Preface xxi 1 Comparative Analysis of Secure Multi-Party Techniques in the Cloud 1 Janak Dhokrat, Namita Pulgam, Tabassum Maktum and Vanita Mane 1.1 Introduction 2 1.2 Related Work 5 1.3 Comparative Analysis 9 1.4 Summary 11 1.5 Conclusion 16 1.6 Compliance with Ethical Standards 17 2 Exploring the Role of Mathematics in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) Applications 21 R. Venkatesh 2.

1 Introduction to Mathematics in Artificial Intelligence 23 2.2 Mathematical Foundations of AI 29 2.3 Advanced Mathematical Techniques in Machine Learning 33 2.4 Applications of Mathematics in Deep Learning 41 2.5 Future Directions and Challenges 47 2.6 Conclusion 53 3 ChatGPT as Rough Set Model Bridging Conversation Gap and Uncertainty 57 Anshit Mukerjee, Biswadip Basu Mallik and Sudeshna Das 3.1 Introduction 58 3.2 Literature Review 58 3.

3 Methodology 62 3.4 Results 68 3.5 Discussions 76 3.6 Conclusion and Future Works 77 4 Simulating M/G/1 Queuing Network with Time-Varying Arrival Rates and Server Failure Using Python Programming 81 Sreelekha Menon, Surya K.A. and Reshma R. 4.1 Introduction 82 4.

2 Methodology 84 4.3 Numerical Example 86 4.4 Python Code 89 4.5 Negative Arrivals 89 4.6 Conclusion 89 5 A Technique of Watermarking Using DGT and DCT 91 Narendrakumar R. Dasre and Pritam Gujarathi 5.1 Introduction 91 5.2 Proposed Algorithm Using DGT and DCT 93 5.

3 Experimental Results 95 5.4 Statistical Analysis 99 5.5 Conclusion 113 6 Performance and Economic Study of an Impatient Consumer Queue with Working Vacations, Secondary Service and Server Failures 117 K. Jyothsna, P. Vijaya Kumar and P. Vijaya Laxmi 6.1 Introduction 118 6.2 Model Overview 121 6.

3 Steady-State Analysis 122 6.4 Performance Characteristics 125 6.5 Sensitivity Analysis 128 6.6 Conclusion 135 7 Optimal Strategies for Multi-Item Stochastic Inventory Model for Convertible Items 139 Mamta Keswani and Uttam Kumar Khedlekar 7.1 Introduction 139 7.2 Literature Survey 142 7.3 Problem Statement 144 7.4 Assumptions 145 7.

5 Notations 146 7.6 Model Formulation 147 7.7 Optimization by Using Dynamic Programming 148 7.8 Numerical Validations 158 7.9 Conclusion 163 8 Sampling Statistics-Based Predictive Machine Learning Model for Large Scale Data Set 165 Kamlesh Kumar Pandey, Anurag Singh and Sudeep Kumar Verma 8.1 Introduction 166 8.2 Challenging Issues of Big Data for Machine Learning 168 8.3 Big Data Strategies for Machine Learning 170 8.

4 Sampling 172 8.5 Sampling Model for Machine Learning 180 8.6 Experimental Analysis 184 8.7 Conclusion 188 9 Correlation of Family History with Tumor Grade and Lymph Node Involvement in Breast Cancer Patients 195 Suganthi P. and Ebenesar Anna Bagyam J. 9.1 Introduction 196 9.2 Literature Review 197 9.

3 Methodology 201 9.4 Data Collection and Analysis of Parameters 201 9.5 Analysis of Parameters Using Statistical Tool 206 9.6 Conclusion 209 10 Unlocking AI, ML, and DL Innovations: "The Essential Role of Mathematics" 211 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 212 10.2 Linear Algebra 215 10.3 Calculus: Foundations for Optimization and Training Algorithms 221 10.4 Probability and Statistics: Analyzing and Validating Models 225 10.5 Optimization: Refining Models and Resource Allocation 230 10.

6 Discrete Mathematics: Graph Theory and Combinatorics in AI 235 10.7 Information Theory: Guiding Feature Selection and Model Evaluation 240 10.8 Applications in Various Domains 243 10.9 Conclusion 245 11 Optimization and Metaheuristics: Mathematical Approaches in AI, Machine Learning, and Deep Learning 247 C. Saranya Jothi, J.P. Shritharanyaa, E. Surya, R.

Roselinkiruba, P. Jeevanasree and B. Lalitha 11.1 Introduction to Metaheuristics and Optimization 248 11.2 Metaheuristics Algorithms and Their Mathematical Foundations 252 11.3 Applications in Artificial Intelligence 263 11.4 Metaheuristics in Machine Learning Applications 266 11.5 Metaheuristics in Deep Learning Applications 268 11.

