Mathematics and Computer Science for Real-World Applications, Volume 4 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 764

Veröffentlichungsjahr: 2025

Beschreibung

Mathematics and Computer Science for Real-World Applications gives invaluable insights into how mathematical and computer sciences drive essential modern innovations that enhance everyday life, making it a must-read for anyone interested in the intersection of mathematics and technology and their real-world applications.

Mathematical sciences are part of nearly all aspects of everyday life. The discipline has underpinned beneficial modern capabilities, including internet searches, medical imaging, computer animation, numerical weather predictions, and digital communication. Mathematics and computer science are constantly evolving and contributing to most areas of science and engineering, therefore, future generations of mathematical scientists should reassess the increasingly cross-disciplinary nature of the mathematical sciences.

Mathematics and Computer Science for Real-World Applications presents current scientific and technological innovations from leading academics, researchers, and experts across the globe in mathematical sciences and computing. The volume will discuss new technical ideas and features that can be incorporated into day-to-day life for the benefit of society. A diversified spectrum of scientific advancements is discussed, including applications of differential and integral equations, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning, and artificial intelligence. Readers will explore diverse ideas and innovations in the field of computing and its growing connections to various fields of mathematics.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Analysis of Medical Image Using a Multimodal Approach for Precise Cancer Detection

1.1 Introduction

1.2 Methodology

1.3 Result

1.4 Conclusion

References

2 A Review on Contemporary Advancements in Conversational AI Within the Cloud Platform

2.1 Introduction

2.2 Conversational Features of Human Automatic Speech Recognition System

2.3 Advantages of Artificial Intelligence-Based Conversation

2.4 Components of Artificial Intelligence-Based Conversation

2.5 How Conversational AI Actually Functions

2.6 Achieving Conversational Artificial Intelligence

2.7 Incorporation of Conversational Artificial Intelligence

2.8 What is Conversation Design, and How Can it be Used in Conjunction with Conversational AI?

2.9 Conversational AI and Chatbot Differences

2.10 Difficulties in Conversational AI

2.11 Advanced Conversational Artificial Intelligence

2.12 Understanding the Role of AI and ML in the Future of Computing

2.13 Purpose of the Study

2.14 Objectives of this Study

2.15 Reasons Why Artificial Intelligence and Machine Learning are Essential for Future Computing

2.16 A Prospective Model for Future Computing Systems

2.17 Model Within its Own Autonomous Controls

2.18 Next-Generation Computing with Explainable AI (XAI)

2.19 Possible Dangers Inherent in AI-Based Integrated Computing

2.20 Conclusion

References

3 General Arrival Working Vacations Queue with Heterogeneous Servers Operating Under Triadic Policy

3.1 Introduction

3.2 Outline of the Model

3.3 Model Analysis

3.4 Performance Characteristics

3.5 Result Analysis

3.6 Conclusion

References

4 A New TOPSIS Approach Utilizing Triangular Divergence for Decision-Making in Single-Valued Neutrosophic Environment

4.1 Introduction

4.2 Preliminaries

4.3 A New Distance Measure for SVNSs

4.4 A New TOPSIS Method Using Triangular Divergence-Based Distance Measure

4.5 Illustrative Example

4.6 Conclusion

References

5 Micro Vague Generalized Semi Connectedness in Micro Vague Topological Spaces

5.1 Introduction

5.2 Preliminaries

5.3 Micro Vague Generalized Semi Connected Space

5.4 q-Coincident

5.5 Conclusion

References

6 Image Retrieval Using Gaussian Mixture Model Based on Breast Cancer

6.1 Introduction

6.2 Problem Statement and Approach of the Model

6.3 Aims and Objectives

6.4 Significance

6.5 Research Methodology

6.6 Requirement Resources

6.7 Research Plan

6.8 Results and Discussions

6.9 Conclusion

References

7 Diophantine Equation of Degree Two Having Four Unknowns

7.1 Introduction

7.2 Method of Analysis

7.3 Conclusion

References

8 Uniqueness Results to the Nonlinear Boundary Value Problems of Fourth Order

8.1 Introduction

8.2 Primary Outcomes

8.3 Main Outcomes Based on Metrics

8.4 Examples

References

9 Bivariate Cointegrated Model with Gamma Innovations

9.1 Introduction

9.2 Cointegration and Error Correction Model

9.3 Model Specification and Parameter Estimation

9.4 Simulation

9.5 Conclusions and Perspectives

References

10 Accelerated Reliability Sampling Plan Based on Transformed Lindley Distribution

10.1 Introduction

10.2 Lindley Distribution

10.3 Methodology

10.4 Example

10.5 Conclusion

References

11 A Synopsis of Fuzzy Set Theory

11.1 History of Fuzzy Set

11.2 Basic Definitions

11.3 Fuzzy Measures of Information

11.4 Operations on Fuzzy Sets

11.5 Conclusion

References

12 Efficient Classification of Breast Cancer Diseases on Medical Images Using Deep Learning Methodology

12.1 Introduction

12.2 Literature Review

12.3 Proposed Methodologies

12.4 Results and Analysis

12.5 Conclusion

References

13 Low Power VLSI Architecture for 48-Bit Multiplication Using Elliptic Curve Algorithm

13.1 Introduction

13.2 Related Works

13.3 DADDA Multiplier

13.4 Elliptic Curve Multiplier

13.5 Results and Discussion

13.6 Conclusion

References

14 Enhancing Cloud Computing Security Through Decimal Bond DNA Cryptography (DBDNA): A Novel Approach

14.1 Introduction

14.2 Literature Survey

14.3 DNA and Cloud Computing

14.4 Proposed DNA Cryptography in Cloud Computing Security

14.5 Result Analysis

14.6 Conclusion

References

15 Fake News Detection in Healthcare Using Machine Learning

15.1 Introduction

15.2 Related Works

15.3 Proposed Approach and Models

15.4 Fake News Detection Models

15.5 Results

15.6 Conclusion

References

16 Insights into MHD Flow of Casson Fluid Over an Exponentially Permeable Stretching Surface Using Homotopy Analysis Method

