Mathematics and Computer Science, Volume 1

Name: Mathematics and Computer Science, Volume 1
Brand: Wiley
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Sharmistha Ghosh M. Niranjanamurthy Krishanu Deyasi Biswadip Basu Mallik Santanu Das(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 4. Juli 2023

560 Seiten

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Sharmistha Ghosh, PhD, is a professor at the Institute of Engineering and Management, India. She received her doctorate in mathematics from the Indian Institute of Technology, India. Her major field of study includes fuzzy and vague databases as well as computational fluid dynamics, and she has published several papers in scientific journals. She is also the editor of several scientific journals and works as a reviewer of journals as well as doctoral theses in India as well abroad.

M. Niranjanamurthy, PhD, is an assistant professor in the Department of Computer Applications, M S Ramaiah Institute of Technology, Bangalore, Karnataka. He earned his PhD in computer science at JJTU, Rajasthan, India. He has over 11 years of teaching experience and two years of industry experience as a software engineer. He has published several books, and he is working on numerous books for Scrivener Publishing. He has published over 60 papers for scholarly journals and conferences, and he is working as a reviewer in 22 scientific journals. He also has numerous awards to his credit.

Krishanu Deyasi is an associate professor in the Department of Basic Sciences and Humanities at the Institute of Engineering and Management, India. He earned his PhD from the Indian Institute of Science Education and Research, and he has postdoctoral experience from The Institute of Mathematical Sciences, India. He has written three books and has published papers in scientific journals. He is also an editor for several scientific journals.

Biswadip Basu Mallik is a senior assistant professor of mathematics in the Department of Basic Sciences and Humanities at the Institute of Engineering and Management, India. He has been involved in teaching and research for more than 21 years and has published several research papers in various scientific journals along with book chapters with various publishers. He has authored five books and has five patents to his credit. He is a managing editor of the Journal of Mathematical Sciences & Computational Mathematics and is an editorial board member and reviewer for several scientific journals.

Santanu Das is an assistant professor in the Department of Basic Sciences and Humanities at the Institute of Engineering and Management, India. He completed his undergraduate degree, and is pursuing his doctorate, from Jadavpur University.

