{"product_id":"advanced-mathematics-in-computing-communication-and-security-hardback-9781394307272","title":"Advanced Mathematics in Computing, Communication and Security (Hardback) 9781394307272","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAdvanced Mathematics in Computing, Communication and Security\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eDipti Jadhav (Edited by), Jadha (Author), Pritam Wani (Edited by), Narendrakumar Dasre (Edited by), M. Niranjanamurthy (Edited by), Biswadip Basu Mallik (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394307272, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 10 December 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e576 pages\u003cbr\u003e22.9 x 15.2 x 3.4 cm, 0.975 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eExplore the cutting edge of scientific computing with this volume, which provides a comprehensive look at the interdependency between mathematics and computer science.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eWithin 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Comparative Analysis of Secure Multi-Party Techniques in the Cloud 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJanak Dhokrat, Namita Pulgam, Tabassum Maktum and Vanita Mane\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003cbr\u003e1.2 Related Work 5\u003cbr\u003e1.3 Comparative Analysis 9\u003cbr\u003e1.4 Summary 11\u003cbr\u003e1.5 Conclusion 16\u003cbr\u003e1.6 Compliance with Ethical Standards 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Exploring the Role of Mathematics in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) Applications 21\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. Venkatesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction to Mathematics in Artificial Intelligence 23\u003cbr\u003e2.2 Mathematical Foundations of AI 29\u003cbr\u003e2.3 Advanced Mathematical Techniques in Machine Learning 33\u003cbr\u003e2.4 Applications of Mathematics in Deep Learning 41\u003cbr\u003e2.5 Future Directions and Challenges 47\u003cbr\u003e2.6 Conclusion 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 ChatGPT as Rough Set Model Bridging Conversation Gap and Uncertainty 57\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAnshit Mukerjee, Biswadip Basu Mallik and Sudeshna Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 58\u003cbr\u003e3.2 Literature Review 58\u003cbr\u003e3.3 Methodology 62\u003cbr\u003e3.4 Results 68\u003cbr\u003e3.5 Discussions 76\u003cbr\u003e3.6 Conclusion and Future Works 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Simulating M\/G\/1 Queuing Network with Time-Varying Arrival Rates and Server Failure Using Python Programming 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSreelekha Menon, Surya K.A. and Reshma R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 82\u003cbr\u003e4.2 Methodology 84\u003cbr\u003e4.3 Numerical Example 86\u003cbr\u003e4.4 Python Code 89\u003cbr\u003e4.5 Negative Arrivals 89\u003cbr\u003e4.6 Conclusion 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Technique of Watermarking Using DGT and DCT 91\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNarendrakumar R. Dasre and Pritam Gujarathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 91\u003cbr\u003e5.2 Proposed Algorithm Using DGT and DCT 93\u003cbr\u003e5.3 Experimental Results 95\u003cbr\u003e5.4 Statistical Analysis 99\u003cbr\u003e5.5 Conclusion 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Performance and Economic Study of an Impatient Consumer Queue with Working Vacations, Secondary Service and Server Failures 117\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Jyothsna, P. Vijaya Kumar and P. Vijaya Laxmi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 118\u003cbr\u003e6.2 Model Overview 121\u003cbr\u003e6.3 Steady-State Analysis 122\u003cbr\u003e6.4 Performance Characteristics 125\u003cbr\u003e6.5 Sensitivity Analysis 128\u003cbr\u003e6.6 Conclusion 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Optimal Strategies for Multi-Item Stochastic Inventory Model for Convertible Items 139\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMamta Keswani and Uttam Kumar Khedlekar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 139\u003cbr\u003e7.2 Literature Survey 142\u003cbr\u003e7.3 Problem Statement 144\u003cbr\u003e7.4 Assumptions 145\u003cbr\u003e7.5 Notations 146\u003cbr\u003e7.6 Model Formulation 147\u003cbr\u003e7.7 Optimization by Using Dynamic Programming 148\u003cbr\u003e7.8 Numerical Validations 158\u003cbr\u003e7.9 Conclusion 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Sampling Statistics-Based Predictive Machine Learning Model for Large Scale Data Set 165\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKamlesh Kumar Pandey, Anurag Singh and Sudeep Kumar Verma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 166\u003cbr\u003e8.2 Challenging Issues of Big Data for Machine Learning 168\u003cbr\u003e8.3 Big Data Strategies for Machine Learning 170\u003cbr\u003e8.4 Sampling 172\u003cbr\u003e8.5 Sampling Model for Machine Learning 180\u003cbr\u003e8.6 Experimental Analysis 184\u003cbr\u003e8.7 Conclusion 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Correlation of Family History with Tumor Grade and Lymph Node Involvement in Breast Cancer Patients 195\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuganthi P. and Ebenesar Anna Bagyam J.