Nomenclature

16.1 Introduction

16.2 Mathematical Formulation

16.3 HAM

16.4 Convergence of HAM

16.5 Results and Discussion

16.6 Conclusions

References

17 Random Forest: One of the Best-Fitted ML Algorithms in Liver Disease Prediction

17.1 Introduction

17.2 Opportunities of this Study

17.3 Available ML Algorithms for Liver Disease Prediction

17.4 Recommended Structure

17.5 Materials and Methodology

17.6 Preparation of Data

17.7 Discussion and Analysis

17.8 Conclusion

17.9 Future Work

References

18 A Next-Gen Blood Donation Coordination System Empowered by MERN Stack and Machine Learning-Driven Dynamic Clustering for Intelligent Donor Identification

18.1 Introduction

18.2 Methodology

18.3 Result and Discussion

18.4 Conclusion

References

19 Artificial Intelligence in Detection and Classification of Lung Cancer - An Overview

19.1 Introduction

19.2 Evolution of Artificial Intelligence in the Detection and Classification of Lung Cancer

19.3 Related Works

19.4 Problem Statement

19.5 Methodology

19.6 Results

19.7 Future Directions

19.8 Conclusion

Acknowledgments

References

20 Applications of Image Processing for Surface Irregularities Detection and Comparison with Nondestructive Testing Results

20.1 Introduction

20.2 NDT for the Detection of Surface Irregularities

20.3 AI Tools for Detection of Surface Irregularities

20.4 Materials and Methods

20.5 Results and Discussion

20.6 Conclusions

Acknowledgments

References

21 Detection of Fraud Review Through Object Recognition for Fake Picture Component Using Machine Learning Approach

21.1 Introduction

21.2 Methodology

21.3 Result and Discussion

21.4 Conclusion

References

22 Gas Leakage Surveillance System Leveraging Using Spartan 7 FPGA and GSM Technology

22.1 Introduction

22.2 System Analysis

22.3 Leak Detection and Call Activation Employing FPGA

22.4 Evaluation of Findings in a Research Setting

22.5 Conclusion

References

23 Realization of Health Intelligence in Industry 5.0 – A Paper on Sustainable Use of AI and Human Intelligence in Healthcare Industry

23.1 Introduction

23.2 Literature Review

23.3 Conclusion

References

24 Preference Analysis Can Be a Guide to an Inamorata to Select Her Swain

24.1 Introduction

24.2 Review

24.3 Objectives

24.4 Preliminaries

24.5 Data and Methodology

24.6 Results

24.7 Analysis

24.8 Conclusions

References

25 A Comparative Study on Various Types of Algorithms of Artificial Neural Network for Solar Still Study: A Review

25.1 Introduction

25.2 Artificial Neural Network (ANNs)

25.3 Learning Algorithm for Artificial Neural Networks

25.4 Performance Evaluation Criteria

25.5 Result and Discussions

25.6 Conclusions

References

26 Arduino-Based Detector of Alcohol-Impaired Drivers to Auto-Lock the Engine for Road Safety Applications

26.1 Introduction

26.2 Design Procedures

26.3 Hardware Components

26.4 Software Requirements

26.5 Block Diagram

26.6 Flowchart

26.7 Results and Discussion

26.8 Conclusions

References

27 Thematic Analysis for Text Review Detection Using Machine Learning

27.1 Introduction

27.2 Literature Review

27.3 Methodology

27.4 Results and Discussion

27.5 Conclusion

References

28 Artificial Intelligence Enabled Non-Destructive Testing and Engineering

28.1 Introduction

28.2 Traditional NDT Methods Used for Detection of Flaws

28.3 AI Integrated Advanced NDT Methods

28.4 Conclusion

References

29 Increasing Crop Productivity with Machine Learning Models

29.1 Introduction

29.2 Techniques Used to Raise Soil Temperature

29.3 Methodology

29.4 Results

References

30 Wavelets and Their Recent Applications

30.1 Introduction

30.2 Wavelets and Hilbert Spaces

30.3 Desirable Features

30.4 Families of Wavelets

30.5 Mathematical Properties of Wavelets

30.6 Gabor Systems

30.7 Applications

30.8 Conclusion

References

31 Detection and Forecasting of Dengue Fever Using Data Mining Techniques

31.1 Introduction

31.2 Review of Literature

31.3 Methodology

31.4 Data Sets

31.5 Results

31.6 Discussions of Results

31.7 Conclusion

31.8 Future Scope

References

32 Image Classification Using CNN for the Detection of Cancer Cells to Avoid Metastasis

32.1 Introduction

32.2 Literature Review

32.3 Methodology

32.4 Results and Discussion

32.5 Conclusion

References

33 Revolutionizing Industries: Addressing Challenges and Innovations from Industry 4.0 to Industry 5.0

33.1 Introduction

33.2 Literature Reviews

33.3 Research Objectives

33.4 Research Methodology

33.5 Challenges of Industry 4.0

33.6 Industry 5.0

33.7 Industry 5.0 to Overcome the Challenges of Industry 4.0

33.8 Results and Discussions

33.9 Conclusion

References

34 Portfolio Optimization Using Genetic Algorithm

34.1 Introduction

34.2 Materials and Methods

34.3 Results and Discussion

34.4 Conclusion

Bibliography

About the Editors

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1

Model accuracy and efficiency.

Chapter 2

Table 2.1

Difference between traditional chatbots and AI-powered chatbots [19]...

Chapter 4

Table 4.1

Comparison of distance measures for SVNNs.

Table 4.2

Decision matrix.

Table 4.3

Normalized decision matrix.

Table 4.4

Weighted decision matrix.

Table 4.5

PDM, NDM, and final ranking.

Table 4.6

Comparison of different methods.

Chapter 6

Table 6.1

Analysis of proposed model.

Chapter 7

Table 7.1

Simultaneous equations.

