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Mathematics and Computer Science for Real-World Applications

Edited by Biswadip Basu Mallik, M. Niranjanamurthy, Sharmistha Ghosh, Krishanu Deyasi, and Santanu Das
Series: Advances in Data Engineering and Machine Learning
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394275328  |  Hardcover  |  
634 pages
Price: $225 USD
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One Line Description
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.

Audience
Professors, students, and researchers working in research areas such as mathematics, statistics, theoretical computer science, computer applications, and data science

Description
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.

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Author / Editor Details
Biswadip Basu Mallik, PhD is a senior assistant professor in the Department of Basic Science and Humanities at the Institute of Engineering and Management, Kolkata, with over 23 years of teaching experience. He has over 85 publications to his credit, including eight books and several book chapters, research articles, and conference proceedings. His research interests include computational fluid dynamics, mathematical modeling, data science, and machine learning.

M. Niranjanamurthy, PhD is an assistant professor in the Department of Artificial Intelligence and Machine Learning at the Bhusanayana Mukundadas Sreenivasaiah Institute of Technology and Management. He has over 90 publications, including six books and numerous book chapters, articles, and conference proceedings.

Sharmistha Ghosh, PhD is a Mathematics professor in the Department of Basic Science and Humanities at the Institute of Engineering and Management, Kolkata, with over 22 years of experience. She has published one book in addition to numerous conference proceedings and articles in international refereed journals.

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.

