{"product_id":"mathematics-and-computer-science-volume-1-9781119879671","title":"Mathematics and Computer Science Volume 1","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMATHEMATICS AND COMPUTER SCIENCE This first volume in a new multi-volume set gives readers the basic concepts and applications for diverse ideas and innovations in the field  of computing together with its growing interactions with mathematics. This new edited volume 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. The chapters focus on recent advances in computer science, and mathematics, and where the two intersect to create value for end users through practical applications of the theory. The 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 T\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Error Estimation of the Function by (\u003c\/b\u003eZ\u003cb\u003e \u003csub\u003er\u003c\/sub\u003e\u003csup\u003eu\u003c\/sup\u003e ,r ≥ 1) Using Product Means (E,s)(\u003c\/b\u003e \u003cb\u003eN, p\u003csub\u003en\u003c\/sub\u003e ,,q\u003csub\u003en\u003c\/sub\u003e) n of) the Conjugate Fourier Series 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAradhana Dutt Jauhari and Pankaj Tiwar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.1.1 Definition 1 2\u003c\/p\u003e \u003cp\u003e1.1.2 Definition 2 2\u003c\/p\u003e \u003cp\u003e1.1.3 Definition 3 2\u003c\/p\u003e \u003cp\u003e1.2 Theorems 5\u003c\/p\u003e \u003cp\u003e1.2.1 Theorem 1 5\u003c\/p\u003e \u003cp\u003e1.2.2 Theorem 2 5\u003c\/p\u003e \u003cp\u003e1.3 Lemmas 6\u003c\/p\u003e \u003cp\u003e1.3.1 Lemma 1 6\u003c\/p\u003e \u003cp\u003e1.3.2 Lemma 2 6\u003c\/p\u003e \u003cp\u003e1.3.3 Lemma 3 9\u003c\/p\u003e \u003cp\u003e1.4 Proof of the Theorems 9\u003c\/p\u003e \u003cp\u003e1.4.1 Proof of the Theorem 1 9\u003c\/p\u003e \u003cp\u003e1.4.2 Proof of the Theorem 2 15\u003c\/p\u003e \u003cp\u003e1.5 Corollaries 16\u003c\/p\u003e \u003cp\u003e1.5.1 Corollary 1 16\u003c\/p\u003e \u003cp\u003e1.5.2 Corollary 2 16\u003c\/p\u003e \u003cp\u003e1.6 Example 16\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 18\u003c\/p\u003e \u003cp\u003eReferences 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Blow Up and Decay of Solutions for a Klein-Gordon Equation With Delay and Variable Exponents 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHazal Yüksekkaya and Erhan Pişkin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.2 Preliminaries 23\u003c\/p\u003e \u003cp\u003e2.3 Blow Up of Solutions 26\u003c\/p\u003e \u003cp\u003e2.4 Decay of Solutions 36\u003c\/p\u003e \u003cp\u003eAcknowledgment 43\u003c\/p\u003e \u003cp\u003eReferences 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Some New Inequalities Via Extended Generalized Fractional Integral Operator for Chebyshev Functional 45\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBhagwat R. Yewale and Deepak B. Pachpatte\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 45\u003c\/p\u003e \u003cp\u003e3.2 Preliminaries 46\u003c\/p\u003e \u003cp\u003e3.3 Fractional Inequalities for the Chebyshev Functional 47\u003c\/p\u003e \u003cp\u003e3.4 Fractional Inequalities in the Case of Extended Chebyshev Functional 53\u003c\/p\u003e \u003cp\u003e3.5 Some Other Fracional Inequalities Related to the Extended Chebyshev Functional 57\u003c\/p\u003e \u003cp\u003e3.6 Concluding Remark 63\u003c\/p\u003e \u003cp\u003eReferences 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Blow Up of the Higher-Order Kirchhoff-Type System With Logarithmic Nonlinearities 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNazlı Irkil and Erhan Pişkin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 67\u003c\/p\u003e \u003cp\u003e4.2 Preliminaries 69\u003c\/p\u003e \u003cp\u003e4.3 Blow Up for Problem for E (0) \u0026lt; d 78\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 84\u003c\/p\u003e \u003cp\u003eReferences 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Developments in Post-Quantum Cryptography 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSrijita Sarkar, Saranya Kumar, Anaranya Bose and Tiyash Mukherjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 90\u003c\/p\u003e \u003cp\u003e5.2 Modern-Day Cryptography 90\u003c\/p\u003e \u003cp\u003e5.2.1 Symmetric Cryptosystems 91\u003c\/p\u003e \u003cp\u003e5.2.2 Asymmetric Cryptosystems 91\u003c\/p\u003e \u003cp\u003e5.2.3 Attacks on Modern Cryptosystems 92\u003c\/p\u003e \u003cp\u003e5.2.3.1 Known Attacks 93\u003c\/p\u003e \u003cp\u003e5.2.3.2 Side-Channel Attacks 93\u003c\/p\u003e \u003cp\u003e5.3 Quantum Computing 93\u003c\/p\u003e \u003cp\u003e5.3.1 The Main Aspects of Quantum Computing 94\u003c\/p\u003e \u003cp\u003e5.3.2 Shor’s Algorithm 95\u003c\/p\u003e \u003cp\u003e5.3.3 Grover’s Algorithm 96\u003c\/p\u003e \u003cp\u003e5.3.4 The Need for Post-Quantum Cryptography 96\u003c\/p\u003e \u003cp\u003e5.4 Algorithms Proposed for Post-Quantum Cryptography 97\u003c\/p\u003e \u003cp\u003e5.4.1 Code-Based Cryptography 97\u003c\/p\u003e \u003cp\u003e5.4.2 Lattice-Based Cryptography 98\u003c\/p\u003e \u003cp\u003e5.4.3 Multivariate Cryptography 99\u003c\/p\u003e \u003cp\u003e5.4.4 Hash-Based Cryptography 99\u003c\/p\u003e \u003cp\u003e5.4.5 Supersingular Elliptic Curve Isogeny Cryptography 100\u003c\/p\u003e \u003cp\u003e5.4.6 Quantum-Resistant Symmetric Key Cryptography 100\u003c\/p\u003e \u003cp\u003e5.5 Launching of the Project Called “Open Quantum Safe” 100\u003c\/p\u003e \u003cp\u003e5.6 Algorithms Proposed During the NIST Standardization Procedure for Post-Quantum Cryptography 101\u003c\/p\u003e \u003cp\u003e5.7 Hardware Requirements of Post-Quantum Cryptographic Algorithms 101\u003c\/p\u003e \u003cp\u003e5.7.1 NTRUEncrypt 101\u003c\/p\u003e \u003cp\u003e5.7.1.1 Polynomial Multiplication 102\u003c\/p\u003e \u003cp\u003e5.7.1.2 Hardware to Accelerate NTRUEncrypt 103\u003c\/p\u003e \u003cp\u003e5.7.2 Hardware-Software Design to Implement PCQ Algorithms 103\u003c\/p\u003e \u003cp\u003e5.7.3 Implementation of Cryptographic Algorithms Using HLS 103\u003c\/p\u003e \u003cp\u003e5.8 Challenges on the Way of Post-Quantum Cryptography 104\u003c\/p\u003e \u003cp\u003e5.9 Post-Quantum Cryptography Versus Quantum Cryptography 105\u003c\/p\u003e \u003cp\u003e5.10 Future Prospects of Post-Quantum Cryptography 106\u003c\/p\u003e \u003cp\u003eReferences 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A Statistical Characterization of MCX Crude Oil Price with Regard to Persistence Behavior and Seasonal Anomaly 111\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnindita Bhattacharjee, Jaya Mamta Prosad and M.K. Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 111\u003c\/p\u003e \u003cp\u003e6.2 Related Literature 113\u003c\/p\u003e \u003cp\u003e6.3 Data Description and Methodology 114\u003c\/p\u003e \u003cp\u003e6.3.1 Data 114\u003c\/p\u003e \u003cp\u003e6.3.2 Methodology 115\u003c\/p\u003e \u003cp\u003e6.3.2.1 Characterizing Persistence Behavior of Crude Oil Return Time Series Using Hurst Exponent 115\u003c\/p\u003e \u003cp\u003e6.3.2.2 Zipf Plot 116\u003c\/p\u003e \u003cp\u003e6.3.2.3 Seasonal Anomaly in Oil Returns 117\u003c\/p\u003e \u003cp\u003e6.4 Analysis and Findings 117\u003c\/p\u003e \u003cp\u003e6.4.1 Persistence Behavior of Daily Oil Stock Price 117\u003c\/p\u003e \u003cp\u003e6.4.2 Detecting Seasonal Pattern in Oil Prices 121\u003c\/p\u003e \u003cp\u003e6.5 Conclusion and Implications 123\u003c\/p\u003e \u003cp\u003eReferences 125\u003c\/p\u003e \u003cp\u003eAppendix 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Some Fixed Point and Coincidence Point Results Involving G\u003csub\u003eα\u003c\/sub\u003e -Type Weakly Commuting Mappings 133\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKrishna Kanta Sarkar, Krishnapada Das and Abhijit Pramanink\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 133\u003c\/p\u003e \u003cp\u003e7.2 Definitions and Mathematical Preliminaries 134\u003c\/p\u003e \u003cp\u003e7.2.1 Definition: G-metric Space (G-ms) 134\u003c\/p\u003e \u003cp\u003e7.2.2 Definition: t-norm 135\u003c\/p\u003e \u003cp\u003e7.2.3 Definition: t-norm of Hadžić type (H-type) 135\u003c\/p\u003e \u003cp\u003e7.2.4 Definition: G-fuzzy metric space (G-fms) 135\u003c\/p\u003e \u003cp\u003e7.2.5 Definition 136\u003c\/p\u003e \u003cp\u003e7.2.6 Lemma 136\u003c\/p\u003e \u003cp\u003e7.2.7 Lemma 136\u003c\/p\u003e \u003cp\u003e7.2.8 Definition 136\u003c\/p\u003e \u003cp\u003e7.2.9 Definition 136\u003c\/p\u003e \u003cp\u003e7.2.10 Definition: Φ-Function 136\u003c\/p\u003e \u003cp\u003e7.2.11 Definition: Ψ-Function 137\u003c\/p\u003e \u003cp\u003e7.2.12 Lemma 137\u003c\/p\u003e \u003cp\u003e7.2.13 Definition 138\u003c\/p\u003e \u003cp\u003e7.2.14 Definition 138\u003c\/p\u003e \u003cp\u003e7.2.15 Definition 138\u003c\/p\u003e \u003cp\u003e7.2.16 Definition 138\u003c\/p\u003e \u003cp\u003e7.2.17 Definition 139\u003c\/p\u003e \u003cp\u003e7.2.18 Remarks 139\u003c\/p\u003e \u003cp\u003e7.2.19 Lemma 139\u003c\/p\u003e \u003cp\u003e7.3 Main Results 140\u003c\/p\u003e \u003cp\u003e7.3.1 Theorem 140\u003c\/p\u003e \u003cp\u003e7.3.2 Theorem 144\u003c\/p\u003e \u003cp\u003e7.3.3 Definition Ψ-Function 151\u003c\/p\u003e \u003cp\u003e7.3.4 Theorem 152\u003c\/p\u003e \u003cp\u003e7.3.5 Theorem 159\u003c\/p\u003e \u003cp\u003e7.3.6 Corollary 167\u003c\/p\u003e \u003cp\u003e7.3.7 Corollary 168\u003c\/p\u003e \u003cp\u003e7.3.8 Example 169\u003c\/p\u003e \u003cp\u003e7.3.9 Example 169\u003c\/p\u003e \u003cp\u003e7.3.10 Example 170\u003c\/p\u003e \u003cp\u003e7.3.11 Example 170\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 170\u003c\/p\u003e \u003cp\u003e7.5 Open Question 171\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Grobner Basis and Its Application in Motion of Robot Arm 173\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnjan Samanta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 173\u003c\/p\u003e \u003cp\u003e8.1.1 Define Orderings in K[y\u003csub\u003e1\u003c\/sub\u003e , ., y\u003csub\u003en\u003c\/sub\u003e] 174\u003c\/p\u003e \u003cp\u003e8.1.2 Introducing Division Rule in K[y\u003csub\u003e1\u003c\/sub\u003e , ., y\u003csub\u003en\u003c\/sub\u003e] 174\u003c\/p\u003e \u003cp\u003e8.2 Hilbert Basis Theorem and Grobner Basis 175\u003c\/p\u003e \u003cp\u003e8.3 Properties of Grobner Basis 175\u003c\/p\u003e \u003cp\u003e8.4 Applications of Grobner Basis 176\u003c\/p\u003e \u003cp\u003e8.4.1 Ideal Membership Problem 176\u003c\/p\u003e \u003cp\u003e8.4.2 Solving Polynomial Equations 177\u003c\/p\u003e \u003cp\u003e8.5 Application of Grobner Basis in Motion of Robot Arm 178\u003c\/p\u003e \u003cp\u003e8.5.1 Geometric Elucidation of Robots 178\u003c\/p\u003e \u003cp\u003e8.5.2 Mathematical Representation 179\u003c\/p\u003e \u003cp\u003e8.5.3 Forward Kinematic Problem 179\u003c\/p\u003e \u003cp\u003e8.5.4 Inverse Kinematic Problem 182\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 189\u003c\/p\u003e \u003cp\u003eReferences 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 A Review on the Formation of Pythagorean Triplets and Expressing an Integer as a Difference of Two Perfect Squares 191\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSouradip Roy, Tapabrata