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Advanced Mathematics in Computing, Communication and Security

Edited by Dipti Jadhav, Pritam Wani, Narendrakumar Dasre, Niranjanamurthy M., and Biswadip Basu Mallik
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394307272  |  Hardcover  |  
572 pages
Price: $225 USD
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One Line Description
Explore the cutting edge of scientific computing with this volume, which provides a comprehensive look at the interdependency between mathematics and computer science.

Description
Within the evolving landscape of computer science, mathematics is increasingly playing a pivotal role. Disciplines like linear algebra, statistics, calculus, and discrete mathematics serve as the cornerstone for comprehension and innovation within various computer science domains. This book underscores the deep-seated interdependency between the realms of mathematics and scientific computing, exploring how each discipline mutually reinforces and advances the other. With its rich theoretical framework and analytical rigor, mathematics provides the bedrock upon which many computational concepts and methodologies are built. In turn, computer science offers a practical avenue for applying mathematical abstractions to tackle real-world problems efficiently and effectively. Cutting-edge technologies, such as scientific computing, deep learning, and computer vision, require not only a mastery of foundational mathematics, but a diverse interdisciplinary approach. This book sheds light on the burgeoning frontiers of computer science, bringing together researchers with expertise across multiple industries, making it an essential resource for beginners and experienced practitioners alike.

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Author / Editor Details
Dipti Jadhav, PhD is an Associate Professor in the Department of Information Technology in the Ramrao Adik Institute of Technology at D.Y. Patil University with more than 18 years of research and teaching experience. She has edited one book, authored more than 30 research papers in international journals and conferences, and holds one Australian and one German patent. Her research focuses on image processing, computer vision, pattern recognition, software engineering, machine learning, and artificial intelligence.

Pritam Wani, PhD is an Assistant Professor of Applied Mathematics at the Ramrao Adik Institute of Technology with more than 20 years of experience. She has authored and reviewed six books, published more than ten research papers in reputed national and international journals, and holds four registered copyrights. Her areas of interest include image processing, topology, number theory, and applied mathematics.

Narendrakumar Dasre, PhD is an Associate Professor of Applied Mathematics at the Ramrao Adik Institute of Technology with more than 21 years of teaching experience. He has authored and reviewed 14 national and international books and published 11 research papers in national and international journals. His areas of interest include image processing, topology, number theory, and applied mathematics.

Niranjanamurthy M., PhD is an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at the BMS Institute of Technology and Management with more than 14 years of experience. He has published more than 25 books and more than 95 articles in various national and international conferences and journals. He has also filed 30 patents, six of which have been granted. His areas of interest include data science, machine learning, e-commerce and m-commerce, software testing and engineering, and cloud computing.

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Table of Contents
Preface
1. Comparative Analysis of Secure Multi-Party Techniques in the Cloud
Janak Dhokra, Namita Pulgam, Tabassum Maktum and Vanita Mane
1.1 Introduction
1.1.1 Traditional Data Sharing Practices
1.1.2 Critical Issues in SMPC
1.2 Related Work
1.3 Comparative Analysis
1.4 Summary
1.5 Conclusion
1.6 Compliance with Ethical Standards
References
2. Exploring the Role of Mathematics in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) Applications
R. Venkatesh
2.1 Introduction to Mathematics in Artificial Intelligence
2.1.1 Historical Overview
2.1.2 Importance of Mathematics in AI, ML and DL
2.1.3 Goals and Content of the Chapter
2.2 Mathematical Foundations of AI
2.2.1 Linear Algebra: Vectors, Matrices for Data Representation and Transformation
2.2.2 Calculus: Optimization, Gradient Learning and Optimization Algorithms
2.2.3 Probability Theory for Modeling Uncertainty and Making Decisions
2.3 Advanced Mathematical Techniques in Machine Learning
2.3.1 Convex Optimization for Model Training and Parameter Estimation
2.3.2 Graph Theory for Representation Learning and Structured Data Analysis
2.3.3 Information Theory for Quantifying and Understanding Uncertainty and Complexity
2.4 Applications of Mathematics in Deep Learning
2.4.1 Deep Neural Networks and their Mathematical Formulations
2.4.2 Reinforcement Learning and Dynamic Programming Principles
2.4.3 Statistical Learning Theory and Generalization Bounds
2.5 Future Directions and Challenges
2.5.1 Emerging Trends in Mathematical AI Research
2.5.2 Challenges and Limitations of Current Approaches
2.5.3 Prospects for Interdisciplinary Collaboration and Innovation
2.6 Conclusion
References
3. ChatGPT as Rough Set Model Bridging Conversation Gap and Uncertainty
Anshit Mukerjee, Biswadip Basu Mallik and Sudeshna Das
3.1 Introduction
3.2 Literature Review
3.3 Methodology
3.3.1 Algorithm
3.3.2 Pseudocode
3.3.3 Working Model
3.4 Results
3.4.1 Empirical Validation
3.4.2 Graphical Representation
3.5 Discussions
3.6 Conclusion and Future Works
References
4. Simulating M/G/1 Queuing Network with Time-Varying Arrival Rates and Server Failure Using Python Programming
Sreelekha Menon, Surya K.A. and Reshma R.