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

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Table of Contents
Preface
1. Analysis of Medical Image Using a Multimodal Approach for Precise Cancer Detection
Soumen Santra, Mouparna De, Dipankar Majumdar and Surajit Mandal
1.1 Introduction
1.2 Methodology
1.2.1 Understanding the Objective and Scope
1.2.2 Data Collection and Importation
1.2.3 Image Preprocessing
1.2.4 Feature Extraction
1.2.5 Segmentation Using Gaussian Mixture Model (GMM)
1.2.6 Classification and Cancer Detection
1.2.6.1 CNN Algorithm
1.2.7 Testing and Validation
1.2.8 Error Handling and Debugging
1.2.9 Optimization and Scalability
1.3 Result
1.4 Conclusion
References
2. A Review on Contemporary Advancements in Conversational AI Within the Cloud Platform
Srinivasa Rao Gundu, Panem Charanarur and Biswadip Basu Mallik
2.1 Introduction
2.2 Conversational Features of Human Automatic Speech Recognition System
2.2.1 Morality in Artificial Intelligence-Based Conversation
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
K. Jyothsna, P. Vijaya Kumar and P. Vijaya Laxmi
3.1 Introduction
3.2 Outline of the Model
3.3 Model Analysis
3.3.1 Probabilities at the Pre-Arrival Instants
3.3.2 Probabilities at the Arbitrary Instants
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
Prayosi Chatterjee and Mijanur Rahaman Seikh
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
4.5.1 Comparative Analysis
4.6 Conclusion
References
5. Micro Vague Generalized Semi Connectedness in Micro Vague Topological Spaces
Vargees Vahini T. and Trinita Pricilla M.
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
Tamaghna Dutta and Soumen Santra
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
R. Sathiyapriya and M.A. Gopalan
7.1 Introduction
7.2 Method of Analysis
7.3 Conclusion
References
8. Uniqueness Results to the Nonlinear Boundary Value Problems of Fourth Order
B. Madhubabu, N. Sreedhar and K. R. Prasad
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
Amritha Jayaram and Nimitha John
9.1 Introduction
9.2 Cointegration and Error Correction Model
9.3 Model Specification and Parameter Estimation
9.4 Simulation
9.4.1 Simulation Results
9.5 Conclusions and Perspectives
References
10. Accelerated Reliability Sampling Plan Based on Transformed Lindley Distribution
Rini Raju and Jiju Gillariose
10.1 Introduction
10.2 Lindley Distribution
10.3 Methodology
10.3.1 Assumptions
10.3.2 Acceptance Procedure
10.3.3 Design of ARSP
10.4 Example
10.4.1 Sensitivity Analysis
10.5 Conclusion
References
11. A Synopsis of Fuzzy Set Theory
Praphull Chhabra and Sonam Chhabra
11.1 History of Fuzzy Set
11.2 Basic Definitions
11.3 Fuzzy Measures of Information
11.4 Operations on Fuzzy Sets
11.4.1 Intersection (logical and)
11.4.2 Uniformity (exclusive or)
11.4.3 Complement (negation)
11.5 Conclusion
References
12. Efficient Classification of Breast Cancer Diseases on Medical Images Using Deep Learning Methodology
Pramit Brata Chanda, Subhadip Das and Subir Kumar Sarkar
12.1 Introduction
12.2 Literature Review
12.3 Proposed Methodologies
12.3.1 Dataset Used
12.3.2 Imported Python Libraries for Building Models
12.3.3 Image Pre-Processing
12.3.4 Data Augmentation
12.4 Results and Analysis
12.4.1 Recall
12.4.2 Precision
12.5 Conclusion
References
13. Low Power VLSI Architecture for 48-Bit Multiplication Using Elliptic Curve Algorithm
Bommi R.M., Uganya G., A. Mary Joy Kinol and Blessy Sam A.S.
13.1 Introduction
13.2 Related Works
13.3 DADDA Multiplier
13.4 Elliptic Curve Multiplier
13.4.1 Method for Implementation
13.4.2 Improved Multiplication Algorithm
13.5 Results and Discussion
13.5.1 Simulation Result
13.5.2 Comparison Table
13.6 Conclusion
References
14. Enhancing Cloud Computing Security Through Decimal Bond DNA Cryptography (DBDNA): A Novel Approach
Animesh Kairi and Tapas Bhadra
14.1 Introduction
14.2 Literature Survey
14.3 DNA and Cloud Computing
14.4 Proposed DNA Cryptography in Cloud Computing Security
14.4.1 Encryption Process
14.4.2 Decryption Process
14.5 Result Analysis
14.6 Conclusion
References
15. Fake News Detection in Healthcare Using Machine Learning
Tessa Hormese and R. Rajesh
15.1 Introduction
15.2 Related Works
15.3 Proposed Approach and Models
15.3.1 Proposed Framework
15.3.2 Dataset Preparation
15.3.3 Feature Extraction
15.4 Fake News Detection Models
15.4.1 Logistic Regression
15.4.2 Support Vector Machine
15.4.3 K-Nearest Neighbor
15.4.4 Random Forest
15.4.5 Multinomial Naive Bayes
15.4.6 Gradient Boosting
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
P. Vijaya Kumar, K. Jyothsna and S. Mohammed Ibrahim
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
Subhas Halder, Satrajit Das, Sukanta Kundu and Hiranmoy Samanta
17.1 Introduction
17.2 Opportunities of this Study
17.3 Available ML Algorithms for Liver Disease Prediction
17.3.1 Logistic Regression
17.3.2 Random Forest
17.3.3 Decision Tree
17.3.4 K-Nearest Neighbors (KNNs)
17.3.5 Naïve Bayes
17.4 Recommended Structure
17.5 Materials and Methodology
17.6 Preparation of Data
17.6.1 Data Collection
17.6.1.1 Data Prepressing
17.6.1.2 Acquire the Dataset
17.6.2 Preprocessing Dataset
17.6.2.1 Importing Libraries
17.6.2.2 Importing the Datasets
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
Siddhant Shaw, Mouparna De, Soumen Santra, Anirban Sarkar and Subrata Jana
18.1 Introduction
18.2 Methodology
18.2.1 Problem Definition and Scope
18.2.2 Selection of the MERN Stack
18.2.3 System Architecture and Design
18.2.4 Clustering Algorithm Implementation
18.2.4.1 K-Means Clustering Algorithm for Identifying Potential Blood Donors
18.2.5 Integration and Testing
18.2.6 Deployment and User Feedback
18.3 Result and Discussion
18.3.1 Clustering Algorithm Performance
18.3.2 Clustering Algorithm Evaluation
18.3.3 System Interface
18.3.4 System Responsiveness and Scalability