Bhattacharyya, Subhadip Roy, Souradeep Paul and Arpan Adhikary\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 191\u003c\/p\u003e \u003cp\u003e9.2 Calculation of Triples 193\u003c\/p\u003e \u003cp\u003e9.2.1 Calculation for Odd Numbers 193\u003c\/p\u003e \u003cp\u003e9.2.2 Calculation for Even Numbers 195\u003c\/p\u003e \u003cp\u003e9.2.3 Code Snippet 199\u003c\/p\u003e \u003cp\u003e9.2.4 Observation 200\u003c\/p\u003e \u003cp\u003e9.3 Computing the Number of Primitive Triples 200\u003c\/p\u003e \u003cp\u003e9.3.1 Calculation for Odd Numbers 200\u003c\/p\u003e \u003cp\u003e9.3.2 Calculation for Even Numbers 203\u003c\/p\u003e \u003cp\u003e9.3.3 Code Snippet 204\u003c\/p\u003e \u003cp\u003e9.3.4 Observation 205\u003c\/p\u003e \u003cp\u003e9.4 Representation of Integers as Difference of Two Perfect Squares 205\u003c\/p\u003e \u003cp\u003e9.4.1 Calculation for Odd Numbers 205\u003c\/p\u003e \u003cp\u003e9.4.2 Calculation for Even Numbers 206\u003c\/p\u003e \u003cp\u003e9.4.3 Corollaries 208\u003c\/p\u003e \u003cp\u003e9.4.4 Code Snippet 210\u003c\/p\u003e \u003cp\u003e9.4.5 Output 210\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 211\u003c\/p\u003e \u003cp\u003eReferences 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Solution of Matrix Games With Pay‐Offs of Single-Valued Neutrosophic Numbers and Its Application to Market Share Problem 213\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMijanur Rahaman Seikh and Shibaji Dutta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 213\u003c\/p\u003e \u003cp\u003e10.2 Preliminaries 216\u003c\/p\u003e \u003cp\u003e10.3 Matrix Games With SVNN Pay-Offs and Concept of Solution 218\u003c\/p\u003e \u003cp\u003e10.4 Mathematical Model Construction for SVNNMG 219\u003c\/p\u003e \u003cp\u003e10.4.1 Algorithm for Solving SVNNMG 223\u003c\/p\u003e \u003cp\u003e10.5 Numerical Example 224\u003c\/p\u003e \u003cp\u003e10.5.1 A Market Share Problem 224\u003c\/p\u003e \u003cp\u003e10.5.2 The Solution Procedure and Result Discussion 226\u003c\/p\u003e \u003cp\u003e10.5.3 Analysis and Comparison of Results With li and Nan’s Approach 227\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 228\u003c\/p\u003e \u003cp\u003eReferences 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 A Novel Score Function-Based EDAS Method for the Selection of a Vacant Post of a Company with q-Rung Orthopair Fuzzy Data 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eUtpal Mandal and Mijanur Rahaman Seikh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 231\u003c\/p\u003e \u003cp\u003e11.2 Preliminaries 234\u003c\/p\u003e \u003cp\u003e11.3 A Novel Score Function of q-ROFNs 236\u003c\/p\u003e \u003cp\u003e11.3.1 Some Existing q-ROF Score Functions 236\u003c\/p\u003e \u003cp\u003e11.3.2 A Novel Score Function of q-ROFNs 237\u003c\/p\u003e \u003cp\u003e11.4 EDAS Method for q-ROF MADM Problem 240\u003c\/p\u003e \u003cp\u003e11.5 Numerical Example 244\u003c\/p\u003e \u003cp\u003e11.6 Comparative Analysis 246\u003c\/p\u003e \u003cp\u003e11.7 Conclusions 247\u003c\/p\u003e \u003cp\u003eAcknowledgments 248\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Complete Generalized Soft Lattice 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManju John and Susha D.