Inhalt

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 Error Estimation of the Function by (Zrµ, r =1) Using Product Means (E,s)(N, pn,qn) of the Conjugate Fourier Series
1.1 Introduction
1.1.1 Definition 1
1.1.2 Definition 2
1.1.3 Definition 3
1.2 Theorems
1.2.1 Theorem 1
1.2.2 Theorem 2
1.3 Lemmas
1.3.1 Lemma 1
1.3.2 Lemma 2
1.3.3 Lemma 3
1.4 Proof of the Theorems
1.4.1 Proof of the Theorem 1
1.4.2 Proof of the Theorem 2
1.5 Corollaries
1.5.1 Corollary 1
1.5.2 Corollary 2
1.6 Example
1.7 Conclusion
References
Chapter 2 Blow Up and Decay of Solutions for a Klein-Gordon Equation With Delay and Variable Exponents
2.1 Introduction
2.2 Preliminaries
2.3 Blow Up of Solutions
2.4 Decay of Solutions
Acknowledgment
References
Chapter 3 Some New Inequalities Via Extended Generalized Fractional Integral Operator for Chebyshev Functional
3.1 Introduction
3.2 Preliminaries
3.3 Fractional Inequalities for the Chebyshev Functional
3.4 Fractional Inequalities in the Case of Extended Chebyshev Functional
3.5 Some Other Fracional Inequalities Related to the Extended Chebyshev Functional
3.6 Concluding Remark
References
Chapter 4 Blow Up of the Higher-Order Kirchhoff-Type System With Logarithmic Nonlinearities
4.1 Introduction
4.2 Preliminaries
4.3 Blow Up for Problem for E (0) & d
4.4 Conclusion
References
Chapter 5 Developments in Post-Quantum Cryptography
5.1 Introduction
5.2 Modern-Day Cryptography
5.2.1 Symmetric Cryptosystems
5.2.2 Asymmetric Cryptosystems
5.2.3 Attacks on Modern Cryptosystems
5.2.3.1 Known Attacks
5.2.3.2 Side-Channel Attacks
5.3 Quantum Computing
5.3.1 The Main Aspects of Quantum Computing
5.3.2 Shor's Algorithm
5.3.3 Grover's Algorithm
5.3.4 The Need for Post-Quantum Cryptography
5.4 Algorithms Proposed for Post-Quantum Cryptography
5.4.1 Code-Based Cryptography
5.4.2 Lattice-Based Cryptography
5.4.3 Multivariate Cryptography
5.4.4 Hash-Based Cryptography
5.4.5 Supersingular Elliptic Curve Isogeny Cryptography
5.4.6 Quantum-Resistant Symmetric Key Cryptography
5.5 Launching of the Project Called "Open Quantum Safe"
5.6 Algorithms Proposed During the NIST Standardization Procedure for Post-Quantum Cryptography
5.7 Hardware Requirements of Post-Quantum Cryptographic Algorithms
5.7.1 NTRUEncrypt
5.7.1.1 Polynomial Multiplication
5.7.1.2 Hardware to Accelerate NTRUEncrypt
5.7.2 Hardware-Software Design to Implement PCQ Algorithms
5.7.3 Implementation of Cryptographic Algorithms Using HLS
5.8 Challenges on the Way of Post-Quantum Cryptography
5.9 Post-Quantum Cryptography Versus Quantum Cryptography
5.10 Future Prospects of Post-Quantum Cryptography
References
Chapter 6 A Statistical Characterization of MCX Crude Oil Price with Regard to Persistence Behavior and Seasonal Anomaly
6.1 Introduction
6.2 Related Literature
6.3 Data Description and Methodology
6.3.1 Data
6.3.2 Methodology
6.3.2.1 Characterizing Persistence Behavior of Crude Oil Return Time Series Using Hurst Exponent
6.3.2.2 Zipf Plot
6.3.2.3 Seasonal Anomaly in Oil Returns
6.4 Analysis and Findings
6.4.1 Persistence Behavior of Daily Oil Stock Price
6.4.2 Detecting Seasonal Pattern in Oil Prices
6.5 Conclusion and Implications
References
Appendix
Chapter 7 Some Fixed Point and Coincidence Point Results Involving Ga-Type Weakly Commuting Mappings
7.1 Introduction
7.2 Definitions and Mathematical Preliminaries
7.2.1 Definition: G-metric Space (G-ms)
7.2.2 Definition: t-norm
7.2.3 Definition: t-norm of Hadzic type (H-type)
7.2.4 Definition: G-fuzzy metric space (G-fms)
7.2.5 Definition
7.2.6 Lemma
7.2.7 Lemma
7.2.8 Definition
7.2.9 Definition
7.2.10 Definition: F-Function
7.2.11 Definition: ?-Function
7.2.12 Lemma
7.2.13 Definition
7.2.14 Definition
7.2.15 Definition
7.2.16 Definition
7.2.17 Definition
7.2.18 Remarks
7.2.19 Lemma
7.3 Main Results
7.3.1 Theorem
7.3.2 Theorem
7.3.3 Definition ?-Function
7.3.4 Theorem
7.3.5 Theorem
7.3.6 Corollary
7.3.7 Corollary
7.3.8 Example
7.3.9 Example
7.3.10 Example
7.3.11 Example
7.4 Conclusion
7.5 Open Question
References
Chapter 8 Grobner Basis and Its Application in Motion of Robot Arm
8.1 Introduction
8.1.1 Define Orderings in K[y1, ..., yn]
8.1.2 Introducing Division Rule in K[y1, ..., yn]
8.2 Hilbert Basis Theorem and Grobner Basis