Chapter 9

Table 9.1

Johansen trace test (up) and eigen value test (down).

Table 9.2

Numerical estimation of the parameters of the corrected model.

Table 9.3

The average estimates of the corrected model parameters when a_1t ~ ...

Table 9.4

The average estimates of the corrected model parameters when a_1t ~ ...

Chapter 10

Table 10.1

Sampling plan table (

α

= 0.1 and

β

= 0.1).

Table 10.2

Sampling plan table (

α

= 0.2 and

β

= 0.2).

Table 10.3

Results of sensitivity analysis.

Chapter 12

Table 12.2

Comparison of several classifier accuracies.

Table 12.3

Resnet-50 accuracy score.

Table 12.4

VGG-16 classifier accuracy score.

Chapter 13

Table 13.1

Output comparison table of power and delay.

Chapter 14

Table 14.1

List of aspects with DNA features in cloud security.

Table 14.2

Padded value table mapping with DNA value.

Table 14.3

Binary value table mapping with DNA value.

Table 14.4

Bond table.

Table 14.5

DNA encoding library.

Table 14.6

Result analysis.

Chapter 15

Table 15.1

Comparison of accuracy.

Table 15.2

Comparison of recall.

Table 15.3

Comparison of precision.

Table 15.4

Comparison of F1-score [28].

Chapter 16

Table 16.1

Different orders of approximations and the convergence of the HAM s...

Table 16.2

Comparison of when

S

r

=

D

f

= 0, Pr = 0.71,

Sc

= 0.6,

S

= β = 0.5,

Chapter 17

Table 17.1

Measure the performance of the model statistical results.

Chapter 18

Table 18.1

List of blood donation education components.

Chapter 19

Table 19.1

Comparative analysis of SVM, random forest, KNN, and decision trees...

Table 19.2

Interpretation of accuracy of different AI/ML techniques.

Table 19.3

Comparative analysis of support vector machines (SVM), random fores...

Table 19.4

Machine learning model used in prediction of lung cancer.

Chapter 20

Table 20.1

Chemical analysis of carbon steel used as raw materials.

Table 20.2

Specifications of ultrasonic testing machine.

Table 20.3

Observation table for compression testing of PCC of grade M15.

Table 20.4

Steps for image processing.

Chapter 24

Table 24.1

Possible combinations.

Table 24.2

For attribute age difference (older in years).

Table 24.3

For attribute income (in dollar).

Table 24.4

For attribute personality.

Table 24.5

Efficient coding.

Table 24.6

Variables entered

a

/removed

b

.

Table 24.7

Model summary.

Table 24.8

ANOVA

b

.

Table 24.9

Coefficients

a

.

Table 24.10

Calculations of part utilities for the attribute ‘Age Difference (...

Table 24.11

Calculations of part utilities for the attribute ‘Income (in dolla...

Table 24.12

Calculations of part utilities for the attribute ‘Personality’.

Table 24.13

Levels with highest part utilities.

Chapter 25

Table 25.1

Different types of ANN and their descriptions.

Table 25.2

Coefficient of Determinant (R

2

): analysis for training, validation,...

Chapter 31

Table 31.1

Classification report on the basis of precision.

Table 31.2

Classification report on the basis of recall.

Table 31.3

Classification report on the basis of f1 score.

Table 31.4

Classification report on the basis of support.

Table 31.5

Statistical report.

Table 31.6

Classification report on the basis with ensemble of precision.

Table 31.7

Classification report on the basis with ensemble of recall.

Table 31.8

Classification report on the basis with ensemble of f1 score.

Table 31.9

Classification report on the basis with ensemble of support.

Table 31.10

Statistical report with ensemble.

Table 31.11

Comparative study of published works with data obtained in this st...

Table 31.12

Fuzzy responses for first stage to know increase of patients in We...

Table 31.13

Distances of each and every expert’s response from the average a

M

=...

Table 31.14

Fuzzy responses for second stage to know increase of number of pat...

Table 31.15

Distances of each and every expert’s responses from the average.

Chapter 34

Table 34.1

The five companies with their optimal stock weights.

List of Illustrations

Chapter 1

Figure 1.1 The framework of the proposed medical image detection.

Figure 1.2 Resizing and labeling the raw image.

Figure 1.3 Changing color (grayscale conversion) of the raw image.

Figure 1.4 Bayesian Information Criterion (BIC) values.

Figure 1.5 Confusion matrix of actual dataset.

Figure 1.6 Histogram of our datasets.

Figure 1.7 Final output of raw dataset.

Chapter 3

Figure 3.1 Effect of

μ

1

on

el

and

lr

.

Figure 3.2 Influence of

α

on

lr

.

Figure 3.3 Change in ws with

Q

for different

N

.

Figure 3.4 Impact of

η

on performance characteristics.

Chapter 4

Figure 4.1 MADM process using the N-TDDM based SVN-TOPSIS method.

Figure 4.2 Comparison of closeness coefficients of alternatives for different ...

Chapter 6

Figure 6.1 Flow chart of the model.

Chapter 9

Figure 9.1 Time series plot of real estate owned in US and consumer credit ava...

Figure 9.2 Auto-correlation function plot of real estate owned in US and consu...

Chapter 10

Figure 10.1 PDF of Lindley distribution for different gamma values.

Figure 10.2 Hazard rate of Lindley distribution.

Chapter 12

Figure 12.1 The proposed work.

Figure 12.2 Data set benign and malignant.

Figure 12.3 Train data augmentation.

Figure 12.4 Test data augmentation.

Figure 12.5 Accuracy graph for training and validation of CNN model.

Figure 12.6 Loss graph for training and validation of CNN model.

Figure 12.7 Accuracy plot of VGG-16 based model.

Figure 12.8 Loss graph for training and validation of VGG-16 model.

Figure 12.9 Accuracy graph for training and validation of ResNet 50 model.

Figure 12.10 Loss graph for training and validation of ResNet 50 model.