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 251\u003c\/p\u003e \u003cp\u003e12.2 Soft Sets and Soft Elements—Some Basic Concepts 252\u003c\/p\u003e \u003cp\u003e12.3 gs-Posets and gs-Chains 253\u003c\/p\u003e \u003cp\u003e12.4 Soft Isomorphism and Duality of gs-Posets 257\u003c\/p\u003e \u003cp\u003e12.5 gs-Lattices and Complete gs-Lattices 259\u003c\/p\u003e \u003cp\u003e12.6 s-Closure System and s-Moore Family 264\u003c\/p\u003e \u003cp\u003e12.7 Complete gs-Lattices From s-Closure Systems 266\u003c\/p\u003e \u003cp\u003e12.8 A Representation Theorem of a Complete gs-Lattice as an s-Closure System 267\u003c\/p\u003e \u003cp\u003e12.9 gs-Lattices and Fixed Point Theorem 268\u003c\/p\u003e \u003cp\u003eReferences 269\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Data Representation and Performance in a Prediction Model 271\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eApurbalal Senapati, Soumen Maji and Arunendu Mondal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 272\u003c\/p\u003e \u003cp\u003e13.1.1 Various Methods for Predictive Modeling 272\u003c\/p\u003e \u003cp\u003e13.1.2 Problem Definition 275\u003c\/p\u003e \u003cp\u003e13.2 Data Description and Representations 276\u003c\/p\u003e \u003cp\u003e13.3 Experiment and Result 281\u003c\/p\u003e \u003cp\u003e13.4 Error Analysis 282\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 283\u003c\/p\u003e \u003cp\u003eReferences 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Video Watermarking Technique Based on Motion Frames by Using Encryption Method 285\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePraful Saxena and Santosh Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 286\u003c\/p\u003e \u003cp\u003e14.2 Methodology Used 287\u003c\/p\u003e \u003cp\u003e14.2.1 Discrete Wavelet Transform 287\u003c\/p\u003e \u003cp\u003e14.2.2 Singular-Value Decomposition 289\u003c\/p\u003e \u003cp\u003e14.3 Literature Review 289\u003c\/p\u003e \u003cp\u003e14.4 Watermark Encryption 290\u003c\/p\u003e \u003cp\u003e14.5 Proposed Watermarking Scheme 292\u003c\/p\u003e \u003cp\u003e14.5.1 Watermark Embedding 292\u003c\/p\u003e \u003cp\u003e14.5.2 Watermark Extraction 294\u003c\/p\u003e \u003cp\u003e14.6 Experimental Results 296\u003c\/p\u003e \u003cp\u003e14.7 Conclusion 297\u003c\/p\u003e \u003cp\u003eReferences 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Feature Extraction and Selection for Classification of Brain Tumors 299\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSaswata Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 299\u003c\/p\u003e \u003cp\u003e15.2 Related Work 301\u003c\/p\u003e \u003cp\u003e15.3 Methodology 303\u003c\/p\u003e \u003cp\u003e15.3.1 Contrast Enhancement 303\u003c\/p\u003e \u003cp\u003e15.3.2 K-Means Clustering 303\u003c\/p\u003e \u003cp\u003e15.3.3 Canny Edge Detection 305\u003c\/p\u003e \u003cp\u003e15.3.4 Feature Extraction 308\u003c\/p\u003e \u003cp\u003e15.3.5 Feature Selection 309\u003c\/p\u003e \u003cp\u003e15.3.5.1 Genetic Algorithm for Feature Selection 309\u003c\/p\u003e \u003cp\u003e15.3.5.2 Particle Swarm Optimization for Feature Selection 311\u003c\/p\u003e \u003cp\u003e15.4 Results 313\u003c\/p\u003e \u003cp\u003e15.5 Future Scope 313\u003c\/p\u003e \u003cp\u003e15.6 Conclusion 314\u003c\/p\u003e \u003cp\u003eReferences 315\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Student’s Self-Esteem on the Self-Learning Module in Mathematics 6 317\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAriel Gulla Villar and Biswadip Basu Mallik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 318\u003c\/p\u003e \u003cp\u003e16.1.1 Research Questions 318\u003c\/p\u003e \u003cp\u003e16.1.2 Scope and Limitation 319\u003c\/p\u003e \u003cp\u003e16.1.3 Significance of the Study 319\u003c\/p\u003e \u003cp\u003e16.2 Methodology 320\u003c\/p\u003e \u003cp\u003e16.2.1 Research Design 320\u003c\/p\u003e \u003cp\u003e16.2.2 Respondents of the Study 320\u003c\/p\u003e \u003cp\u003e16.2.3 Sampling Procedure 320\u003c\/p\u003e \u003cp\u003e16.2.4 Locale of the Study 320\u003c\/p\u003e \u003cp\u003e16.2.5 Data Collection 321\u003c\/p\u003e \u003cp\u003e16.2.6 Instrument of the Study 