4.1 Introduction
4.2 Methodology
4.3 Numerical Example
4.4 Python Code
4.5 Negative Arrivals
4.6 Conclusion
Bibliography
5. A Technique of Watermarking Using DGT and DCT
Narendrakumar R. Dasre and Pritam Gujarathi
5.1 Introduction
5.2 Proposed Algorithm Using DGT and DCT
5.2.1 Information Encoding Algorithm
5.2.2 Information Decoding Algorithm
5.3 Experimental Results
5.3.1 Encoding and Decoding of the Information
5.3.2 JPEG Compression Attack
5.3.3 BIT Compression Attack
5.3.4 Cropping Attack
5.4 Statistical Analysis
5.4.1 Statistical Analysis for Jpeg Attack
5.4.2 Statistical Analysis for Bit Attack
5.4.3 Statistical Analysis for Cropping Attack
5.5 Conclusion
Acknowledgement
References
6. Performance and Economic Study of an Impatient Consumer Queue with Working Vacations, Secondary Service and Server Failures
K. Jyothsna, P. Vijaya Kumar and P. Vijaya Laxmi
6.1 Introduction
6.1.1 Consumer Support Call Centre
6.2 Model Overview
6.3 Steady-State Analysis
6.4 Performance Characteristics
6.4.1 Economic Study
6.4.2 Cuckoo Search Algorithm
6.5 Sensitivity Analysis
6.6 Conclusion
References
7. Optimal Strategies for Multi-Item Stochastic Inventory Model for Convertible Items
Mamta Keswani and Uttam Kumar Khedlekar
7.1 Introduction
7.2 Literature Survey
7.3 Problem Statement
7.4 Assumptions
7.5 Notations
7.6 Model Formulation
7.7 Optimization by Using Dynamic Programming
7.7.1 Heuristic Approach
7.8 Numerical Validations
7.9 Conclusion
References
8. Sampling Statistics-Based Predictive Machine Learning Model for Large Scale Data Set
Kamlesh Kumar Pandey, Anurag Singh and Sudeep Kumar Verma
8.1 Introduction
8.2 Challenging Issues of Big Data for Machine Learning
8.2.1 Data Challenges
8.2.2 Process Challenges
8.2.3 Management Challenges
8.3 Big Data Strategies for Machine Learning
8.3.1 Divide-and-Conquer
8.3.2 Incremental Learning
8.3.3 Sampling
8.3.4 Granular Computing
8.3.5 Data Summarization
8.3.6 Feature Selection
8.3.7 Condensation
8.3.8 Instance Selection
8.3.9 Parallelization
8.4 Sampling
8.4.1 Uniform Random Sampling
8.4.2 Stratified Sampling (SS)
8.4.3 Systematic Sampling
8.4.4 Reservoir Sampling
8.4.5 Progressive Sampling
8.5 Sampling Model for Machine Learning
8.6 Experimental Analysis
8.6.1 Selected Machine Learning Algorithm
8.6.2 Validation Criteria
8.6.3 Experimental Results and Discussion
8.7 Conclusion
References
9. Correlation of Family History with Tumor Grade and Lymph Node Involvement in Breast Cancer Patients
Suganthi P. and Ebenesar Anna Bagyam J.
