{"product_id":"mathematics-and-computer-science-volume-2-hardback-9781119896326","title":"Mathematics and Computer Science, Volume 2 (Hardback) 9781119896326","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMathematics and Computer Science, Volume 2\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\"\u003eSharmistha Ghosh (Edited by), Ghosh (Author), M. Niranjanamurthy (Edited by), Krishanu Deyasi (Edited by), Biswadip Basu Mallik (Edited by), Santanu Das (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119896326, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 1 August 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e432 pages\u003cbr\u003e22.9 x 15.2 x 2.6 cm, 0.83 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\u003cb\u003eMATHEMATICS AND COMPUTER SCIENCE\u003c\/b\u003e \u003cp\u003e\u003cb\u003eThis second volume in a new multi-volume set builds on the basic concepts and fundamentals laid out in the previous volume, presenting the reader with more advanced and cutting-edge topics being developed in this exciting field.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThis second volume in a new series from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. Building on what was presented in volume one, the chapters focus on more advanced topics in computer science, mathematics, and where the two intersect to create value for end users through practical applications. \u003c\/p\u003e\n\u003cp\u003eThe chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A Comprehensive Review on Text Classification and Text Mining Techniques Using Spam Dataset Detection 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTamannas Siddiqui and Abdullah Yahya Abdullah Amer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Text Mining Techniques 3\u003c\/p\u003e \u003cp\u003e1.2.1 Data Mining 3\u003c\/p\u003e \u003cp\u003e1.2.2 Information Retrieval 4\u003c\/p\u003e \u003cp\u003e1.2.3 Natural Language Processing (NLP) 5\u003c\/p\u003e \u003cp\u003e1.2.4 Information Extraction 5\u003c\/p\u003e \u003cp\u003e1.2.5 Text Summarization 6\u003c\/p\u003e \u003cp\u003e1.2.6 Text Categorization 7\u003c\/p\u003e \u003cp\u003e1.2.7 Clustering 7\u003c\/p\u003e \u003cp\u003e1.2.8 Information Visualization 7\u003c\/p\u003e \u003cp\u003e1.2.9 Question Answer 8\u003c\/p\u003e \u003cp\u003e1.3 Dataset and Preprocessing Steps 9\u003c\/p\u003e \u003cp\u003e1.3.1 Text Preprocess 9\u003c\/p\u003e \u003cp\u003e1.4 Feature Extraction 9\u003c\/p\u003e \u003cp\u003e1.4.1 Term Frequency – Inverse Document Frequency 10\u003c\/p\u003e \u003cp\u003e1.4.2 Bag of Words (BoW) 10\u003c\/p\u003e \u003cp\u003e1.5 Supervised Machine Learning Classification 11\u003c\/p\u003e \u003cp\u003e1.6 Evaluation 11\u003c\/p\u003e \u003cp\u003e1.7 Experimentation and Discussion Results for Spam Detection Data 11\u003c\/p\u003e \u003cp\u003e1.8 Text Mining Applications 13\u003c\/p\u003e \u003cp\u003e1.9 Text Classification Support 13\u003c\/p\u003e \u003cp\u003e1.9.1 Health 13\u003c\/p\u003e \u003cp\u003e1.9.2 Business and Marketing 14\u003c\/p\u003e \u003cp\u003e1.9.3 Law 14\u003c\/p\u003e \u003cp\u003e1.10 Conclusions 14\u003c\/p\u003e \u003cp\u003eReferences 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Study of Lidar Signals of the Atmospheric Boundary Layer Using Statistical Technique 19\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKamana Mishra and Bhavani Kumar Yellapragada\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 19\u003c\/p\u003e \u003cp\u003e2.2 Methodology 21\u003c\/p\u003e \u003cp\u003e2.2.1 A Statistical Approach to Determine