6 Challenges and Future Directions 271 11.7 Conclusions 272 12 A Survey on Mathematics for Edge Detection Algorithms in Image Processing 275 Maheshkumar D. Kudre, Narendrakumar R. Dasre and Pritam Wani 12.1 Introduction 275 12.2 Literature Review 276 12.3 Conclusion 286 13 PUF Inspired AES Cryptosystem for Securing Information 289 Sivasankari Narasimhan, Sumathy Raju, Kavya Sri and Anitha N. 13.

1 Introduction 290 13.2 Related Works 291 13.3 Proposed PUF with AES Approach 292 13.4 Simulation Results and Discussion 296 13.5 AES-PUF Against Machine Learning Attacks 302 13.6 Conclusion 303 14 Leveraging Honeypots and Stochastic Gradient Descent for Advanced Cybersecurity 305 J. Esther, Regi Anbumozhi and S. Subbulakshmi 14.

1 Introduction 306 14.2 Literature Review 308 14.3 Methodology 309 14.4 Result & Discussion 315 14.5 Conclusion 319 15 Review of "Optimizing Peer Review Workflows with AI: A Queuing Model Approach" 321 Sreelekha Menon, Reshma R. and Surya K.A. 15.

1 Introduction 322 15.2 Queuing Models to Analyze the Impact of AI Peer Review Process 323 15.3 Methodology 324 15.3.4 Challenges and Drawbacks 326 15.4 Conclusion 327 16 To Analyze the Success of Prostate Cancer Prediction Using Machine Learning 329 Bhaskar Nandi, Soumit Chowdhury, Subrata Jana, Biswadip Basu Mallik, Krishna Pada Das and Sudipta Banerjee 16.1 Introduction 330 16.2 Literature Review 331 16.

3 Objectives 334 16.4 Hypothesis 334 16.5 Attributes 335 16.6 Flow Chart and Data Description 336 16.7 Data Analysis 337 16.8 Model Evaluation 344 16.9 Result Analysis 350 16.10 Conclusions 352 17 Statistics in Data Science 357 Nishant Wanjari, Aashka Gupta, Reshma Gulwani and Aditi Chhabria 17.

1 Introduction to Statistics 358 17.2 Relationships between Data Science and Statistics 360 17.3 Correlation and Covariance 363 17.4 Regression Analysis 364 17.5 Probability and Probability Functions 365 17.6 Bayesian Statistics 368 17.7 Hypothesis Testing 368 17.8 Statistics in Predictive Modeling 371 17.

9 Statistics Meets Computation to Form Data Science 373 17.10 Statistics Applications in Data Science 375 17.11 Statistical Software and Packages in Data Science 377 18 Frames for Applications in Engineering 381 Jamkhongam Touthang 18.1 Introduction 382 18.2 Finite Frames 384 18.3 Frames in Infinite-Dimensional Settings 390 18.4 Applications 400 18.5 Challenges in Signal Processing 407 19 Development and Optimization of an ADRC-Controlled IPMC Actuator for Enhanced Disturbance Rejection and Creep Compensation 415 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 416 19.2 Ionic Polymer Metal Composites Creep Model 417 19.3 Design of the ADRC Controller 419 19.4 ADRC Controller Parameters Can Be Changed Using the Particle Swarm Optimization Method 424 19.5 Conclusions 432 20 Industry 4.0: Revolutionizing Production through Cyber-Physical Systems 437 Girisha L., Meinathan S.

, Ravivarman G., Girimurugan R., Irudhayamary Premkumar, Catherene Julie Aarthy C. and Biswadip Basu Mallik 20.1 Introduction 438 20.2 A Case Study 440 20.3 Infrastructure Requirements 442 20.4 Legal Issues and Cyber-Security 445 20.

5 Development of New Business Models 448 20.6 Challenges to Achieve Sustainable Development 451 20.7 Conclusions 453 21 Safeguarding Security and Privacy in the Business Sector: The Role of AI and ML 459 Joshua Bapu J., Saranya N., Chacko Jose P., Suresh Kumar K., Jayachandran T., Girimurugan R.

and Biswadip Basu Mallik 21.1 Introduction 460 21.2 An Ethical Investigation into Cyber Security 462 21.3 Literature Review 463 21.4 Artificial Intelligence Cybersecurity as a Business Ethics Duty 466 21.5 Using AI and Big Language Models in Business Settings: Falling for the Marketing Hype 470 21.6 Risk Considerations for Cyber Security in Generative AI and Huge Language Models 471 21.7 Ethical Implications of Generative AI Risk 474 21.

8 Ethical Implementations of Generative AI 479 21.9 Conclusions 480 22 Advancing Women Health: Detecting Polycystic Ovary Syndrome through Machine Learning 485 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 486 22.2 Related Works 488 22.3 Proposed Work 489 22.4 Dataset 491 22.5 Experimental Setup 494 22.

6 Results & Discussion 494 22.7 Conclusions 498 23.