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.4 Data Collection and Analysis of Parameters
9.5 Analysis of Parameters Using Statistical Tool
9.6 Conclusion
References
10. Unlocking AI, ML, and DL Innovations: “The Essential Role of Mathematics”
R. Roselinkiruba, Vasumathy M., C.P. Koushik, C. Saranya Jothi, S. Divya and A. Keerthika
10.1 Introduction for the Mathematical Concepts in AI, ML and DL
10.1.1 Applications of Mathematics in AI
10.1.2 Linear Algebra: For the Data Representation
10.2 Linear Algebra
10.2.1 Overview of LA
10.2.2 Impact of Data Representation
10.2.3 Representation of Data in ML Models Using Vectors and Matrices
10.2.4 Use of Linear Algebra in Feedforward Neural Networks, Convolutional Layers
10.3 Calculus: Foundations for Optimization and Training Algorithms
10.3.1 Application in Optimization Techniques: Gradient Descent and Backpropagation
10.3.2 Importance in Training Neural Networks and Tuning Models
10.4 Probability and Statistics: Analyzing and Validating Models
10.4.1 Key Concepts in Probability and Statistics
10.4.2 Importance of Probabilistic Models in Predicting Outcomes and Managing Uncertainty
10.4.3 Statistical Techniques for Hypothesis Testing and Model Validation
10.4.4 Validation Techniques: Cross-Validation, Bootstrapping
10.5 Optimization: Refining Models and Resource Allocation
10.5.1 Overview of Optimization Techniques
10.5.2 Types of Optimization Techniques
10.5.3 Hyperparameter Tuning
10.5.4 Constrained vs Unconstrained
10.5.5 Adaptive Resource Allocation
10.5.6 Optimization in Resource Management
10.5.7 Strategies for Efficient Resource Allocation in AI Systems
10.6 Discrete Mathematics: Graph Theory and Combinatorics in AI
10.6.1 Introduction to Discrete Mathematics and Its Importance
10.6.2 Applications of Graph Theory in Network Analysis and Problem-Solving
10.6.2.1 Use of Graphs in Representing Networks
10.6.2.2 Algorithms for Graph Traversal, Shortest Path, and Network Flow
10.6.3 Future Directions in AI Enabled by Discrete Mathematics
10.6.3.1 The Role of Discrete Mathematics in Improving Algorithmic Efficiency
10.6.3.2 Graph Theory in AI: Neural Networks as Graphs
10.6.3.3 Combinatorics in AI: Solving Optimization Problems
10.7 Information Theory: Guiding Feature Selection and Model Evaluation
10.7.1 Concepts of Entropy, Mutual Information, and Data Compression
10.7.2 Applications in Feature Extraction and Selection
10.7.3 Methods for Evaluating Model Performance Using Information Theory
10.8 Applications in Various Domains
10.9 Conclusion
References
11. Optimization and Metaheuristics: Mathematical Approaches in AI, Machine Learning, and Deep Learning
C. Saranya Jothi, J.P. Shritharanyaa, E. Surya, R. Roselinkiruba, P. Jeevanasree and B. Lalitha
11.1 Introduction to Metaheuristics and Optimization
11.1.1 Overview of Optimization in AI, ML, and DL
11.1.2 Introduction to Metaheuristics
11.1.3 Mathematical Foundations of Metaheuristics
11.2 Metaheuristics Algorithms and Their Mathematical Foundations
11.2.1 Genetic Algorithms (GA)
11.2.2 Particle Swarm Optimization (PSO)
11.2.3 Ant Colony Optimization (ACO)
11.3 Applications in Artificial Intelligence
11.3.1 Hyperparameter Optimization in ML and DL
11.3.2 Feature Selection and Dimensionality Reduction
11.4 Metaheuristics in Machine Learning Applications
11.4.1 Model Selection and Ensemble Methods
11.4.2 Image Processing and Computer Vision
11.5 Metaheuristics in Deep Learning Applications
11.5.1 Optimization in Reinforcement Learning
11.5.2 Function Approximation and Regression
11.5.3 Time Series Prediction
11.6 Challenges and Future Directions
11.6.1 Scalability and Computational Complexity
11.6.2 Integration with Modern AI Techniques
11.7 Conclusions
References
12. A Survey on Mathematics for Edge Detection Algorithms in Image Processing
Maheshkumar D. Kudre, Narendrakumar R. Dasre and Pritam Wani
12.1 Introduction
12.2 Literature Review
12.2.1 Robert Operator
12.2.2 Development of Sobel Operator
12.2.3 Sobel Operator
12.2.4 Prewitt Operator
12.2.5 Marr-Hildreth Operator or Laplacian of Gaussian (LoG) Operator
12.2.6 Canny Operator
12.2.7 Scharr Operator
12.3 Conclusion
References
13. PUF Inspired AES Cryptosystem for Securing Information
Sivasankari Narasimhan, Sumathy Raju, Kavya Sri and Anitha N.