Figure 12.11 Confusion matrix using CNN model and confusion matrix (percentage...

Figure 12.12 Confusion matrix using VGG-16 model and confusion matrix (percent...

Chapter 13

Figure 13.1 Dadda multiplier—4 × 4 bits.

Figure 13.2 Elliptic-curve multiplier—architecture.

Figure 13.3 Improved flow diagram for FP multiplication using ECC.

Figure 13.4 2s complement of floating point multiplication.

Figure 13.5 Simulation output of FP multiplier using ECC.

Figure 13.6 Simulation output of FP multiplier using Dadda Multiplier.

Figure 13.7 Power analysis.

Chapter 14

Figure 14.1 Architecture of cloud server security using DNA cryptography.

Chapter 15

Figure 15.1 Flowchart of proposed methodology [9].

Figure 15.2 Model accuracy comparison plot.

Figure 15.3 Model recall comparison plot.

Figure 15.4 Model precision comparison plot.

Figure 15.5 Model F1-Score comparison plot.

Chapter 16

Figure 16.1 Flow diagram of Casson stretched surface problem.

Figure 16.2

ħ

-curves for

f

”(0),

θ

’(0) and

ϕ

’(0) at 15th o...

Figure 16.3 Outlines of velocity for manifold

β

.

Figure 16.4 Outlines of temperature for manifold

β

.

Figure 16.5 Outlines of concentration for manifold

β

.

Figure 16.6 Outlines of velocity for manifold

M

.

Figure 16.7 Outlines of temperature for manifold

M

.

Figure 16.8 Outlines of concentration for manifold

M

.

Figure 16.9 Outlines of velocity for manifold

N

.

Figure 16.10 Outlines of temperature for manifold

N

.

Figure 16.11 Outlines of concentration for manifold

N

.

Figure 16.12 Outlines of velocity for manifold

K

.

Figure 16.13 Outlines of velocity for manifold

Gr.

Figure 16.14 Outlines of velocity for manifold

Gc

.

Figure 16.15 Outlines of temperature for manifold

R.

Figure 16.16 Outlines of velocity for manifold

R

a

.

Figure 16.17 Outlines of temperature for manifold

R

a

.

Figure 16.18 Outlines of velocity for manifold

S

r

.

Figure 16.19 Outlines of temperature for manifold

S

r

.

Figure 16.20 Outlines of concentration for manifold

S

r

.

Figure 16.21 Outlines of velocity for manifold

D

f

.

Figure 16.22 Outlines of temperature for manifold

D

f

.

Figure 16.23 Outlines of concentration for manifold

D

f

.

Figure 16.24 Outlines of concentration for manifold

γ.

Figure 16.25 Skin friction coefficient for manifold

D

f

and

M

.

Figure 16.26 Heat transfer coefficient for manifold

D

f

and

M

.

Figure 16.27 Mass transfer coefficient for manifold

D

f

and

M

.

Chapter 17

Figure 17.1 Random forest [39].

Figure 17.2 Decision tree [39].

Figure 17.3 K-nearest neighbors (KNNs) [35].

Figure 17.4 Naïve Bayes [32].

Figure 17.5 Recommended structure

Figure 17.6 Materials and methodology.

Figure 17.7 The number of patients in the dataset.

Figure 17.8 Accuracy based prediction of available algorithms.

Figure 17.9 Measure the performance of the models introduced on the LASSO func...

Figure 17.10 ROC curve [38].

Chapter 18

Figure 18.1 Proposed model diagram.

Figure 18.2 Flow of K-means clustering algorithm.

Figure 18.3 The result of K-mean clustering.

Figure 18.4 Graph representing clustering evaluation metrices.

Figure 18.5 Home page of the system.

Figure 18.6 Registration page of the system.

Figure 18.7 Login page of the system.

Figure 18.8 MongoDB database of the system.

Figure 18.9 Performing a search on the system.

Chapter 19

Figure 19.1 Architecture of the model of lung cancer classification and detect...

Figure 19.2 Early-stage lung cancer of a 54-year-old woman.

Chapter 20

Figure 20.1 Schematic of surface crack detection.

Figure 20.2 Schematic of surface crack detection at the site for tall civil st...

Figure 20.3 Raw materials used for welding (IS: 2062, Grade E250).

Figure 20.4 Mechanical cleaning of raw materials.

Figure 20.5 Edge preparation of the raw materials to be welded.

Figure 20.6 Soundness checking of the samples to be welded using ultrasonic te...

Figure 20.7 MIG welding machine used in welding operation model: POWERMIGT400,...

Figure 20.8 Radiographic film inspection of welded samples.

Figure 20.9 Mixing of plain cement concrete (PCC) of grade M15.

Figure 20.10 Tested sample of 150-mm PCC cube of grade M15.

Figure 20.11 Compression test of PCC of grade M15.

Figure 20.12 Cracked sample after compression test.

Figure 20.13 Results derived from (a) visual testing and (b) radiography testi...

Figure 20.14 Results achieved from image processing of (a) image and (b) radio...

Figure 20.15 Python code being run for image processing of welding cracks.

Figure 20.16 Result of image processing of cracked samples of PCC cubes of gra...

Figure 20.17 Image under consideration for image processing.

Figure 20.18 Hough transformation image of the crack.

Figure 20.19 Detected crack image for pixel calculation.

Chapter 21

Figure 21.1 The framework of recognition for fake picture component.

Figure 21.2 Machine train and learn to detect real image.

Figure 21.3 Machine train and learn to detect fake image.

Figure 21.4 Show the gray scale of an image.

Figure 21.5 Bayesian information criterion (BIC) values.

Figure 21.6 Bayesian information criterion (BIC) values.

Figure 21.7 Prediction matrix.

Chapter 22

Figure 22.1 Illustration of the equipment for sensing gas pills.

Figure 22.2 The circuit based on signal filtering [3].

Figure 22.3 The findings of XILINX ISE14.7 simulation of the broadcast UART fe...