the CBLH 21\u003c\/p\u003e \u003cp\u003e2.2.2 A Statistical Approach to Determine the Best Fit Distribution to the Backscatter Signals of the Lidar Dataset 22\u003c\/p\u003e \u003cp\u003e2.3 Mathematical Background of Method 23\u003c\/p\u003e \u003cp\u003e2.4 Example and Result 24\u003c\/p\u003e \u003cp\u003e2.5 Conclusion and Future Scope 27\u003c\/p\u003e \u003cp\u003eAcknowledgement 28\u003c\/p\u003e \u003cp\u003eReferences 28\u003c\/p\u003e \u003cp\u003eAnnexure 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Optimal Personalized Therapies in Colon Cancer Induced Immune Response using a Fokker-Planck Framework 33\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSouvik Roy and Suvra Pal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 33\u003c\/p\u003e \u003cp\u003e3.2 The Control Framework Based on Fokker-Planck Equations 35\u003c\/p\u003e \u003cp\u003e3.3 Theoretical Results 39\u003c\/p\u003e \u003cp\u003e3.4 Numerical Schemes 41\u003c\/p\u003e \u003cp\u003e3.5 Results 43\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 44\u003c\/p\u003e \u003cp\u003eAcknowledgments 45\u003c\/p\u003e \u003cp\u003eReferences 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Detection and Classification of Leaf Blast Disease using Decision Tree Algorithm in Rice Crop 49\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSarvesh Vishwakarma and Bhavna Chilwal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 49\u003c\/p\u003e \u003cp\u003e4.2 Proposed Methodology 51\u003c\/p\u003e \u003cp\u003e4.3 Result Analysis 52\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 55\u003c\/p\u003e \u003cp\u003e4.5 Future Work 55\u003c\/p\u003e \u003cp\u003eReferences 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Novel Hybrid Optimal Deep Network and Optimization Approach for Human Face Emotion Recognition 59\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJ. Seetha, M. Ayyadurai and M. Mary Victoria Florence\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 60\u003c\/p\u003e \u003cp\u003e5.2 Related Work 61\u003c\/p\u003e \u003cp\u003e5.3 System Model and Problem Statement 62\u003c\/p\u003e \u003cp\u003e5.4 Proposed Model 63\u003c\/p\u003e \u003cp\u003e5.4.1 Preprocessing Stage 64\u003c\/p\u003e \u003cp\u003e5.4.2 Knowledge Based Face Detection 64\u003c\/p\u003e \u003cp\u003e5.4.3 Image Resizing 64\u003c\/p\u003e \u003cp\u003e5.4.4 Feature Extraction 64\u003c\/p\u003e \u003cp\u003e5.5 Proposed HDC-GEN Classification 65\u003c\/p\u003e \u003cp\u003e5.6 Result and Discussion 68\u003c\/p\u003e \u003cp\u003e5.6.1 Performance Metrics Evaluation 69\u003c\/p\u003e \u003cp\u003e5.6.2 Comparative Analysis 70\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 74\u003c\/p\u003e \u003cp\u003eReferences 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 An Application of Information Technology in Adaptive Leadership of Ministry of Ayush During Pandemic of Covid 19: A Case Study 77\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVikram Singh, Shikha Kapoor and Sandeep Kumar Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 77\u003c\/p\u003e \u003cp\u003e6.2 Ministry of AYUSH 78\u003c\/p\u003e \u003cp\u003e6.3 Leadership Principles and Practices by Ministry of AYUSH During Covid- 19 79\u003c\/p\u003e \u003cp\u003e6.4 Effective Communication 79\u003c\/p\u003e \u003cp\u003e6.5 Sharing of Resources 80\u003c\/p\u003e \u003cp\u003e6.6 Shared Decision Making 81\u003c\/p\u003e \u003cp\u003e6.7 Training of Manpower 81\u003c\/p\u003e \u003cp\u003e6.8 Use of IT Platform 81\u003c\/p\u003e \u003cp\u003e6.9 Finding Opportunities