Mathematics and Computer Science, Volume 2 E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
MATHEMATICS AND COMPUTER SCIENCE This 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. This 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. 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.
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Seitenzahl: 533
Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
1 A Comprehensive Review on Text Classification and Text Mining Techniques Using Spam Dataset Detection
1.1 Introduction
1.2 Text Mining Techniques
1.3 Dataset and Preprocessing Steps
1.4 Feature Extraction
1.5 Supervised Machine Learning Classification
1.6 Evaluation
1.7 Experimentation and Discussion Results for Spam Detection Data
1.8 Text Mining Applications
1.9 Text Classification Support
1.10 Conclusions
References
2 Study of Lidar Signals of the Atmospheric Boundary Layer Using Statistical Technique
2.1 Introduction
2.2 Methodology
2.3 Mathematical Background of Method
2.4 Example and Result
2.5 Conclusion and Future Scope
Acknowledgement
References
Annexure
3 Optimal Personalized Therapies in Colon Cancer Induced Immune Response using a Fokker-Planck Framework
3.1 Introduction
3.2 The Control Framework Based on Fokker-Planck Equations
3.3 Theoretical Results
3.4 Numerical Schemes
3.5 Results
3.6 Conclusion
Acknowledgments
References
4 Detection and Classification of Leaf Blast Disease using Decision Tree Algorithm in Rice Crop
4.1 Introduction
4.2 Proposed Methodology
4.3 Result Analysis
4.4 Conclusion
4.5 Future Work
References
5 Novel Hybrid Optimal Deep Network and Optimization Approach for Human Face Emotion Recognition
5.1 Introduction
5.2 Related Work
5.3 System Model and Problem Statement
5.4 Proposed Model
5.5 Proposed HDC-GEN Classification
5.6 Result and Discussion
5.7 Conclusion
References
6 An Application of Information Technology in Adaptive Leadership of Ministry of Ayush During Pandemic of Covid 19: A Case Study
6.1 Introduction
6.2 Ministry of AYUSH
6.3 Leadership Principles and Practices by Ministry of AYUSH During Covid-19
6.4 Effective Communication
6.5 Sharing of Resources
6.6 Shared Decision Making
6.7 Training of Manpower
6.8 Use of IT Platform
6.9 Finding Opportunities for R&D During the Crisis
6.10 Collaborating with Stakeholders for International Day of Yoga (IDY)
6.11 Providing Hope When Nothing Seemed to be Working
6.12 Leveraging Old Knowledge
6.13 Conclusion
References
7 Encoder-Decoder Models for Protein Secondary Structure Prediction
7.1 Introduction
7.2 Literature Review
7.3 Experimental Work
7.4 Results and Discussion
7.5 Conclusion
References
8 Hesitancy, Awareness, and Vaccination: A Computational Analysis on Complex Networks
8.1 Introduction
8.2 Model Formulation
8.3 Model Analysis on Complex Network
8.4 Conclusions and Perspectives
References
9 Propagation of Seismic Waves in Porous Thermoelastic Semi-Infinite Space with Impedance Boundary Conditions
9.1 Introduction
9.2 Basic Equations
9.3 Problem Formulation
9.4 Reflection at the Free Surface
9.5 Numerical Results and Discussion
9.6 Conclusion
References
10 IoT Based Ensemble Predictive Techniques to Determine the Student Observing Analysis through E-Learning
10.1 Introduction
10.2 Review of Literature
10.3 Methodology
10.4 Analysis and Interpretation
10.5 Findings and Conclusion
References
11 Modelling and Analysis of a Congestion Dependent Queue with Bernoulli Scheduled Vacation Interruption and Client Impatience
11.1 Introduction
11.2 Model Overview
11.3 Model Analysis
11.4 Special Cases
11.5 Performance Metrics
11.6 Numerical Outcomes
11.7 Conclusion
References
12 Resource Allocation Determines Alternate Cell Fate in Bistable Genetic Switch
12.1 Introduction
12.2 Model Formulation
12.3 Result Section
12.4 Conclusion
Acknowledgement
References
13 A Hybrid Approach to Ontology Evaluation
13.1 Introduction
13.2 Background
13.3 The Developed OntoEva Method
13.4 Ontology Selection for Epilepsy Disorder
13.5 Results
13.6 Comparison of Ontologies
13.7 Conclusion
References
14 Smart Health Care Waste Segregation and Safe Disposal
14.1 Introduction
14.2 Related Works
14.3 System Architecture
14.4 Methodology
14.5 Mobile App
14.6 Conclusions and Future Works Declarations
Declarations
References
15 Investigation of Viscoelastic Magnetohydrodynamics (MHD) Flow Over an Expanded Lamina Surrounded in a Permeable Media
15.1 Introduction
15.2 Formulation of the Problem
15.3 Result and Argument
15.4 Conclusion
References
16 Quickest Multi-Commodity Contraflow with Non-Symmetric Traversal Times
16.1 Introduction
16.2 Preliminaries with Flow Models
16.3 QMCCF with Non-Symmetric Transit Times
16.4 Conclusions
Acknowledgments
References
17 A Mathematical Representation for Deteriorating Goods with a Trapezoidal-Type Demand, Shortages and Time Dependent Holding Cost
17.1 Introduction
17.2 Assumptions and Notations
17.3 Formulation and Solution
17.4 Numerical Example
17.5 Discussion
17.6 Inference
References
18 An Amended Moth Flame Optimization Algorithm Based on Fibonacci Search Approach for Solving Engineering Design Problems
18.1 Introduction
18.2 Classical MFO Algorithm
18.3 Proposed Method
18.4 Results and Discussions on IEEE CEC 2019 Benchmark Problems
18.5 Real-Life Applications