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 196\u003cbr\u003e9.2 Literature Review 197\u003cbr\u003e9.3 Methodology 201\u003cbr\u003e9.4 Data Collection and Analysis of Parameters 201\u003cbr\u003e9.5 Analysis of Parameters Using Statistical Tool 206\u003cbr\u003e9.6 Conclusion 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Unlocking AI, ML, and DL Innovations: \"The Essential Role of Mathematics\" 211\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. Roselinkiruba, Vasumathy M., C.P. Koushik, C. Saranya Jothi, S. Divya and A. Keerthika\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction for the Mathematical Concepts in AI, ML and DL 212\u003cbr\u003e10.2 Linear Algebra 215\u003cbr\u003e10.3 Calculus: Foundations for Optimization and Training Algorithms 221\u003cbr\u003e10.4 Probability and Statistics: Analyzing and Validating Models 225\u003cbr\u003e10.5 Optimization: Refining Models and Resource Allocation 230\u003cbr\u003e10.6 Discrete Mathematics: Graph Theory and Combinatorics in AI 235\u003cbr\u003e10.7 Information Theory: Guiding Feature Selection and Model Evaluation 240\u003cbr\u003e10.8 Applications in Various Domains 243\u003cbr\u003e10.9 Conclusion 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Optimization and Metaheuristics: Mathematical Approaches in AI, Machine Learning, and Deep Learning 247\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eC. Saranya Jothi, J.P. Shritharanyaa, E. Surya, R. Roselinkiruba, P. Jeevanasree and B. Lalitha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction to Metaheuristics and Optimization 248\u003cbr\u003e11.2 Metaheuristics Algorithms and Their Mathematical Foundations 252\u003cbr\u003e11.3 Applications in Artificial Intelligence 263\u003cbr\u003e11.4 Metaheuristics in Machine Learning Applications 266\u003cbr\u003e11.5 Metaheuristics in Deep Learning Applications 268\u003cbr\u003e11.6 Challenges and Future Directions 271\u003cbr\u003e11.7 Conclusions 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 A Survey on Mathematics for Edge Detection Algorithms in Image Processing 275\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMaheshkumar D. Kudre, Narendrakumar R. Dasre and Pritam Wani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 275\u003cbr\u003e12.2 Literature Review 276\u003cbr\u003e12.3 Conclusion 286\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 PUF Inspired AES Cryptosystem for Securing Information 289\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSivasankari Narasimhan, Sumathy Raju, Kavya Sri and Anitha N.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 290\u003cbr\u003e13.2 Related Works 291\u003cbr\u003e13.3 Proposed PUF with AES Approach 292\u003cbr\u003e13.4 Simulation Results and Discussion 296\u003cbr\u003e13.5 AES-PUF Against Machine Learning Attacks 302\u003cbr\u003e13.6 Conclusion 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Leveraging Honeypots and Stochastic Gradient Descent for Advanced Cybersecurity 305\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJ. Esther, Regi Anbumozhi and S. Subbulakshmi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 306\u003cbr\u003e14.2 Literature Review 308\u003cbr\u003e14.3 Methodology 309\u003cbr\u003e14.4 Result \u0026amp; Discussion 315\u003cbr\u003e14.5 Conclusion 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Review of \"Optimizing Peer Review Workflows with AI: A Queuing Model Approach\" 321\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSreelekha Menon, Reshma R. and Surya K.A.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 322\u003cbr\u003e15.2 Queuing Models to Analyze the Impact of AI Peer Review Process 323\u003cbr\u003e15.3 Methodology 324\u003cbr\u003e15.3.4 Challenges and Drawbacks 326\u003cbr\u003e15.4 Conclusion 327\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 To Analyze the Success of Prostate Cancer Prediction Using Machine Learning 329\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBhaskar Nandi, Soumit Chowdhury, Subrata Jana, Biswadip Basu Mallik, Krishna Pada Das and Sudipta Banerjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 330\u003cbr\u003e16.2 Literature Review 331\u003cbr\u003e16.3 Objectives 334\u003cbr\u003e16.4 Hypothesis 334\u003cbr\u003e16.5 Attributes 335\u003cbr\u003e16.6 Flow Chart and Data Description 336\u003cbr\u003e16.7 Data Analysis 337\u003cbr\u003e16.8 Model Evaluation 344\u003cbr\u003e16.9 Result Analysis 350\u003cbr\u003e16.10 Conclusions 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Statistics in Data Science 357\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNishant Wanjari, Aashka Gupta, Reshma Gulwani and Aditi Chhabria\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction to Statistics 358\u003cbr\u003e17.2 Relationships between Data Science and Statistics 360\u003cbr\u003e17.3 Correlation and Covariance 363\u003cbr\u003e17.4 Regression Analysis 