for R\u0026amp;D During the Crisis 83\u003c\/p\u003e \u003cp\u003e6.10 Collaborating with Stakeholders for International Day of Yoga (IDY) 84\u003c\/p\u003e \u003cp\u003e6.11 Providing Hope When Nothing Seemed to be Working 87\u003c\/p\u003e \u003cp\u003e6.12 Leveraging Old Knowledge 87\u003c\/p\u003e \u003cp\u003e6.13 Conclusion 88\u003c\/p\u003e \u003cp\u003eReferences 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Encoder-Decoder Models for Protein Secondary Structure Prediction 91\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAshish Kumar Sharma and Rajeev Srivastava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 91\u003c\/p\u003e \u003cp\u003e7.2 Literature Review 93\u003c\/p\u003e \u003cp\u003e7.3 Experimental Work 93\u003c\/p\u003e \u003cp\u003e7.3.1 Data Set 93\u003c\/p\u003e \u003cp\u003e7.3.2 Proposed Methodology 94\u003c\/p\u003e \u003cp\u003e7.3.3 Data Preprocessing 94\u003c\/p\u003e \u003cp\u003e7.3.4 Long Short Term Memory 94\u003c\/p\u003e \u003cp\u003e7.4 Results and Discussion 96\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 97\u003c\/p\u003e \u003cp\u003eReferences 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Hesitancy, Awareness, and Vaccination: A Computational Analysis on Complex Networks 101\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDibyajyoti Mallick, Aniruddha Ray, Ankita Das and Sayantari Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 101\u003c\/p\u003e \u003cp\u003e8.2 Model Formulation 103\u003c\/p\u003e \u003cp\u003e8.3 Model Analysis on Complex Network 106\u003c\/p\u003e \u003cp\u003e8.3.1 Effect of Vaccination Rate 107\u003c\/p\u003e \u003cp\u003e8.3.2 Effect of Negative Rumors 108\u003c\/p\u003e \u003cp\u003e8.3.3 Effect of Positive Peer Influence 108\u003c\/p\u003e \u003cp\u003e8.4 Conclusions and Perspectives 111\u003c\/p\u003e \u003cp\u003eReferences 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Propagation of Seismic Waves in Porous Thermoelastic Semi-Infinite Space with Impedance Boundary Conditions 115\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnnu Rani and Dinesh Kumar Madan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 115\u003c\/p\u003e \u003cp\u003e9.2 Basic Equations 116\u003c\/p\u003e \u003cp\u003e9.3 Problem Formulation 118\u003c\/p\u003e \u003cp\u003e9.4 Reflection at the Free Surface 121\u003c\/p\u003e \u003cp\u003e9.4.1 Boundary Conditions 124\u003c\/p\u003e \u003cp\u003e9.4.2 Energy Ratios 126\u003c\/p\u003e \u003cp\u003e9.5 Numerical Results and Discussion 127\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 133\u003c\/p\u003e \u003cp\u003eReferences 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 IoT Based Ensemble Predictive Techniques to Determine the Student Observing Analysis through E-Learning 137\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRufia Thaseen I., S. Shahar Banu and Sudha Rajesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 138\u003c\/p\u003e \u003cp\u003e10.2 Review of Literature 140\u003c\/p\u003e \u003cp\u003e10.2.1 Objectives of the Study 142\u003c\/p\u003e \u003cp\u003e10.3 Methodology 142\u003c\/p\u003e \u003cp\u003e10.4 Analysis and Interpretation 143\u003c\/p\u003e \u003cp\u003e10.5 Findings and Conclusion 147\u003c\/p\u003e \u003cp\u003eReferences 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Modelling and Analysis of a Congestion Dependent Queue with Bernoulli Scheduled Vacation Interruption and Client Impatience 151\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK. Jyothsna and P. Vijaya Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 152\u003c\/p\u003e \u003cp\u003e11.2 Model Overview 154\u003c\/p\u003e \u003cp\u003e11.3 Model Analysis 155\u003c\/p\u003e \u003cp\u003e11.3.1 Pre-Arrival Epoch Probabilities 157\u003c\/p\u003e \u003cp\u003e11.3.2 Arbitrary Epoch Probabilities 162\u003c\/p\u003e \u003cp\u003e11.4 Special Cases 163\u003c\/p\u003e \u003cp\u003e11.5 Performance Metrics 163\u003c\/p\u003e \u003cp\u003e11.6 Numerical Outcomes 164\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 169\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Resource Allocation Determines Alternate Cell Fate in Bistable Genetic Switch 173\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePriya Chakraborty and Sayantari Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 173\u003c\/p\u003e \u003cp\u003e12.2 Model Formulation 176\u003c\/p\u003e \u003cp\u003e12.3 Result Section 178\u003c\/p\u003e \u003cp\u003e12.3.1 Pitchfork Bifurcation in Genetic Toggle 178\u003c\/p\u003e \u003cp\u003e12.3.1.1 Resource Affinity Regulates the Symmetry of Pitchfork Bifurcation 178\u003c\/p\u003e \u003cp\u003e12.3.1.2 Availability of Total mRNA Pool Regulates the Symmetry of Pitchfork Bifurcation 180\u003c\/p\u003e \u003cp\u003e12.3.1.3 Total Resource Availability Regulates the Point of Bifurcation in the System 180\u003c\/p\u003e \u003cp\u003e12.3.2 Saddle Node Bifurcation in Genetic Toggle 180\u003c\/p\u003e \u003cp\u003e12.3.2.1 Resource Distribution Regulates the Point of Bifurcation in Toggle Switch 182\u003c\/p\u003e \u003cp\u003e12.3.2.2 Region of Interest in Toggle Switch is Significantly Regulated by Resource Allocation 182\u003c\/p\u003e \u003cp\u003e12.3.2.3 Total Resource Availability T Regulates Saddle Node Bifurcation Curve 183\u003c\/p\u003e \u003cp\u003e12.4 Conclusion 183\u003c\/p\u003e \u003cp\u003eAcknowledgement 184\u003c\/p\u003e \u003cp\u003eReferences 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 A Hybrid Approach to Ontology Evaluation 187\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAastha Mishra and Preetvanti Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 187\u003c\/p\u003e \u003cp\u003e13.2 Background 188\u003c\/p\u003e \u003cp\u003e13.3 The Developed OntoEva Method 189\u003c\/p\u003e \u003cp\u003e13.4 Ontology Selection for Epilepsy Disorder 190\u003c\/p\u003e \u003cp\u003e13.4.1 Accuracy 191\u003c\/p\u003e \u003cp\u003e13.4.2 Adaptability 191\u003c\/p\u003e \u003cp\u003e13.4.3 Clarity 191\u003c\/p\u003e \u003cp\u003e13.4.4 Completeness 192\u003c\/p\u003e \u003cp\u003e13.4.5 Conciseness 192\u003c\/p\u003e \u003cp\u003e13.4.6 Consistency 192\u003c\/p\u003e \u003cp\u003e13.4.7 Organizational Fitness 192\u003c\/p\u003e \u003cp\u003e13.5 Results 201\u003c\/p\u003e \u003cp\u003e13.6 Comparison of Ontologies 201\u003c\/p\u003e \u003cp\u003e13.7 Conclusion 202\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Smart Health Care Waste Segregation and Safe Disposal 205\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR.M. Bommi, Sami Venkata Sai Rajeev, Sarvepalli Navya, Veluru Sai Teja and Uppala Supriya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 206\u003c\/p\u003e \u003cp\u003e14.2 Related Works 207\u003c\/p\u003e \u003cp\u003e14.3 System Architecture 210\u003c\/p\u003e \u003cp\u003e14.3.1 Wrapping 212\u003c\/p\u003e \u003cp\u003e14.3.2 Incinerator 213\u003c\/p\u003e \u003cp\u003e14.3.3 Conveyor Cleaning System 213\u003c\/p\u003e \u003cp\u003e14.3.4 Circuit Diagram 213\u003c\/p\u003e \u003cp\u003e14.3.5 Optimal Path Planning Algorithm for Waste Collection 214\u003c\/p\u003e \u003cp\u003e14.4 Methodology 214\u003c\/p\u003e \u003cp\u003e14.5 Mobile