13.1 Introduction
13.2 Related Works
13.3 Proposed PUF with AES Approach
13.3.1 PUF with AES Approach
13.3.2 PUF-AES Message Authentication Codes (MAC) Creation
13.4 Simulation Results and Discussion
13.4.1 PUF Inspired AES Analysis
13.4.2 PUF Inspired MAC Analysis
13.5 AES-PUF Against Machine Learning Attacks
13.6 Conclusion
References
14. Leveraging Honeypots and Stochastic Gradient Descent for Advanced Cybersecurity
J. Esther, Regi Anbumozhi and S. Subbulakshmi
14.1 Introduction
14.2 Literature Review
14.3 Methodology
14.3.1 Initiator
14.3.2 Honeypot Setup in the Cloud Environment
14.3.3 Data Aggregation & Pre-Processing
14.3.4 Model Training with Stochastic Gradient Descent (SGD)
14.3.5 Integration and Deployment
14.4 Result & Discussion
14.4.1 Data Set
14.4.2 Statistical Parameters
14.5 Conclusion
References
15. Review of “Optimizing Peer Review Workflows with AI: A Queuing Model Approach”
Sreelekha Menon, Reshma R. and Surya K.A.
15.1 Introduction
15.1.1 Peer Review Procedure
15.1.2 The Role of Artificial Intelligence in Peer Review Process
15.2 Queuing Models to Analyze the Impact of AI Peer Review Process
15.2.1 Model Parameters
15.3 Methodology
15.3.1 Software Tools
15.3.2 Evaluating AI Accuracy and Impact
15.3.3 Success Probability
15.3.4 Challenges and Drawbacks
15.4 Conclusion
15.4.1 Opportunities for Further Research
References
16. To Analyze the Success of Prostate Cancer Prediction Using Machine Learning
Bhaskar Nandi, Soumit Chowdhury, Subrata Jana, Biswadip Basu Mallik, Krishna Pada Das and Sudipta Banerjee
16.1 Introduction
16.2 Literature Review
16.3 Objectives
16.4 Hypothesis
16.5 Attributes
16.6 Flow Chart and Data Description
16.7 Data Analysis
16.8 Model Evaluation
16.8.1 K-Nearest-Neighbors Algorithm
16.8.1.1 Confusion Matrix
16.8.1.2 Classification Report
16.8.2 Logistic Regression
16.8.2.1 Confusion Matrix
16.8.2.2 Classification Report
16.8.3 SVM
16.8.3.1 Confusion Matrix
16.8.3.2 Classification Report
16.8.4 Decision Tree
16.8.4.1 Confusion Matrix
16.8.4.2 Classification Report
16.8.5 Random Forest
16.8.5.1 Confusion Matrix
16.8.5.2 Classification Report
16.8.6 Naïve-Bayes
16.8.6.1 Confusion Matrix
16.8.6.2 Classification Report
16.9 Result Analysis
16.10 Conclusions
References
17. Statistics in Data Science
Nishant Wanjari, Aashka Gupta, Reshma Gulwani and Aditi Chhabria
17.1 Introduction to Statistics
17.1.1 Descriptive Statistics
17.1.1.1 Measures of Central Tendency
17.1.1.2 Measures of Variability (Dispersion)
17.1.1.3 Measures of Shape
17.1.2 Inferential Statistics
17.2 Relationships between Data Science and Statistics
17.2.1 The Role of Statistics in Data Science
17.3 Correlation and Covariance
17.3.1 Covariance
17.3.2 Correlation
17.4 Regression Analysis
17.4.1 Linear Regression
17.4.2 Multiple Regression
17.4.3 Logistic Regression
17.5 Probability and Probability Functions
17.5.1 Probability Mass Function (PMF)
17.5.2 Probability Density Function (PDF)
17.5.3 Cumulative Distribution Function (CDF)
