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 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.
Scikit-learn, a tool for developing Machine Learning algorithms, is a standard library of Python. Through Scikit-learn, a trained model for predictive analysis can be developed. Such models aim to provide accurate predictions. Stock predictions are based on changes and patterns identified in the historical dataset. Following the trends and patterns of the historical changes of stocks, Machine Learning algorithms can be developed for achieving accurate outcomes. An effective model is developed, which enhance the working pattern or performance of the machine that further helps to draw a precise analysis of stocks.
Python provides a rich data structure library called Pandas, which provides fast and efficient data transformation and analysis. The word Pandas is an abbreviation of Python Data Analysis Library. Pandas facilitate optimized and dynamic data structure designs work with “relational” or “labeled” data. Python’s approach is meant to provide a high-level, high-performance building block that can be used to do real-world analysis of data. Pandas Library is allowing users to import data from different file formats, such as CSV, SQL, Microsoft Excel etc. It helps in data preparation, as well as in data modeling, for those projects, which aims data analysis for the extraction of information. Python’s future will be built on this layer for statistical computing. In addition to discussing future areas of work and growth opportunities for statistics and data analytics applications built on Python, the study provides details about the language’s design and features. In this research paper, we intend to solve the problem of missing values in a dataset using the DROPNA function in Python using Pandas library.
Author(s): Sharmistha Ghosh; M. Niranjanamurthy; Krishanu Deyasi; Biswadip Basu Mallik; Santanu Das
Publisher: Wiley-Scrivener
Year: 2023
Language: English
Pages: 560
Cover
Series Page
Title Page
Copyright Page
Preface
1 Error Estimation of the Function by Using Product Means of the Conjugate Fourier Series
1.1 Introduction
1.2 Theorems
1.3 Lemmas
1.4 Proof of the Theorems
1.5 Corollaries
1.6 Example
1.7 Conclusion
References
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
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
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) < (0) < d
4.4 Conclusion
References
5 Developments in Post-Quantum Cryptography
5.1 Introduction
5.2 Modern-Day Cryptography
5.3 Quantum Computing
5.4 Algorithms Proposed for Post-Quantum 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
5.8 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
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
6.4 Analysis and Findings
6.5 Conclusion and Implications
References
Appendix
7 Some Fixed Point and Coincidence Point Results Involving Gα-Type Weakly Commuting Mappings
7.1 Introduction
7.2 Definitions and Mathematical Preliminaries
7.3 Main Results
7.4 Conclusion
7.5 Open Question
References
8 Grobner Basis and Its Application in Motion of Robot Arm
8.1 Introduction
8.2 Hilbert Basis Theorem and Grobner Basis
8.3 Properties of Grobner Basis
8.4 Applications of Grobner Basis
8.5 Application of Grobner Basis in Motion of Robot Arm
8.6 Conclusion
References
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
9.3 Computing the Number of Primitive Triples
9.4 Representation of Integers as Difference of Two Perfect Squares
9.5 Conclusion
References
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
10.5 Numerical Example
10.6 Conclusion
References
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
11.4 EDAS Method for q-ROF MADM Problem
11.5 Numerical Example
11.6 Comparative Analysis
11.7 Conclusions
Acknowledgments
References
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
13 Data Representation and Performance in a Prediction Model
13.1 Introduction
13.2 Data Description and Representations
13.3 Experiment and Result
13.4 Error Analysis
13.5 Conclusion
References
14 Video Watermarking Technique Based on Motion Frames by Using Encryption Method
14.1 Introduction
14.2 Methodology Used
14.3 Literature Review
14.4 Watermark Encryption
14.5 Proposed Watermarking Scheme
14.6 Experimental Results
14.7 Conclusion
References
15 Feature Extraction and Selection for Classification of Brain Tumors
15.1 Introduction
15.2 Related Work
15.3 Methodology
15.4 Results
15.5 Future Scope
15.6 Conclusion
References
16 Student’s Self-Esteem on the Self-Learning Module in Mathematics 1
16.1 Introduction
16.2 Methodology
16.3 Results and Discussion
16.4 Conclusion
16.5 Recommendation
References
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
18 Numerical Solution of Partial Differential Equations: Finite Difference Method
18.1 Introduction
18.2 Finite Difference Method
18.3 Multilevel Explicit Difference Schemes
18.4 Two-Level Implicit Scheme
18.5 Conclusion
References
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
19.7 Experimental Setup
19.8 Detection Probability and Dark Counts
19.9 Security Analysis of Our Algorithm
19.10 Conclusion
References
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
21 Pose Estimation Using Machine Learning and Feature Extraction
21.1 Introduction
21.2 Related Work
21.3 Proposed Work
21.4 Outcome and Discussion
21.5 Conclusion
References
22 E-Commerce Data Analytics Using Web Scraping
22.1 Introduction
22.2 Research Objective
22.3 Literature Review
22.4 Feasibility and Application
22.5 Proposed Methodology
22.6 Conclusion
References
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)
23.4 Main Results
23.5 Conclusion
References
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
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
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
26.4 Challenges and Processes of Supply Chain Transparency
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
27 Prediction of Seasonal Aliments Using Big Data: A Case Study
27.1 Introduction
27.2 Related Works
27.3 Conclusion
References
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
29 Application of Nanofluids in Heat Exchanger and its Computational Fluid Dynamics
29.1 Computational Fluid Dynamics
29.2 Nanofluids
29.3 Preparation of Nanofluids
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