364\u003cbr\u003e17.5 Probability and Probability Functions 365\u003cbr\u003e17.6 Bayesian Statistics 368\u003cbr\u003e17.7 Hypothesis Testing 368\u003cbr\u003e17.8 Statistics in Predictive Modeling 371\u003cbr\u003e17.9 Statistics Meets Computation to Form Data Science 373\u003cbr\u003e17.10 Statistics Applications in Data Science 375\u003cbr\u003e17.11 Statistical Software and Packages in Data Science 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Frames for Applications in Engineering 381\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJamkhongam Touthang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 382\u003cbr\u003e18.2 Finite Frames 384\u003cbr\u003e18.3 Frames in Infinite-Dimensional Settings 390\u003cbr\u003e18.4 Applications 400\u003cbr\u003e18.5 Challenges in Signal Processing 407\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Development and Optimization of an ADRC-Controlled IPMC Actuator for Enhanced Disturbance Rejection and Creep Compensation 415\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMohammed Mohaideen M., Seenivasan S., Ravivarman G., Rangarajan R. V., Naveenkumar P., Sekar G., Balachandar K., Girimurugan R. and Biswadip Basu Mallik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 416\u003cbr\u003e19.2 Ionic Polymer Metal Composites Creep Model 417\u003cbr\u003e19.3 Design of the ADRC Controller 419\u003cbr\u003e19.4 ADRC Controller Parameters Can Be Changed Using the Particle Swarm Optimization Method 424\u003cbr\u003e19.5 Conclusions 432\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Industry 4.0: Revolutionizing Production through Cyber-Physical Systems 437\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGirisha L., Meinathan S., Ravivarman G., Girimurugan R., Irudhayamary Premkumar, Catherene Julie Aarthy C. and Biswadip Basu Mallik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 438\u003cbr\u003e20.2 A Case Study 440\u003cbr\u003e20.3 Infrastructure Requirements 442\u003cbr\u003e20.4 Legal Issues and Cyber-Security 445\u003cbr\u003e20.5 Development of New Business Models 448\u003cbr\u003e20.6 Challenges to Achieve Sustainable Development 451\u003cbr\u003e20.7 Conclusions 453\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Safeguarding Security and Privacy in the Business Sector: The Role of AI and ML 459\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJoshua Bapu J., Saranya N., Chacko Jose P., Suresh Kumar K., Jayachandran T., Girimurugan R. and Biswadip Basu Mallik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 460\u003cbr\u003e21.2 An Ethical Investigation into Cyber Security 462\u003cbr\u003e21.3 Literature Review 463\u003cbr\u003e21.4 Artificial Intelligence Cybersecurity as a Business Ethics Duty 466\u003cbr\u003e21.5 Using AI and Big Language Models in Business Settings: Falling for the Marketing Hype 470\u003cbr\u003e21.6 Risk Considerations for Cyber Security in Generative AI and Huge Language Models 471\u003cbr\u003e21.7 Ethical Implications of Generative AI Risk 474\u003cbr\u003e21.8 Ethical Implementations of Generative AI 479\u003cbr\u003e21.9 Conclusions 480\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Advancing Women Health: Detecting Polycystic Ovary Syndrome through Machine Learning 485\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. DeviPriya, K. V. V. S. Trinadh Naidu, V. Chandra Kumar, Subrata Jana, Biswadip Basu Mallik, K. Bhanu Rajesh Naidu and M. V. Rajesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 486\u003cbr\u003e22.2 Related Works 488\u003cbr\u003e22.3 Proposed Work 489\u003cbr\u003e22.4 Dataset 491\u003cbr\u003e22.5 Experimental Setup 494\u003cbr\u003e22.6 Results \u0026amp; Discussion 494\u003cbr\u003e22.7 Conclusions 498\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Personalized Hotel Recommendation System Using Similarity Measures and Heuristic Analysis 501\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePrapti Sinha, Rajashree Shedge and Dipti Jadhav\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 502\u003cbr\u003e23.2 Background 503\u003cbr\u003e23.3 Literature Survey 505\u003cbr\u003e23.4 Dataset 508\u003cbr\u003e23.5 Proposed Framework 508\u003cbr\u003e23.6 Result 519\u003cbr\u003e23.7 Conclusion 519\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Predictive Structural Equation Modeling for Multi-Dimensional Skill-Development Among Higher Education Learners in Formal Learning Environment 521\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eS. Bhuma Devi, Preeti Jain and Gargi Tyagi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 522\u003cbr\u003e24.2 Related Work 523\u003cbr\u003e24.3 Research Methodology 526\u003cbr\u003e24.4 Results and Discussion 535\u003cbr\u003e24.5 Conclusion 541\u003c\/p\u003e \u003cp\u003eReferences 542\u003cbr\u003eIndex 545\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433528258840,"sku":"9781394307272","price":153.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394307272.jpg?v=1784853437","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/advanced-mathematics-in-computing-communication-and-security-hardback-9781394307272","provider":"Freshly Printed Books","version":"1.0","type":"link"}