17.5.4 Empirical Distribution Function (EDF)
17.6 Bayesian Statistics
17.7 Hypothesis Testing
17.8 Statistics in Predictive Modeling
17.8.1 Types of Predictive Models
17.8.2 Types of Predictive Modeling Techniques
17.8.3 Use Case of Predictive Models
17.8.4 Predictive Modeling Roadblocks
17.9 Statistics Meets Computation to Form Data Science
17.9.1 Data Science: Computational Methods
17.10 Statistics Applications in Data Science
17.10.1 Health Care
17.10.2 Financial Industry
17.11 Statistical Software and Packages in Data Science
References
18. Frames for Applications in Engineering
Jamkhongam Touthang
18.1 Introduction
18.2 Finite Frames
18.3 Frames in Infinite-Dimensional Settings
18.3.1 Gabor Frames
18.3.2 Wavelet Frames
18.3.3 Generalized Frames
18.3.4 Fusion Frames
18.3.5 Weaving Frames
18.3.6 K-Frames
18.4 Applications
18.4.1 Machine Learning and Data Analysis
18.4.2 Biomedical Engineering
18.4.3 Structural Health Monitoring (SHM)
18.4.4 Coding Theory
18.4.5 Computer Vision
18.4.6 Multi-Antenna Code Design
18.4.7 Quantum Computing
18.4.8 Sparse Approximation
18.5 Challenges in Signal Processing
18.5.1 Speech and Language Processing
18.5.2 Graph Signal Processing
18.5.3 Machine Learning
18.5.4 Medical Imaging
18.5.5 Wireless Communications
18.5.6 Quantum Signal Processing
18.5.7 Sparse Signal Reconstruction
References
19. Development and Optimization of an ADRC-Controlled IPMC Actuator for Enhanced Disturbance Rejection and Creep Compensation
Mohammed Mohaideen M., Seenivasan S., Ravivarman G., Rangarajan R. V., Naveenkumar P., Sekar G., Balachandar K., Girimurugan R. and Biswadip Basu Mallik
19.1 Introduction
19.2 Ionic Polymer Metal Composites Creep Model
19.3 Design of the ADRC Controller
19.4 ADRC Controller Parameters Can Be Changed Using the Particle Swarm Optimization Method
19.4.1 Simulations and Experiments
19.5 Conclusions
References
20. Industry 4.0: Revolutionizing Production through Cyber-Physical Systems
Girisha L., Meinathan S., Ravivarman G., Girimurugan R., Irudhayamary Premkumar, Catherene Julie Aarthy C. and Biswadip Basu Mallik
20.1 Introduction
20.2 A Case Study
20.3 Infrastructure Requirements
20.3.1 Requirements of the Industry 4.0
20.4 Legal Issues and Cyber-Security
20.4.1 Strategic Principles of Cyber Security
20.4.2 Legal Implications for Industry 4.0
20.5 Development of New Business Models
20.5.1 The Industry 4.0: Features, Challenges, and Requirements for Business Models
20.5.2 Four Approaches: The Digital Transformation of Manufacturing Companies
20.6 Challenges to Achieve Sustainable Development
20.6.1 Sustainable Development Challenges and their Practical Solutions
20.7 Conclusions
References
21. Safeguarding Security and Privacy in the Business Sector: The Role of AI and ML
Joshua Bapu J., Saranya N., Chacko Jose P., Suresh Kumar K., Jayachandran T., Girimurugan R. and Biswadip Basu Mallik
21.1 Introduction
21.2 An Ethical Investigation into Cyber Security