App 217\u003c\/p\u003e \u003cp\u003e14.6 Conclusions and Future Works 218\u003c\/p\u003e \u003cp\u003eDeclarations 219\u003c\/p\u003e \u003cp\u003eAvailability of Data and Materials 219\u003c\/p\u003e \u003cp\u003eCompeting Interests 219\u003c\/p\u003e \u003cp\u003eAuthor’s Contribution 219\u003c\/p\u003e \u003cp\u003eReferences 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Investigation of Viscoelastic Magnetohydrodynamics (MHD) Flow Over an Expanded Lamina Surrounded in a Permeable Media 223\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHiranmoy Mondal, Arindam Sarkar and Raj Nandkeolyar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 223\u003c\/p\u003e \u003cp\u003e15.1.1 Literature Review 223\u003c\/p\u003e \u003cp\u003e15.1.2 Nomenclature 224\u003c\/p\u003e \u003cp\u003e15.2 Formulation of the Problem 226\u003c\/p\u003e \u003cp\u003e15.2.1 Analytical Solution 227\u003c\/p\u003e \u003cp\u003e15.2.2 Numerical Methods (Spectral Quasi-Linearization Methods) 228\u003c\/p\u003e \u003cp\u003e15.3 Result and Argument 230\u003c\/p\u003e \u003cp\u003e15.4 Conclusion 235\u003c\/p\u003e \u003cp\u003eReferences 236\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Quickest Multi-Commodity Contraflow with Non-Symmetric Traversal Times 239\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShiva Prakash Gupta, Urmila Pyakurel and Tanka Nath Dhamala\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 239\u003c\/p\u003e \u003cp\u003e16.2 Preliminaries with Flow Models 242\u003c\/p\u003e \u003cp\u003e16.2.1 Mathematical Model with Contraflow 242\u003c\/p\u003e \u003cp\u003e16.3 QMCCF with Non-Symmetric Transit Times 244\u003c\/p\u003e \u003cp\u003e16.3.1 Approximation Approach for the QMCCF 245\u003c\/p\u003e \u003cp\u003e16.4 Conclusions 248\u003c\/p\u003e \u003cp\u003eAcknowledgments 248\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 A Mathematical Representation for Deteriorating Goods with a Trapezoidal-Type Demand, Shortages and Time Dependent Holding Cost 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRuma Roy Chowdhury\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 251\u003c\/p\u003e \u003cp\u003e17.2 Assumptions and Notations 253\u003c\/p\u003e \u003cp\u003e17.2.1 Assumptions 253\u003c\/p\u003e \u003cp\u003e17.2.2 Notation 254\u003c\/p\u003e \u003cp\u003e17.2.3 Decision Variables 255\u003c\/p\u003e \u003cp\u003e17.3 Formulation and Solution 255\u003c\/p\u003e \u003cp\u003e17.3.1 Case 1 i) 0 \u0026lt; ν \u0026lt; t \u003csub\u003e1\u003c\/sub\u003e \u0026lt; t \u003csub\u003e2\u003c\/sub\u003e \u0026lt; t \u003csub\u003e3\u003c\/sub\u003e \u0026lt; T 255\u003c\/p\u003e \u003cp\u003e17.3.2 Case 2 ii) 0 \u0026lt; t \u003csub\u003ea\u003c\/sub\u003e \u0026lt; ν \u0026lt; t \u003csub\u003eb\u003c\/sub\u003e \u0026lt; t \u003csub\u003ec\u003c\/sub\u003e \u0026lt; T 257\u003c\/p\u003e \u003cp\u003e17.3.3 Case 3 iii) 0 \u0026lt; t \u003csub\u003ea\u003c\/sub\u003e \u0026lt; t \u003csub\u003eb\u003c\/sub\u003e \u0026lt; ν \u0026lt; t \u003csub\u003ec\u003c\/sub\u003e \u0026lt; T 259\u003c\/p\u003e \u003cp\u003e17.4 Numerical Example 261\u003c\/p\u003e \u003cp\u003e17.5 Discussion 261\u003c\/p\u003e \u003cp\u003e17.6 Inference 262\u003c\/p\u003e \u003cp\u003eReferences 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 An Amended Moth Flame Optimization Algorithm Based on Fibonacci Search Approach for Solving Engineering Design Problems 265\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSaroj Kumar Sahoo and Apu Kumar Saha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 266\u003c\/p\u003e \u003cp\u003e18.2 Classical MFO Algorithm 268\u003c\/p\u003e \u003cp\u003e18.3 Proposed Method 270\u003c\/p\u003e \u003cp\u003e18.4 Results and Discussions on IEEE CEC 2019\u003c\/p\u003e \u003cp\u003eBenchmark Problems 273\u003c\/p\u003e \u003cp\u003e18.5 Real-Life Applications 277\u003c\/p\u003e \u003cp\u003e18.5.1 Optimal Gas Production Capacity Problem 277\u003c\/p\u003e \u003cp\u003e18.5.2 Three-Bar Truss Design (TSD) Problem 277\u003c\/p\u003e \u003cp\u003e18.6 Conclusion with Future Studies 278\u003c\/p\u003e \u003cp\u003eReferences 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Image Segmentation of Neuronal Cell with Ensemble Unet Architecture 283\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKirtan Kanani, Aditya K. Gupta, Ankit Kumar Nikum, Prashant Gupta and Dharmik Raval\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 284\u003c\/p\u003e \u003cp\u003e19.2 Methods 285\u003c\/p\u003e \u003cp\u003e19.3 Dataset 285\u003c\/p\u003e \u003cp\u003e19.4 Implementation Details 286\u003c\/p\u003e \u003cp\u003e19.5 Evaluation Metrics 286\u003c\/p\u003e \u003cp\u003e19.6 Result 286\u003c\/p\u003e \u003cp\u003e19.7 Conclusion 289\u003c\/p\u003e \u003cp\u003eReferences 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Automorphisms of Some Non-Abelian p−Groups of Order p\u003csup\u003e4\u003c\/sup\u003e 291\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMuniya Sansanwal, Harsha Arora and Mahender Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 291\u003c\/p\u003e \u003cp\u003e20.2 Categorization of p-Groups with Order p\u003csup\u003e4\u003c\/sup\u003e 292\u003c\/p\u003e \u003cp\u003e20.3 Number of Automorphisms of Some Non-Abelian Groups of Order p\u003csup\u003e4\u003c\/sup\u003e 293\u003c\/p\u003e \u003cp\u003e20.3.1 Computation of Automorphisms of Group G\u003csub\u003e9\u003c\/sub\u003e 293\u003c\/p\u003e \u003cp\u003e20.3.2 Computation of Automorphisms of Group G\u003csub\u003e10\u003c\/sub\u003e 297\u003c\/p\u003e \u003cp\u003e20.3.3 Computation of Automorphisms of G\u003csub\u003e11\u003c\/sub\u003e 300\u003c\/p\u003e \u003cp\u003eReferences 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Viscoelastic Equation of p-Laplacian Hyperbolic Type with Logarithmic Source Term 305\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNazlı Irkıl and Erhan Pişkin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 305\u003c\/p\u003e \u003cp\u003e21.2 Preliminaries 307\u003c\/p\u003e \u003cp\u003e21.3 Global Existence Result 314\u003c\/p\u003e \u003cp\u003e21.4 Blow Up Results of the Solution for Equation (21.1) 317\u003c\/p\u003e \u003cp\u003eReferences 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Flow Dynamics in Continuous-Time with Average Arc Capacities 327\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBadri Prasad Pangeni and Tanka Nath Dhamala\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 327\u003c\/p\u003e \u003cp\u003e22.2 Literature Review 329\u003c\/p\u003e \u003cp\u003e22.3 Failure in Extension of AP to AAP 329\u003c\/p\u003e \u003cp\u003e22.4 Formulation 331\u003c\/p\u003e \u003cp\u003e22.5 Conclusion 334\u003c\/p\u003e \u003cp\u003eAcknowledgment 334\u003c\/p\u003e \u003cp\u003eReferences 334\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Analysis of a Multiserver System of Queue-Dependent Channel Using Genetic Algorithm 337\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnupama and Chandan Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 337\u003c\/p\u003e \u003cp\u003e23.2 Description of the Model 338\u003c\/p\u003e \u003cp\u003e23.3 Notations 