321\u003c\/p\u003e \u003cp\u003e16.2.7 Validation of Instrument 321\u003c\/p\u003e \u003cp\u003e16.3 Results and Discussion 322\u003c\/p\u003e \u003cp\u003e16.4 Conclusion 329\u003c\/p\u003e \u003cp\u003e16.5 Recommendation 330\u003c\/p\u003e \u003cp\u003eReferences 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Effects on Porous Nanofluid due to Internal Heat Generation and Homogeneous Chemical Reaction 333\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHiranmoy Mondal and Sharmistha Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eNomenclature 334\u003c\/p\u003e \u003cp\u003e17.1 Introduction 334\u003c\/p\u003e \u003cp\u003e17.2 Mathematical Formulations 336\u003c\/p\u003e \u003cp\u003e17.3 Method of Local Nonsimilarity 341\u003c\/p\u003e \u003cp\u003e17.4 Results and Discussions 342\u003c\/p\u003e \u003cp\u003e17.5 Concluding Remarks 348\u003c\/p\u003e \u003cp\u003eReferences 349\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Numerical Solution of Partial Differential Equations: Finite Difference Method 353\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRoushan Kumar, Rakhi Tiwari and Rashmi Prasad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 353\u003c\/p\u003e \u003cp\u003e18.2 Finite Difference Method 356\u003c\/p\u003e \u003cp\u003e18.2.1 Finite Difference Approximations to Derivatives 356\u003c\/p\u003e \u003cp\u003e18.2.2 Discretization of Domain 356\u003c\/p\u003e \u003cp\u003e18.2.3 Difference Scheme of Partial Differential Equation 358\u003c\/p\u003e \u003cp\u003e18.3 Multilevel Explicit Difference Schemes 360\u003c\/p\u003e \u003cp\u003e18.4 Two-Level Implicit Scheme 364\u003c\/p\u003e \u003cp\u003e18.5 Conclusion 371\u003c\/p\u003e \u003cp\u003eReferences 371\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Godel Code Enciphering for QKD Protocol Using DNA Mapping 373\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePartha Sarathi Goswami and Tamal Chakraborty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 374\u003c\/p\u003e \u003cp\u003e19.2 Related Work 375\u003c\/p\u003e \u003cp\u003e19.3 The DNA Code Set 376\u003c\/p\u003e \u003cp\u003e19.4 Godel Code 376\u003c\/p\u003e \u003cp\u003e19.5 Key Exchange Protocol 378\u003c\/p\u003e \u003cp\u003e19.6 Encoding and Decoding of the Plain Text— The QKD Protocol 378\u003c\/p\u003e \u003cp\u003e19.6.1 Plain Text to Encoded Text and Vice-Versa 379\u003c\/p\u003e \u003cp\u003e19.6.2 The Proposed Message Passing Scheme 380\u003c\/p\u003e \u003cp\u003e19.6.3 Illustration 381\u003c\/p\u003e \u003cp\u003e19.7 Experimental Setup 388\u003c\/p\u003e \u003cp\u003e19.8 Detection Probability and Dark Counts 389\u003c\/p\u003e \u003cp\u003e19.9 Security Analysis of Our Algorithm 390\u003c\/p\u003e \u003cp\u003e19.10 Conclusion 393\u003c\/p\u003e \u003cp\u003eReferences 393\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Predictive Analysis of Stock Prices Through Scikit-Learn: Machine Learning in Python 397\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVikash Kumar Mishra, Richa Binyala, Pratibha Sharma and Simran Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 397\u003c\/p\u003e \u003cp\u003e20.2 Study Area and Dataset 398\u003c\/p\u003e \u003cp\u003e20.3 Methodology 399\u003c\/p\u003e \u003cp\u003e20.4 Results 401\u003c\/p\u003e \u003cp\u003e20.5 Conclusion 402\u003c\/p\u003e \u003cp\u003eReferences 403\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Pose Estimation Using Machine Learning and Feature Extraction 405\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJ. Palanimeera and K. Ponmozhi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 406\u003c\/p\u003e \u003cp\u003e21.2 Related Work 408\u003c\/p\u003e \u003cp\u003e21.3 Proposed Work 