8.3 Properties of Grobner Basis
8.4 Applications of Grobner Basis
8.4.1 Ideal Membership Problem
8.4.2 Solving Polynomial Equations
8.5 Application of Grobner Basis in Motion of Robot Arm
8.5.1 Geometric Elucidation of Robots
8.5.2 Mathematical Representation
8.5.3 Forward Kinematic Problem
8.5.4 Inverse Kinematic Problem
8.6 Conclusion
References
Chapter 9 A Review on the Formation of Pythagorean Triplets and Expressing an Integer as a Difference of Two Perfect Squares
9.1 Introduction
9.2 Calculation of Triples
9.2.1 Calculation for Odd Numbers
9.2.2 Calculation for Even Numbers
9.2.3 Code Snippet
9.2.4 Observation
9.3 Computing the Number of Primitive Triples
9.3.1 Calculation for Odd Numbers
9.3.2 Calculation for Even Numbers
9.3.3 Code Snippet
9.3.4 Observation
9.4 Representation of Integers as Difference of Two Perfect Squares
9.4.1 Calculation for Odd Numbers
9.4.2 Calculation for Even Numbers
9.4.3 Corollaries
9.4.4 Code Snippet
9.4.5 Output
9.5 Conclusion
References
Chapter 10 Solution of Matrix Games With Pay.Offs of Single-Valued Neutrosophic Numbers and Its Application to Market Share Problem
10.1 Introduction
10.2 Preliminaries
10.3 Matrix Games With SVNN Pay-Offs and Concept of Solution
10.4 Mathematical Model Construction for SVNNMG
10.4.1 Algorithm for Solving SVNNMG
10.5 Numerical Example
10.5.1 A Market Share Problem
10.5.2 The Solution Procedure and Result Discussion
10.5.3 Analysis and Comparison of Results With Li and Nan?fs Approach
10.6 Conclusion
References
Chapter 11 A Novel Score Function-Based EDAS Method for the Selection of a Vacant Post of a Company with q-Rung Orthopair Fuzzy Data
11.1 Introduction
11.2 Preliminaries
11.3 A Novel Score Function of q-ROFNs
11.3.1 Some Existing q-ROF Score Functions
11.3.2 A Novel Score Function of q-ROFNs
11.4 EDAS Method for q-ROF MADM Problem
11.5 Numerical Example
11.6 Comparative Analysis
11.7 Conclusions
Acknowledgments
References
Chapter 12 Complete Generalized Soft Lattice
12.1 Introduction
12.2 Soft Sets and Soft Elements.Some Basic Concepts
12.3 gs-Posets and gs-Chains
12.4 Soft Isomorphism and Duality of gs-Posets
12.5 gs-Lattices and Complete gs-Lattices
12.6 s-Closure System and s-Moore Family
12.7 Complete gs-Lattices From s-Closure Systems
12.8 A Representation Theorem of a Complete gs-Lattice as an s-Closure System
12.9 gs-Lattices and Fixed Point Theorem
References
Chapter 13 Data Representation and Performance in a Prediction Model
13.1 Introduction
13.1.1 Various Methods for Predictive Modeling
13.1.2 Problem Definition
13.2 Data Description and Representations
13.3 Experiment and Result
13.4 Error Analysis
13.5 Conclusion
References
Chapter 14 Video Watermarking Technique Based on Motion Frames by Using Encryption Method
14.1 Introduction
14.2 Methodology Used
14.2.1 Discrete Wavelet Transform
14.2.2 Singular-Value Decomposition
14.3 Literature Review
14.4 Watermark Encryption
14.5 Proposed Watermarking Scheme
14.5.1 Watermark Embedding
14.5.2 Watermark Extraction
14.6 Experimental Results
14.7 Conclusion
References
Chapter 15 Feature Extraction and Selection for Classification of Brain Tumors
15.1 Introduction
15.2 Related Work
15.3 Methodology
15.3.1 Contrast Enhancement
15.3.2 K-Means Clustering
15.3.3 Canny Edge Detection
15.3.4 Feature Extraction
15.3.5 Feature Selection
15.3.5.1 Genetic Algorithm for Feature Selection
15.3.5.2 Particle Swarm Optimization for Feature Selection
15.4 Results
15.5 Future Scope
15.6 Conclusion
References
Chapter 16 Student's Self-Esteem on the Self-Learning Module in Mathematics 6
16.1 Introduction
16.1.1 Research Questions
16.1.2 Scope and Limitation
16.1.3 Significance of the Study
16.2 Methodology
16.2.1 Research Design
16.2.2 Respondents of the Study
16.2.3 Sampling Procedure
16.2.4 Locale of the Study
16.2.5 Data Collection
16.2.6 Instrument of the Study
16.2.7 Validation of Instrument
16.3 Results and Discussion
16.4 Conclusion
16.5 Recommendation
References
Chapter 17 Effects on Porous Nanofluid due to Internal Heat Generation and Homogeneous Chemical Reaction
Nomenclature
17.1 Introduction
17.2 Mathematical Formulations
17.3 Method of Local Nonsimilarity
17.4 Results and Discussions
17.5 Concluding Remarks
References