338\u003c\/p\u003e \u003cp\u003e23.4 Steady State Equations 340\u003c\/p\u003e \u003cp\u003e23.4.1 Performance Characteristics of the System 342\u003c\/p\u003e \u003cp\u003e23.4.2 Queue Length Evaluations at Different Epochs 343\u003c\/p\u003e \u003cp\u003e23.4.3 Leisure Period and Working Period Length 343\u003c\/p\u003e \u003cp\u003e23.4.4 Cost of the System 343\u003c\/p\u003e \u003cp\u003e23.5 Conclusions 347\u003c\/p\u003e \u003cp\u003eReferences 347\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 An Approach to Ranking of Single Valued Neutrosophic Fuzzy Numbers Based on (α, β, γ) Cut Sets 349\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSunayana Saikia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 349\u003c\/p\u003e \u003cp\u003e24.2 Definition and Representations 351\u003c\/p\u003e \u003cp\u003e24.3 Proposed Method 355\u003c\/p\u003e \u003cp\u003e24.4 Theorems 356\u003c\/p\u003e \u003cp\u003e24.5 Numerical Examples 357\u003c\/p\u003e \u003cp\u003e24.6 Conclusion 358\u003c\/p\u003e \u003cp\u003eReferences 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Performance Analysis of Database Models Based on Fuzzy and Vague Sets for Uncertain Query Processing 363\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSharmistha Ghosh and Surath Roy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 363\u003c\/p\u003e \u003cp\u003e25.2 Basic Definitions 364\u003c\/p\u003e \u003cp\u003e25.2.1 Fuzzy Set 365\u003c\/p\u003e \u003cp\u003e25.2.2 Vague Set 365\u003c\/p\u003e \u003cp\u003e25.2.3 Similarity Measure 365\u003c\/p\u003e \u003cp\u003e25.3 Algorithm to Generate Membership Values 366\u003c\/p\u003e \u003cp\u003e25.4 Real Life Applications 368\u003c\/p\u003e \u003cp\u003e25.5 Conclusion 382\u003c\/p\u003e \u003cp\u003eReferences 382\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Estimating Error of Signals by Product Means (N, p\u003csub\u003en\u003c\/sub\u003e,q\u003csub\u003en\u003c\/sub\u003e) (C, 2) of the Fourier Series in a W(L\u003csup\u003er\u003c\/sup\u003e , ξ (t))(r ≥ 1) Class 385\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePankaj Tiwari and Aradhana Dutt Jauhari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 385\u003c\/p\u003e \u003cp\u003e26.1.1 Definition 1 386\u003c\/p\u003e \u003cp\u003e26.1.2 Definition 2 386\u003c\/p\u003e \u003cp\u003e26.1.3 Definition 3 386\u003c\/p\u003e \u003cp\u003e26.1.4 Definition 4 387\u003c\/p\u003e \u003cp\u003e26.1.5 Remark 1 387\u003c\/p\u003e \u003cp\u003e26.2 Known Result 388\u003c\/p\u003e \u003cp\u003e26.2.1 Theorem 1 388\u003c\/p\u003e \u003cp\u003e26.3 Main Theorem 389\u003c\/p\u003e \u003cp\u003e26.3.1 Theorem 2 389\u003c\/p\u003e \u003cp\u003e26.4 Some Auxiliary Results 390\u003c\/p\u003e \u003cp\u003e26.4.1 Lemma- 1 390\u003c\/p\u003e \u003cp\u003e26.4.2 Lemma- 2 390\u003c\/p\u003e \u003cp\u003e26.5 Theorem’s Proof 391\u003c\/p\u003e \u003cp\u003e26.6 Applications 394\u003c\/p\u003e \u003cp\u003e26.6.1 Cor. 1 394\u003c\/p\u003e \u003cp\u003e26.6.2 Cor. 2 394\u003c\/p\u003e \u003cp\u003e26.7 Conclusion 394\u003c\/p\u003e \u003cp\u003eAcknowledgement 395\u003c\/p\u003e \u003cp\u003eReferences 395\u003c\/p\u003e \u003cp\u003eAbout the Editors 397\u003c\/p\u003e \u003cp\u003eIndex 401\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":52430993326360,"sku":"9781119896326","price":127.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119896326.jpg?v=1784768199","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/mathematics-and-computer-science-volume-2-hardback-9781119896326","provider":"Freshly Printed Books","version":"1.0","type":"link"}