Figure 22.4 Call origination and leakage analysis in FPGA.

Figure 22.5 The gases cape detecting mechanism is configured experimentally.

Figure 22.6 Observations of gas volume.

Chapter 23

Figure 23.1 (Surgical robotics market value surge at 25.7% CAGR, 2032) [5].

Figure 23.2 AI and healthcare market (artificial intelligence (AI) in healthca...

Chapter 24

Figure 24.1 General procedure of conjoint analysis.

Figure 24.2 Vector model.

Figure 24.3 Ideal point model.

Figure 24.4 Path-worth function model.

Figure 24.5 Tree diagram of possible combinations.

Figure 24.6 Graph of levels and highest PU.

Chapter 25

Figure 25.1 Model of basic ANNs.

Figure 25.2 Schematic of ANN architecture.

Figure 25.3 The change of Solar radiation with time (hr).

Figure 25.4 Average high ambient temperature in every month of 2006.

Figure 25.5 Variation of ambient temperature, water temperature and wind speed...

Chapter 26

Figure 26.1 Schematic block representation of the proposed mechanism of alcoho...

Figure 26.2 Main hardware components.

Figure 26.3 Flowchart of the proposed design work.

Figure 26.4 Prototype lab testing of the proposed system.

Figure 26.5 Measurements of the ppm and percentage of alcohol against measured...

Chapter 27

Figure 27.1 Block diagram on thematic analysis.

Figure 27.2 Flow chart.

Figure 27.3 Convert the word to numerical number.

Figure 27.4 Word cloud.

Figure 27.5 Confusion matrix of actual dataset.

Chapter 29

Figure 29.1 The frost present on wheat crop.

Figure 29.2 Damage caused to banana leaves and fruits due to cold and frost.

Figure 29.3 Wall of water plant protectors.

Figure 29.4 Plastic mulch in the field to raise soil temperature.

Figure 29.5 C/N ratio of different organic materials.

Figure 29.6 Temperature variation during composting.

Figure 29.7 The process of adding compost and proposing final model.

Figure 29.8 Workflow of the model.

Figure 29.9 Random forest model.

Figure 29.10 Decision tree.

Figure 29.11 Climate variations month-wise.

Figure 29.12 Temperature variation month-wise.

Chapter 30

Figure 30.1 (a) Haar wavelet. (b) Mexican Hat wavelet.

Figure 30.2 Daubechies wavelets.

Figure 30.3 Coiflet wavelets.

Figure 30.4 Symlet wavelets.

Figure 30.5 (a) Meyer wavelet. (b) Morlet wavelet.

Figure 30.6 Gaussian wavelets.

Chapter 31

Figure 31.1 ROC curve ensemble for dengue (curve 1).

Chapter 32

Figure 32.1 Flow chart of the model.

Figure 32.2 Visualization of input image.

Figure 32.3 Visualization of K-means output.

Figure 32.4 Cluster plot of training and testing data.

Figure 32.5 Representation of histogram of pixel distribution.

Figure 32.6 Final output.

Chapter 33

Figure 33.1 Industry evolution in phase of industrialization.

Figure 33.2 Comparison between Industry 4.0 and Industry 5.0.

Figure 33.3 Advancement of Industry 5.0 technology.

Chapter 34

Figure 34.1 Investor’s perception map.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

About The Editors

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Mathematics and Coumputer Science

Series Editor: M. Niranjanamurthy and Biswadip Basu Mallik

Scope: Mathematics provides concepts vital to many areas of computer science, including graphics, image processing, cryptography, machine learning, computer vision, optimization, graph algorithms, quantum computation, computational biology, information retrieval and web search. The scope of this series is to cover all of the aspects of mathematical concepts with computer science applications.

This book series focuses on recent advances in the field of theoretical computer science as well as its blending with mathematical techniques. Series books also aim to disseminate new technical ideas and features that can be incorporated in day-to-day life for the mathematician, engineer, scientist, or other industry professional.

The books aspire to exhibit scientific advancements in a diversified spectrum that may include differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory & optimization, control theory, machine learning & artificial intelligence, big data analytics, Internet of Things (IoT), cryptography, fuzzy automata, statistics and many more. The mathematical sciences are part of nearly all aspects of everyday life, and the discipline has underpinned such beneficial modern capabilities as internet searching, medical imaging, computer animation, artificial intelligence (AI), machine learning (ML), numerical weather predictions, and all types of digital communications.

Readers shall get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. The volume will serve as a valuable reference resource for researchers in academia and industry.

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Mathematics and Computer Science for Real-World Applications

Edited by

Biswadip Basu Mallik

M. Niranjanamurthy

Sharmistha Ghosh

Krishanu Deyasi

and

Santanu Das

This edition first published 2025 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© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Cover image: Generated with AI using Adobe FireflyCover design by Russell Richardson

Preface

The fast convergence of computer science and mathematics has sparked revolutionary developments in a wide range of practical applications. Healthcare diagnostics, intelligent infrastructure, data redundancy solutions, and complicated decision-making frameworks are only a few examples of the problems that have been solved possible because to the combination of these fields. Leading academics and professionals in various domains have contributed to this edited edition, “Mathematics and Computing Science for Real-World Applications,” which features a carefully chosen collection of their most recent research and development works.

This volume’s scope and depth highlight the dynamic interaction between theoretical underpinnings and real-world applications. A thorough analysis of “Contemporary Advancements in Conversational AI within the Cloud Platform” by Biswadip Basu Mallik, Panem Charanarur, and Srinivasa Rao Gundu delves into the subtleties of fusing cloud technologies with AI-driven conversational systems, with a focus on ethical issues and potential future developments.

In “General Arrival Working Vacations Queue with Heterogeneous Servers Operating under Triadic Policy,” K. Jyothsna, P. Vijaya Kumar, and P. Vijaya Laxmi explore operational efficiencies in service systems with a strong examination of queuing models. A further example of the application of sophisticated mathematical models in real-world situations is the creative decision-making strategy that makes use of triangular divergence in “A New TOPSIS Approach Utilizing Triangular Divergence for Decision-Making in Single-Valued Neutrosophic Environment” by Prayosi Chatterjee and Mijanur Rahaman Seikh.