18.6 Conclusion with Future Studies
References
19 Image Segmentation of Neuronal Cell with Ensemble Unet Architecture
19.1 Introduction
19.2 Methods
19.3 Dataset
19.4 Implementation Details
19.5 Evaluation Metrics
19.6 Result
19.7 Conclusion
References
20 Automorphisms of Some Non-Abelian
p
−Groups of Order
p
4
20.1 Introduction
20.2 Categorization of
p-
Groups with Order
p
4
20.3 Number of Automorphisms of Some Non-Abelian Groups of Order
p
4
References
21 Viscoelastic Equation of p-Laplacian Hyperbolic Type with Logarithmic Source Term
21.1 Introduction
21.2 Preliminaries
21.3 Global Existence Result
21.4 Blow Up Results of the Solution for Equation (21.1)
References
22 Flow Dynamics in Continuous-Time with Average Arc Capacities 327
22.1 Introduction
22.2 Literature Review
22.3 Failure in Extension of AP to AAP
22.4 Formulation
22.5 Conclusion
Acknowledgment
References
23 Analysis of a Multiserver System of Queue-Dependent Channel Using Genetic Algorithm
23.1 Introduction
23.2 Description of the Model
23.3 Notations
23.4 Steady State Equations
23.5 Conclusions
References
24 An Approach to Ranking of Single Valued Neutrosophic Fuzzy Numbers Based on (
α
,
β
,
γ
) Cut Sets
24.1 Introduction
24.2 Definition and Representations
24.3 Proposed Method
24.4 Theorems
24.5 Numerical Examples
24.6 Conclusion
References
25 Performance Analysis of Database Models Based on Fuzzy and Vague Sets for Uncertain Query Processing
25.1 Introduction
25.2 Basic Definitions
25.3 Algorithm to Generate Membership Values
25.4 Real Life Applications
25.5 Conclusion
References
26 Estimating Error of Signals by Product Means (
C,
2) of the Fourier Series in a
W
(
L
r
,
ξ
(
t
))(
r
≥ 1) Class
26.1 Introduction
26.2 Known Result
26.3 Main Theorem
26.4 Some Auxiliary Results
26.5 Theorem’s Proof
26.6 Applications
26.7 Conclusion
Acknowledgement
References
About the Editors
Index
Also of Interest
End User License Agreement
Guide
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
About the Editors
Index
Also of Interest
End User License Agreement
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Advances in Data Engineering and Machine Learning
Series Editors: Niranjanamurthy M, PhD, Juanying XIE, PhD, and Ramiz Aliguliyev, PhD
Scope: Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise.
It is important to have business goals in line when working with data, especially for companies that handle large and complex datasets and databases. Data Engineering Contains DevOps, Data Science, and Machine Learning Engineering. DevOps (development and operations) is an enterprise software development phrase used to mean a type of agile relationship between development and IT operations. The goal of DevOps is to change and improve the relationship by advocating better communication and collaboration between these two business units. Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.
Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction. Machine learning engineering is the process of using software engineering principles, and analytical and data science knowledge, and combining both of those in order to take an ML model that’s created and making it available for use by the product or the consumers. “Advances in Data Engineering and Machine Learning Engineering” will reach a wide audience including data scientists, engineers, industry, researchers and students working in the field of Data Engineering and Machine Learning Engineering.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Mathematics and Computer Science Volume 2
Edited by
Sharmistha Ghosh
M. Niranjanamurthy
Krishanu Deyasi
Biswadip Basu Mallik
and
Santanu Das
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-89632-6
Front cover images supplied by Wikimedia CommonsCover design by Russell Richardson
Preface
The mathematical sciences are part of nearly all aspects of everyday life. The discipline has underpinned such beneficial modern capabilities as internet searching, medical imaging, computer animation, weather prediction, and all types of digital communications. Mathematics is an essential component of computer science. Without it, you would find it challenging to make sense of abstract language, algorithms, data structures, or differential equations, all of which are necessary to fully appreciate how computers work. In a sense, computer science is just another field of mathematics. It does incorporate various other fields of mathematics, but then focuses those other fields on their use in computer science. Mathematics matters for computer science because it teaches readers how to use abstract language, work with algorithms, self-analyze their computational thinking, and accurately model real-world solutions. Algebra is used in computer programming to develop algorithms and software for working with math functions. It is also involved in design programs for numerical programs. Statistics is a field of math that deploys quantified models, representations, and synopses to conclude from data sets.
This book focuses on mathematics, computer science, and where the two intersect, including heir concepts and applications. It also represents how to apply mathematical models in various areas with case studies. The contents include 29 peer-reviewed papers, selected by the editorial team.
