undergraduate and postgraduate engineering students, faculty members, researchers, and professionals seeking a comprehensive mathematical reference for intelligent system design. Each chapter develops core mathematical concepts logically while highlighting their relevance through engineering-oriented perspectives and contemporary applications.
The editors hope this volume will serve as a valuable academic resource for learners aiming to build strong mathematical foundations for intelligent engineering applications. A thorough understanding of mathematics empowers engineers and researchers to innovate, adapt, and contribute effectively to the rapidly evolving landscape of intelligent technologies.
Sincere appreciation is extended to all contributors, reviewers, academic colleagues, students, and research scholars whose valuable insights, encouragement, and intellectual curiosity have helped shape this publication.
Dr. Saroj Yadav
Key Themes
- Mathematical Foundations for Engineering
- Artificial Intelligence and Machine Learning
- Computational Intelligence
- Operations Research and Optimization
- Transportation Problems
- Fractional Calculus and Differential Equations
- Neural Networks and Deep Learning
- Inverse Problems and Regularization
- Graph Theory and Automata
- Probability Theory and Stochastic Processes
- Signal Processing
- Intelligent Engineering Systems
Table of Contents
Chapter 1: An Effective Improvement to Vogel’s Approximation Method for Large-Scale Transportation Problems
Authors: Anjani Kumar Shukla, Krishna Kumar
Chapter 2: A Study of Region of Convergence and Growth Aspects of Generalized Dirichlet Series with Positive Exponents Analytic in Right Half Plane
Author: Kirti Chauhan
Chapter 3: Computational Analysis of a Fractional-Order SIR Dengue Fever Model Using the Sumudu Transform Homotopy Perturbation Method
Author: Vinod Gill
Chapter 4: Randers Metric in Exponential Law Cosmology
Authors: Anjani Kumar Shukla, Saroj Yadav
Chapter 5: Neural Networks and Deep Learning: A Theoretical Perspective
Authors: Samuel Shikaa, Chinwe Peace Igiri
Chapter 6: Iterative Regularization and Inverse Problems in Intelligent Engineering Systems
Authors: Ankit Singh, Nikunj Upadhyay, Yogesh Ankush Chavan
Chapter 7: Semigroup and Automata-Based Modeling of Graph Dynamics with Python Implementations
Authors: Mosarof Sarkar, Aviram Panda
Chapter 8: Probability Theory and Stochastic Processes: The Language of Uncertainty in Intelligent Systems
Author: Rohit Raskar
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