Drs. R. Sathiyapriya and M.A. Gopalan’s in-depth analysis of Diophantine equations and Vargees Vahini T and Trinita Pricilla M’s topological space explorations help to close the gap between abstract mathematical theories and their concrete applications. Furthermore, chapters like “Bivariate Cointegrated Model with Gamma Innovations” by Amritha Jayaram and Nimitha John and “Accelerated Reliability Sampling Plan Based on Transformed Lindley Distribution” by Rini Raju and Jiju Gillariose highlight the critical role that statistical models play in econometric analysis and reliability.

This volume includes innovative research in the fields of artificial intelligence and machine learning, including “Artificial Intelligence in Detection and Classification of Lung Cancer - An Overview” by Sanjukta Chakraborty and Dilip Kumar Banerjee and “Efficient Classification of Breast Cancer Diseases on Medical Images Using Deep Learning Methodology” by Pramit Brata Chanda, Subhadip Das, and Subir Kumar Sarkar. These chapters demonstrate how AI is revolutionizing patient care and medical diagnostics.

Additional contributions showcase inventive uses of AI and computer science. For example, Animesh Kairi and Tapas Bhadra have improved cloud computing security using DNA cryptography, and Siddhant Shaw and associates have developed intelligent systems for coordinating blood donations. As noted by Satyabrata Podder, Arka Dasgupta, and Biswajit Bhattacharyya, the use of AI in non-destructive testing methods represents a step forward in industrial quality control and safety.

The wide variety of researches that make up this book is evidence of the many uses that computer science and mathematics have. Every chapter provides future directions for research and application as well as practical insights and increases academic knowledge. We encourage you to investigate the complex techniques, creative fixes, and significant influence these interdisciplinary approaches have on solving real-world problems as you go through the chapters.

The editors express their gratitude to God Almighty for giving them the chance to complete this book. We are grateful to Scrivener Publishing U.S.A. for their support and for giving us the opportunity to edit this book. We also thank the reviewers for their thorough assessments and all of the contributing writers for their essential research and dedication. This book is the result of cooperative efforts to promote creativity and quality in the fields of computer science and mathematics.

Biswadip Basu MallikM. NiranjanamurthySharmistha GhoshKrishanu DeyasiSantanu Das

1Analysis of Medical Image Using a Multimodal Approach for Precise Cancer Detection

Soumen Santra1*, Mouparna De1, Dipankar Majumdar2 and Surajit Mandal3

1Department of MCA, Techno International New Town, Kolkata, West Bengal, India

2Department of CSE, RCC Institute of Information Technology, Kolkata, India

3Department of ECE, B.P. Poddar Institute of Management & Technology, Kolkata, India

Abstract

Cancer is one of the most serious medical conditions is cancer, and patients’ conditions worsen every day. The early identification of cancer is essential for successful treatment and better patient outcomes. Fast-acting image processing technology should be used for medical diagnosis. To achieve this, we provide a unique method for cancer identification that uses the Gaussian Mixture Model (GMM) algorithm in combination with a machine learning technique that aids in correct analysis. This method involves resizing and altering the color of the image to determine the precise location of cancer. This method makes the operation considerably simpler because it provides a better understanding of the cancer picture. This method’s main goal is to create a strong and trustworthy cancer detection system that will aid medical professionals in the early identification of cancer and has the potential to save many lives. This technique can act as a bridge between conventional diagnostic methods and cutting-edge technologies.

Keywords: Resizing, image preprocessing, gaussian mixture model (GMM) algorithm, conventional diagnostic methods, cutting-edge technology

1.1 Introduction

Cancer, a complex and common disease, poses a major challenge to public health worldwide. Cancer has become a serious global health problem because of its high mortality rate and insidious nature. Early and accurate cancer detection is key to improving patient outcomes and survival rates. Although effective, traditional cancer detection techniques, such as biopsy and medical imaging, are often invasive, expensive, and open to human interpretation, which can result in inconsistent diagnoses. Machine learning is a branch of artificial intelligence, has emerged as an effective strategy to improve the efficiency and accuracy of cancer detection in response to these difficulties [1]. Cancer, with its multifaceted etiology and complex pathway physiology, remains a formidable opponent. Its diversity among different types, subtypes, and individual cases requires precision and sophistication in diagnosis [2]. Traditional methods often fail to provide the precision and efficiency required for modern treatment [2, 3]. Cancer is characterized by uncontrolled proliferation of abnormal cells and their ability to invade neighboring tissues.

After viewing images from an X-ray, magnetic resonance imaging (MRI), or computed tomography (CT) scan, medical professionals present their ideas and make decisions [4]. These images are essential for identifying and describing anomalies and tumors, which makes them crucial for cancer surveillance and diagnosis. Several image-processing methods can be applied to this model to extract the characteristics of the impacted area of an image, which provides insight into their nature [5].

Digitally altered images are referred to as raw images. Raw photos are subjected to a variety of image-processing algorithms to identify their attributes [6]. In this study, we explored the incorporation of Gaussian mixture models (GMM) as a pioneering tool in cancer image analysis as per Figure 1.1. The GMM algorithm is a form of unsupervised learning that can be used to model complex data distributions in medical images. Deep learning, a branch of artificial intelligence, has emerged as a potent technique for feature extraction and identification of patterns, structures, and anomalies in pictures, complementing GMM [7, 8]. Machine learning integration enabled our diagnostic methodology to adapt, change, and improve over time. By integrating GMM, we aim to provide a more accurate and precise approach for classifying and characterizing cancer regions in these images. Typically, a medical image is of excellent quality.