409\u003c\/p\u003e \u003cp\u003e21.3.1 Yoga Posture Identification 410\u003c\/p\u003e \u003cp\u003e21.3.1.1 Deep Extraction of a Normal Image 410\u003c\/p\u003e \u003cp\u003e21.3.1.2 Human Joints Identification 411\u003c\/p\u003e \u003cp\u003e21.3.1.3 Extraction of L-DoD Features 411\u003c\/p\u003e \u003cp\u003e21.3.1.4 Extraction of D-GoD Features 415\u003c\/p\u003e \u003cp\u003e21.3.2 The Random Forest Classifier’s Design 416\u003c\/p\u003e \u003cp\u003e21.3.2.1 Construction of a Random Forest Model 416\u003c\/p\u003e \u003cp\u003e21.3.2.2 Random Forest Two-Way Voting 417\u003c\/p\u003e \u003cp\u003e21.3.3 Joint Positioning in Humans 418\u003c\/p\u003e \u003cp\u003e21.4 Outcome and Discussion 420\u003c\/p\u003e \u003cp\u003e21.5 Conclusion 422\u003c\/p\u003e \u003cp\u003eReferences 423\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 E-Commerce Data Analytics Using Web Scraping 425\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVikash Kumar Mishra, Bosco Paul Alapatt, Aaditya Aggarwal and Divya Khemani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 425\u003c\/p\u003e \u003cp\u003e22.1.1 Uses of Web Scraping 426\u003c\/p\u003e \u003cp\u003e22.2 Research Objective 426\u003c\/p\u003e \u003cp\u003e22.3 Literature Review 427\u003c\/p\u003e \u003cp\u003e22.4 Feasibility and Application 428\u003c\/p\u003e \u003cp\u003e22.4.1 Web Scrapers Process 428\u003c\/p\u003e \u003cp\u003e22.5 Proposed Methodology 428\u003c\/p\u003e \u003cp\u003e22.5.1 Coding Phase 429\u003c\/p\u003e \u003cp\u003e22.5.2 Spreadsheet Analysis and Results 432\u003c\/p\u003e \u003cp\u003e22.6 Conclusion 433\u003c\/p\u003e \u003cp\u003eReferences 433\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 A New Language-Generating Mechanism of SNPSSP 435\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrithwineel Paul, Soumadip Ghosh and Anjan Pal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 436\u003c\/p\u003e \u003cp\u003e23.2 Spiking Neural P Systems With Structural Plasticity ((SNPSSP) 437\u003c\/p\u003e \u003cp\u003e23.3 Labeled SNPSSP (LSNPSSP) 440\u003c\/p\u003e \u003cp\u003e23.3.1 Working of LSNPSSP 441\u003c\/p\u003e \u003cp\u003e23.4 Main Results 442\u003c\/p\u003e \u003cp\u003e23.5 Conclusion 450\u003c\/p\u003e \u003cp\u003eReferences 450\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Performance Analysis and Interpretation Using Data Visualization 455\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVikash Kumar Mishra, Iyyappan, M., Muskan Soni and Neha Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 455\u003c\/p\u003e \u003cp\u003e24.2 Selecting Data Set 456\u003c\/p\u003e \u003cp\u003e24.3 Proposed Methodology 457\u003c\/p\u003e \u003cp\u003e24.4 Results 458\u003c\/p\u003e \u003cp\u003e24.5 Conclusion 460\u003c\/p\u003e \u003cp\u003eReferences 460\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Dealing with Missing Values in a Relation Dataset Using the DROPNA Function in Python 463\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVikash Kumar Mishra, Shoney Sebastian, Maria Iqbal and Yashwin Anand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 464\u003c\/p\u003e \u003cp\u003e25.2 Background 464\u003c\/p\u003e \u003cp\u003e25.3 Study Area and Data Set 464\u003c\/p\u003e \u003cp\u003e25.4 Methodology 466\u003c\/p\u003e \u003cp\u003e25.5 Results 468\u003c\/p\u003e \u003cp\u003e25.6 Conclusion 468\u003c\/p\u003e \u003cp\u003e25.7 Acknowledgment 469\u003c\/p\u003e \u003cp\u003eReferences 469\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 A Dynamic Review of the Literature on Blockchain-Based Logistics Management 471\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eC. Devi Parameswari and M. Ilayaraja\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 