Chapter 18 Numerical Solution of Partial Differential Equations: Finite Difference Method
18.1 Introduction
18.2 Finite Difference Method
18.2.1 Finite Difference Approximations to Derivatives
18.2.2 Discretization of Domain
18.2.3 Difference Scheme of Partial Differential Equation
18.3 Multilevel Explicit Difference Schemes
18.4 Two-Level Implicit Scheme
18.5 Conclusion
References
Chapter 19 Godel Code Enciphering for QKD Protocol Using DNA Mapping
19.1 Introduction
19.2 Related Work
19.3 The DNA Code Set
19.4 Godel Code
19.5 Key Exchange Protocol
19.6 Encoding and Decoding of the Plain Text-The QKD Protocol
19.6.1 Plain Text to Encoded Text and Vice-Versa
19.6.2 The Proposed Message Passing Scheme
19.6.3 Illustration
19.7 Experimental Setup
19.8 Detection Probability and Dark Counts
19.9 Security Analysis of Our Algorithm
19.10 Conclusion
References
Chapter 20 Predictive Analysis of Stock Prices Through Scikit-Learn: Machine Learning in Python
20.1 Introduction
20.2 Study Area and Dataset
20.3 Methodology
20.4 Results
20.5 Conclusion
References
Chapter 21 Pose Estimation Using Machine Learning and Feature Extraction
21.1 Introduction
21.2 Related Work
21.3 Proposed Work
21.3.1 Yoga Posture Identification
21.3.1.1 Deep Extraction of a Normal Image
21.3.1.2 Human Joints Identification
21.3.1.3 Extraction of L-DoD Features
21.3.1.4 Extraction of D-GoD Features
21.3.2 The Random Forest Classifier's Design
21.3.2.1 Construction of a Random Forest Model
21.3.2.2 Random Forest Two-Way Voting
21.3.3 Joint Positioning in Humans
21.4 Outcome and Discussion
21.5 Conclusion
References
Chapter 22 E-Commerce Data Analytics Using Web Scraping
22.1 Introduction
22.1.1 Uses of Web Scraping
22.2 Research Objective
22.3 Literature Review
22.4 Feasibility and Application
22.4.1 Web Scrapers Process
22.5 Proposed Methodology
22.5.1 Coding Phase
22.5.2 Spreadsheet Analysis and Results
22.6 Conclusion
References
Chapter 23 A New Language-Generating Mechanism of SNPSSP
23.1 Introduction
23.2 Spiking Neural P Systems With Structural Plasticity (SNPSSP)
23.3 Labeled SNPSSP (LSNPSSP)
23.3.1 Working of LSNPSSP
23.4 Main Results
23.5 Conclusion
References
Chapter 24 Performance Analysis and Interpretation Using Data Visualization
24.1 Introduction
24.2 Selecting Data Set
24.3 Proposed Methodology
24.4 Results
24.5 Conclusion
References
Chapter 25 Dealing with Missing Values in a Relation Dataset Using the DROPNA Function in Python
25.1 Introduction
25.2 Background
25.3 Study Area and Data Set
25.4 Methodology
25.5 Results
25.6 Conclusion
25.7 Acknowledgment
References
Chapter 26 A Dynamic Review of the Literature on Blockchain-Based Logistics Management
26.1 Introduction
26.2 Blockchain Concepts and Framework
26.3 Study of the Literature
26.3.1 Blockchain Technology and Supply Chain Trust
26.4 Challenges and Processes of Supply Chain Transparency
26.4.1 Motivation for Transparency in Data
26.5 Challenges in Security
26.6 Discussion: In Terms of Supply Chain Dynamics, Blockchain Technology and Supply Chain Integration
26.7 Conclusion
Acknowledgment
References
Chapter 27 Prediction of Seasonal Aliments Using Big Data: A Case Study
27.1 Introduction
27.2 Related Works
27.3 Conclusion
References
Chapter 28 Implementation of Tokenization in Natural Language Processing Using NLTK Module of Python
28.1 Introduction
28.2 Background
28.3 Study Area and Data Set
28.4 Proposed Methodology
28.5 Result
28.6 Conclusion
28.7 Acknowledgment
Conflicts of Interest/Competing Interests
Availability of Data and Material
References
Chapter 29 Application of Nanofluids in Heat Exchanger and its Computational Fluid Dynamics
29.1 Computational Fluid Dynamics
29.1.1 Continuity Equation
29.1.2 Momentum Equation
29.1.3 Energy Equation
29.1.4 Equations for Turbulent Flows
29.2 Nanofluids
29.2.1 Viscosity
29.2.2 Density
29.2.3 Heat Capacity
29.2.4 Thermal Conductivity
29.3 Preparation of Nanofluids
29.3.1 One-Step Method
29.3.2 Two-Step Method
29.3.3 Nanofluids Implementation in Heat Exchanger
29.4 Use of Computational Fluid Dynamics for Nanofluids
29.5 CFD Approach to Solve Heat Exchanger
29.6 Conclusion
References
About the Editors
Index
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