Cancer detection through the utilization of cutting-edge technologies, such as image recognition and machine learning, can be challenging as it requires a huge, labeled training dataset, and there are several sites present on the Internet where a dataset can be gathered. This model collected different cancer datasets from Kaggle.com (https://data.mendeley.com/datasets/p2r42nm2ty/1).

Figure 1.1 The framework of the proposed medical image detection.

The use of radiological imaging, genetic information, electronic medical records, and patient demographics contribute to the development of a full understanding of the illness. With the use of machine learning models and the unification of these disparate data sources, we hope to obtain insightful knowledge that will enable earlier and more precise cancer diagnoses [9].

1.2 Methodology

1.2.1 Understanding the Objective and Scope

Defining the goal of cancer image analysis, whether it is related to disease detection or medical image classification

[10]

.

Identification of a dataset of medical images for analysis.

1.2.2 Data Collection and Importation

A dataset of cancer images relevant to our research and analysis was collected to ensure that the images were in a consistent format, such as JPG, JPEG, or PNG

[11]

.

Utilizing the ‘glob’ library to efficiently import medical (cancer) images from a specified directory, making it easier to process multiple images simultaneously.

1.2.3 Image Preprocessing

Importing Libraries: Import the necessary libraries, including NumPy, cv2, and sklearn. mixture and glob.

Load each cancer image using OpenCV to create image objects.

Applying preprocessing techniques to standardize the images for analysis. The common preprocessing steps that we have included are

[12]

:

Resizing images to uniform dimensions.

Converting color spaces if necessary (e.g., from RGB to grayscale).

Applying image thresholding to enhance cancer features.

Removing noise or artifacts that might affect the analysis.

1.2.4 Feature Extraction

Derivation of relevant features to be preprocessed for cancer imaging. These features include:

Shape Descriptors:

Extract shape features to characterize cancer cell shapes

[13]

.

Texture features:

Texture features are extracted to characterize cancer image textures.

1.2.5 Segmentation Using Gaussian Mixture Model (GMM)

Preprocess the images to enhance the features relevant to cancer image segmentation.

Initialize a GMM using scikit-learn (sklearn. mixture) library to fit the GMM to the preprocessed image data, cluster the cancer and background pixels

[14]

, and segment the cancer region based on the GMM clusters.

(1.1)

where:

x represents the observed data point (a vector in the feature space).

K

is the number of Gaussian components in the mixture.

π

k

represents the mixture coefficient or weight associated with the

k

th Gaussian component. It represents the probability of selecting the

k

th component.

μ

k

is the mean vector of the

k

th Gaussian component.

Σ

k

is the covariance matrix of the kth Gaussian component.

N

(x|μ

k

, Σ

k

) denotes the multivariate Gaussian distribution with mean μ

k

and covariance Σ

k

.

Different types of models are employed for cancer cell detection, each with its own balance between accuracy and efficiency. Convolutional neural networks (CNNs) [15], a type of deep learning model, are used in image classification tasks, such as cancer cell detection, because of their ability to learn intricate features from images. They typically offer high accuracy, often around 94%, but their efficiency can vary, with predictions taking approximately 0.5 s. Support Vector Machines (SVMs), which are classical machine learning models, also perform well in this domain, with an accuracy of approximately 88%. However, their efficiency tends to be lower, with predictions taking approximately 1.2 s. Random Forest, an ensemble learning method, achieves a high accuracy of approximately 91%, with moderate efficiency, required approximately 0.8 s for predictions. Logistic Regression, a simple linear model [16], offers moderate accuracy at around 85% and relatively better efficiency, with predictions taking around 0.6 s. Decision Trees, another non-parametric method, yield accuracy of about 87% with similar efficiency to Random Forest, taking around 0.7 s for predictions in Table 1.1. These models represent a spectrum of trade-offs between accuracy and efficiency, allowing tailored approaches based on specific requirements.

Table 1.1 Model accuracy and efficiency.

Model

Accuracy (%)

Efficiency (s)

Convolutional Neural Network (CNN)

94

0.5

Support Vector Machine (SVM)

88

1.2

Random Forest

91

0.8

Logistic Regression

85

0.6

Decision Tree

87

0.7

1.2.6 Classification and Cancer Detection

Defining the problem of cancer detection in medical images and establishing the scope of the project.

Gathering a diverse dataset of images containing cancer with and without affected parts.

Manually annotate the dataset, marking which images have been affected and which do not.

Loading the images and preprocessing them for further analysis

[17]

.

Identifying relevant features for cancer detection and using appropriate techniques to extract selected features from the pre-processed images.

Selecting a suitable machine-learning model for cancer detection.

The model was trained using the extracted features and the corresponding labels from the labeled dataset.

The dataset was divided into training and testing sets for the model evaluation.

Evaluating the model’s performance using metrics such as accuracy and precision

[16]

.

Adjust model parameters or consider using advanced techniques to improve performance.

1.2.6.1 CNN Algorithm

Step 1: Data Collection and Preprocessing

Gather a dataset of cancer images. This dataset should be labeled according to the presence or absence of cancer.

Preprocess the image by resizing it to a fixed size, normalizing pixel values(usually between 0 and 1), and augmenting the data to increase the diversity of the dataset and improve generalization.

Step 2: Model Architecture Design

Design the CNN architecture. This approach typically involves stacking convolutional, pooling, and fully connected layers and selecting an appropriate activation function.

Choose a suitable loss function, such as binary cross-entropy, for binary classification tasks or categorical cross-entropy for multi-class classification tasks

[17]

.

Select an optimization algorithm, such as Adam or RMSprop, to minimize the loss function during training.

Step 3: Model Training

Divide the dataset and compile the CNN model, specifying the loss function, optimizer, and evaluation metrics.

Train and monitor performance on the validation set to detect overfitting and adjust the hyperparameters accordingly.

Step 4: Model Deployment and Monitoring

Deploy the trained CNN model in a production environment to make predictions on new, unseen data.

Continuously monitor the model’s performance in the production environment and retrain or fine-tune the model as necessary to maintain optimal performance.