471\u003c\/p\u003e \u003cp\u003e26.2 Blockchain Concepts and Framework 473\u003c\/p\u003e \u003cp\u003e26.3 Study of the Literature 475\u003c\/p\u003e \u003cp\u003e26.3.1 Blockchain Technology and Supply Chain Trust 475\u003c\/p\u003e \u003cp\u003e26.4 Challenges and Processes of Supply Chain Transparency 477\u003c\/p\u003e \u003cp\u003e26.4.1 Motivation for Transparency in Data 478\u003c\/p\u003e \u003cp\u003e26.5 Challenges in Security 478\u003c\/p\u003e \u003cp\u003e26.6 Discussion: In Terms of Supply Chain Dynamics, Blockchain Technology and Supply Chain Integration 479\u003c\/p\u003e \u003cp\u003e26.7 Conclusion 481\u003c\/p\u003e \u003cp\u003eAcknowledgment 481\u003c\/p\u003e \u003cp\u003eReferences 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Prediction of Seasonal Aliments Using Big Data: A Case Study 485\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK. Indhumathi and K. Sathesh Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 486\u003c\/p\u003e \u003cp\u003e27.2 Related Works 486\u003c\/p\u003e \u003cp\u003e27.3 Conclusion 489\u003c\/p\u003e \u003cp\u003eReferences 490\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Implementation of Tokenization in Natural Language Processing Using NLTK Module of Python 493\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVikash Kumar Mishra, Abhimanyu Dhyani, Sushree Barik and Tanish Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 493\u003c\/p\u003e \u003cp\u003e28.2 Background 494\u003c\/p\u003e \u003cp\u003e28.3 Study Area and Data Set 495\u003c\/p\u003e \u003cp\u003e28.4 Proposed Methodology 495\u003c\/p\u003e \u003cp\u003e28.5 Result 498\u003c\/p\u003e \u003cp\u003e28.6 Conclusion 500\u003c\/p\u003e \u003cp\u003e28.7 Acknowledgment 501\u003c\/p\u003e \u003cp\u003eConflicts of Interest\/Competing Interests 501\u003c\/p\u003e \u003cp\u003eAvailability of Data and Material 503\u003c\/p\u003e \u003cp\u003eReferences 503\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Application of Nanofluids in Heat Exchanger and its Computational Fluid Dynamics 505\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM. Appadurai, E. Fantin Irudaya Raj and M. Chithambara Thanu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Computational Fluid Dynamics 506\u003c\/p\u003e \u003cp\u003e29.1.1 Continuity Equation 506\u003c\/p\u003e \u003cp\u003e29.1.2 Momentum Equation 507\u003c\/p\u003e \u003cp\u003e29.1.3 Energy Equation 508\u003c\/p\u003e \u003cp\u003e29.1.4 Equations for Turbulent Flows 510\u003c\/p\u003e \u003cp\u003e29.2 Nanofluids 510\u003c\/p\u003e \u003cp\u003e29.2.1 Viscosity 511\u003c\/p\u003e \u003cp\u003e29.2.2 Density 511\u003c\/p\u003e \u003cp\u003e29.2.3 Heat Capacity 512\u003c\/p\u003e \u003cp\u003e29.2.4 Thermal Conductivity 512\u003c\/p\u003e \u003cp\u003e29.3 Preparation of Nanofluids 512\u003c\/p\u003e \u003cp\u003e29.3.1 One-Step Method 513\u003c\/p\u003e \u003cp\u003e29.3.2 Two-Step Method 514\u003c\/p\u003e \u003cp\u003e29.3.3 Nanofluids Implementation in Heat Exchanger 515\u003c\/p\u003e \u003cp\u003e29.4 Use of Computational Fluid Dynamics for Nanofluids 517\u003c\/p\u003e \u003cp\u003e29.5 CFD Approach to Solve Heat Exchanger 518\u003c\/p\u003e \u003cp\u003e29.6 Conclusion 522\u003c\/p\u003e \u003cp\u003eReferences 522\u003c\/p\u003e \u003cp\u003eAbout the Editors 525\u003c\/p\u003e \u003cp\u003eIndex 527 \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49528866177367,"sku":"9781119879671","price":208.68,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119879671.jpg?v=1731873334","url":"https:\/\/bookcurl.com\/products\/mathematics-and-computer-science-volume-1-9781119879671","provider":"Book Curl","version":"1.0","type":"link"}