21.3 Literature Review
21.3.1 Artificial Intelligence in Cyber Security
21.3.2 CS of Artificial Intelligence
21.3.3 Risks and Ethical Issues with AI
21.4 Artificial Intelligence Cybersecurity as a Business Ethics Duty
21.4.1 Beneficence
21.4.2 Non-Maleficence
21.4.3 Autonomy
21.4.4 Justice
21.4.5 Explicability
21.5 Using AI and Big Language Models in Business Settings: Falling for the Marketing Hype
21.5.1 AI Adoption Poses a Cyberthreat
21.6 Risk Considerations for Cyber Security in Generative AI and Huge Language Models
21.6.1 Poisoning of Data
21.6.2 Training Data Extraction
21.6.3 Model Back Dooring
21.6.4 Adversarial Prompting
21.7 Ethical Implications of Generative AI Risk
21.7.1 Overreliance
21.7.2 Over-Trust
21.8 Ethical Implementations of Generative AI
21.8.1 An AI Model that is Both Ethical and Secure
21.8.2 Fair and Reliable Data Collection Procedures
21.8.3 A Secure Data Storage
21.8.4 Ethical AI Model Retraining and Maintenance
21.8.5 Upskilling, Training Staff and Managing Staff
21.9 Conclusions
References
22. Advancing Women Health: Detecting Polycystic Ovary Syndrome through Machine Learning
K. DeviPriya, K. V. V. S. Trinadh Naidu, V. Chandra Kumar, Subrata Jana, Biswadip Basu Mallik, K. Bhanu Rajesh Naidu and M. V. Rajesh
22.1 Introduction
22.2 Related Works
22.3 Proposed Work
22.4 Dataset
22.5 Experimental Setup
22.6 Results & Discussion
22.7 Conclusions
References
23. Personalized Hotel Recommendation System Using Similarity Measures and Heuristic Analysis
Prapti Sinha, Rajashree Shedge and Dipti Jadhav
23.1 Introduction
23.2 Background
23.3 Literature Survey
23.4 Dataset
23.5 Proposed Framework
23.5.1 Proposed System Overview
23.5.2 EDA and Text Preprocessing
23.5.3 Methodology
23.5.4 Working
23.5.5 Similarity Measures
23.5.5.1 Cosine Similarity
23.5.5.2 Euclidean Distance
23.5.5.3 Manhattan Similarity
23.5.6 Comparison of Similarity Measures
23.5.6.1 Mean Absolute Error
23.5.6.2 Root Mean Square Error
23.5.6.3 Execution Time
23.6 Result
23.7 Conclusion
References
24. Predictive Structural Equation Modeling for Multi-Dimensional Skill-Development Among Higher Education Learners in Formal Learning Environment
S. Bhuma Devi, Preeti Jain and Gargi Tyagi
24.1 Introduction
24.1.1 Motivation
24.1.2 Research Questions
24.2 Related Work
24.2.1 Research Objective
24.3 Research Methodology
24.3.1 Dataset 1
24.3.2 Dataset 2
24.3.3 Modeling the Data
24.3.3.1 Single Value Decomposition
24.3.3.2 Construction of SVD
24.3.3.3 Calculations of SVD
24.4 Results and Discussion
24.4.1 Discussion for the First Research Question (Significance of Learners’ Attitude and Engagement Towards Their Mathematics-Learning-Environment)
24.4.1.1 Principal Component Analysis
24.4.1.2 K-Nearest Neighbor’s Regression
24.4.2 Discussion for the Second and Third Research Questions (Significance of Educators’ Effect in Up-Skilling the Higher Education Learners)
24.5 Conclusion
References
Index

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