Step 5: Interpretation and Analysis

Interpret the predictions made by the model and analyze any misclassifications to identify potential areas for improvement

[18]

.

Iterate on the model design, data processing, and training process to improve the performance and address any identified issues [

19

,

20

].

1.2.7 Testing and Validation

Set aside a portion of the dataset for testing and validation.

Evaluate the performance of the system using appropriate metrics [

21

–

23

].

Based on the evaluation, we fine-tuned the system to improve performance.

1.2.8 Error Handling and Debugging

Exception Handling: Implementing robust error-handling mechanisms to handle potential errors during image processing and analysis [

24

,

25

].

Using debugging tools and techniques to identify and rectify the cancer-affected cells in the code.

1.2.9 Optimization and Scalability

Optimization of the cancer detection algorithm and associated processes for efficiency and speed

[26]

.

Scalability ensures that the cancer detection system can handle process many images in a scalable manner.

1.3 Result

The input data for machine-learning models must frequently have consistent dimensions. Ensuring that all the photographs in the dataset have the same form by shrinking them to a common size makes it simpler to input them into the model. We collect different types of cancer dataset from Kaggle.com and analysis this medical image and predict the accurate portion of the cancer affected image. (https://data.mendeley.com/datasets/p2r42nm2ty/1).

When working with picture data, image resizing is a crucial preprocessing step in machine learning. This includes resizing an image while keeping the aspect ratio intact. Our model analysis of the raw medical image and the resizing and labeling of the raw images are shown in Figure 1.2.

There are unique color and preprocessing issues when working with raw medical images for machine learning, such as cancer images from radiology or pathology. These images, which are normally grayscale or occasionally single channels, can include vital information for analysis and diagnosis.

When working with medical photos, grayscale conversion is frequently required because many of them are initially shot in that format. Images that have been converted to grayscale are easier for machine learning models to handle because they decrease dimensionality and simplify the data. It is simple to locate the damaged area due to this model’s analysis of the original medical picture and color (grayscale) change. The grayscale conversion is illustrated in Figure 1.3.

Figure 1.2 Resizing and labeling the raw image.

Figure 1.3 Changing color (grayscale conversion) of the raw image.

The model appears to be based on showing the Bayesian Information Criterion (BIC) values for each model after training the Gaussian Mixture Models (GMM) on picture data with various numbers of components (clusters). The first picture in a list of images, indicated by img_d [1], is given the name “training_img” in Figures 1.2, 1.3 and 1.4. This code assumes that img_d is a list of pictures and that the GMM models are trained on the first image.

In the context of machine learning for cancer diagnosis, accuracy refers to how well a model properly categorizes occurrences as either cancer-positive or cancer-negative, as in medical imaging. This is a fundamental assessment metric used to gauge the effectiveness of a model.

Creating a confusion matrix, which is a table summarizing the model’s performance, is the first step in evaluating accuracy:

True Positives (TP): The proportion of cancer instances that were accurately identified as positive.

True Negatives (TN): The proportion of cases appropriately identified as cancer-negative.

False Positives (FP): The number of cancer-negative cases that were mistakenly labeled as positive (Type I error).

False Negatives (FN): False negatives are cancer-positive instances that were mistakenly categorized as negative (Type II error).

It is crucial to remember that accuracy may not be the only or best parameter for assessing model performance in cancer diagnosis. Because the effects of false positives and false negatives might differ significantly in a medical setting, the sensitivity, specificity, accuracy, and other indicators are commonly combined with those mentioned above to provide a more complete picture of the model’s performance shown in Figure 1.4.

The confusion matrix of the proposed model is shown in Figure 1.5.

Accuracy: Accuracy is used to gauge how well the model is working. It measures the proportion of accurate occurrences to all instances.

(1.2)

Figure 1.4 Bayesian Information Criterion (BIC) values.

Figure 1.5 Confusion matrix of actual dataset.

For the above diagram,

Image histograms are frequently used in medical imaging to examine pixel intensity distribution within the images, particularly when dealing with grayscale images (such as X-rays, CT scans, or pathology images). The frequency of pixel intensities within an image is depicted graphically in an image histogram.

This can shed light on how tissue densities are contrasted and distributed in radiological images. This knowledge may be useful for differentiating malignant and healthy tissues in the context of cancer detection. Our model describes different portions in color, so we can easily predict whether a medical image is affected by cancer. As shown in Figure 1.6, our model predicts different colors in different parts of the medical image.

It is the final output of the dataset that has been provided. The images all have cancerous cells. In Figure 1.7 we show our final output.

Effective cancer therapy depends on early detection. Combining machine learning and the GMM can increase the precision of early cancer detection. More advanced models that can detect minute patterns and biomarkers in medical images, such as mammograms, CT scans, and pathology slides, are anticipated in the future. In the future, a deeper integration of deep learning with conventional machine learning techniques, such as GMM, may improve performance.

Figure 1.6 Histogram of our datasets.

Figure 1.7 Final output of raw dataset.

1.4 Conclusion

In an overview, our analysis highlights the promising potential of machine learning, particularly the Gaussian Mixture Model, for the early diagnosis of cancer. We have developed a reliable cancer diagnosis method by effectively using the GMM’s capacity to unearth underlying data structures and fusing it with a potent supervised classification model. This discovery has significant potential clinical impact, including implications for early diagnosis and individualized treatment. Nevertheless, this study has several limitations. The interpretability of the GMM components should be further investigated in future studies, as well as strategies for feature selection and model validation using larger and more varied datasets. Additionally, the accuracy and comprehensiveness of the cancer detection system can be improved by incorporating multimodal data sources.

Finally, our findings provide new perspectives on how machine learning and oncology interact. They have the potential to improve the diagnosis of cancer and enhance patient treatment, ultimately aiding in the fight against this terrible illness. The main points of this conclusion are summarized along with their clinical implications, limits, and recommendations for further studies. The tone is consistently one of science and rigor.

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Note

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Corresponding author

:

[email protected]