18.3.5 User Feedback and Satisfaction
18.3.6 Case Studies and Simulations
18.3.7 Long-Term Adaptability
18.3.8 Public Health Impact
18.4 Conclusion
References
19. Artificial Intelligence in Detection and Classification of Lung Cancer - An Overview
Sanjukta Chakraborty and Dilip Kumar Banerjee
19.1 Introduction
19.2 Evolution of Artificial Intelligence in the Detection and Classification of Lung Cancer
19.2.1 Early Stages
19.2.2 Introduction of Machine Learning
19.2.3 Image Segmentation and Feature Extraction
19.2.4 Rise of Deep Learning (DL)
19.2.5 Large-Scale Datasets and Training Pipelines
19.2.6 Binary Classification
19.2.7 Integration with Multimodal Imaging
19.2.8 Explainable AI and Interpretability
19.2.9 Real-Time Decision Support
19.2.10 Ongoing Research and Future Directions
19.3 Related Works
19.4 Problem Statement
19.4.1 Introduction to the Problem
19.4.2 Challenges in Lung Cancer Detection
19.4.3 Opportunities and Solutions
19.4.4 Future Directions and Research Considerations
19.5 Methodology
19.5.1 Workflow of the Proposed Model
19.5.2 SVM in Detection and Classification of Lung Cancer
19.5.3 Random Forest in Detection and Classification of Lung Cancer
19.5.4 KNN in Detection and Classification of Lung Cancer
19.5.5 Decision Tree in Detection and Classification of Lung Cancer
19.6 Results
19.6.1 Interpretations of Different AI/ML Techniques for the Detection and Classification of Lung Cancer
19.6.2 Comparative Analysis of SVM, Random Forest, KNN, Decision Tree in Prediction of Lung Cancer in Terms of Performance Metrics
19.6.3 Impact of Artificial Intelligence on Lung Cancer Detection
19.6.4 Advancements of Artificial Intelligence in the Detection and Classification of Lung Cancer
19.7 Future Directions
19.8 Conclusion
Acknowledgments
References
20. Applications of Image Processing for Surface Irregularities Detection and Comparison with Nondestructive Testing Results
Satyabrata Podder, Arka Dasgupta and Sumana Chakraborty
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
20.4.1 Crack Detection of Welded Joints
20.4.2 Crack Detection on Concrete Surface
20.5 Results and Discussion
20.5.1 Image Processing Applied in Radiography Test Results on Welded Samples
20.5.2 Image Processing Applied in Visual Test Results on Welded Samples
20.5.3 Image Processing Applied in Crack Detection on Concrete Surface
20.5.4 Image Processing Methodology Applied for Surface Cracks
20.6 Conclusions
Acknowledgments
References
21. Detection of Fraud Review Through Object Recognition for Fake Picture Component Using Machine Learning Approach
Ankita Chakraborty, Riya Bhunia, Soumen Santra, Anirban Sarkar, Subrata Jana and Santanu Dasgupta
21.1 Introduction
21.2 Methodology
21.2.1 Data Collection and Preparation
21.2.2 Preprocessing
21.2.3 Feature Extraction
21.2.4 Model Selection
21.2.5 Model Training
21.2.6 Validation and Testing
21.2.7 Fake Component Detection
21.2.8 Fine-Tuning and Optimization
21.2.9 Deployment and Monitoring
21.2.10 Continual Improvement
21.3 Result and Discussion
21.4 Conclusion
References
22. Gas Leakage Surveillance System Leveraging Using Spartan 7 FPGA and GSM Technology
Piyali Saha, Biswajit Kundu and Soumya Sen
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
Anisha Naskar
23.1 Introduction
23.1.1 Human Focused
23.1.2 Image Diagnosis
23.1.3 Telemedicine
23.1.4 Robotic Surgery
23.1.5 Data Analysis and Clinical Trials
23.1.6 Medical Implants
23.1.7 Medical Writing
23.1.8 Gene-Modification
23.1.9 Sustainability
23.1.10 Transformative
23.2 Literature Review
23.3 Conclusion
References
24. Preference Analysis Can Be a Guide to an Inamorata to Select Her Swain
Subrata Jana, Anirban Sarkar, Sayantani Paul, Arpan Ghoshal, Binay Maji and Biswadip Basu Mallik
24.1 Introduction
24.1.1 Universal Love
24.1.2 Courtship
24.1.3 Groom Selection
24.1.4 Justification of Methods
24.2 Review
24.3 Objectives
24.4 Preliminaries
24.5 Data and Methodology
24.6 Results
24.7 Analysis
24.8 Conclusions
24.8.1 Limitations
24.8.2 Future Scopes
References
25. A Comparative Study on Various Types of Algorithms of Artificial Neural Network for Solar Still Study: A Review
Jayanta Chanda and Mrinal Kanti Manik
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
Arindum Das, Bidisha Karmakar, Shaswati Roy, Tejaswita Kumari and Atanu Chowdhury
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
Riya Bhunia, Ankita Chakraborty, Soumen Santra, Anirban Sarkar, Subrata Jana and Santanu Dasgupta
27.1 Introduction
27.2 Literature Review
27.3 Methodology
27.3.1 Data Collection
27.3.2 Data Preprocessing
27.3.3 Thematic Analysis
27.3.4 Model Training
27.3.5 Text Vectorization
27.3.6 Feature Extraction
27.3.7 Confusion Matrix
27.3.8 Topic Modeling
27.3.9 Familiarization with Data
27.3.10 Data Coding
27.3.11 Word Cloud Generation
27.3.12 Visualization and Interpretation
27.3.13 Reporting and Insights
27.3.14 Report and Interpret the Result
27.4 Results and Discussion
27.5 Conclusion
References
28. Artificial Intelligence Enabled Non-Destructive Testing and Engineering
Satyabrata Podder, Arka Dasgupta and Biswajit Bhattachary
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
Priya Yadav
29.1 Introduction
29.2 Techniques Used to Raise Soil Temperature
29.2.1 Plastic Mulching
29.2.2 Wall O’ Water
29.3 Methodology
29.3.1 Random Forest Classifier
29.3.2 Decision Tree
29.4 Results
References
30. Wavelets and Their Recent Applications
Jamkhongam Touthang
30.1 Introduction
30.2 Wavelets and Hilbert Spaces
30.3 Desirable Features
30.4 Families of Wavelets
30.5 Mathematical Properties of Wavelets
30.5.1 Multiresolution Analysis
30.5.2 Vanishing Moments
30.5.3 Singularities
30.5.4 Frames
30.5.5 Frame Multiresolution Analysis (FMRA)
30.5.6 Extension Principles
30.5.7 Fourier Transform in Wavelet Analysis
30.6 Gabor Systems
30.7 Applications
30.7.1 Neural Networks
30.7.2 Image Compression
30.7.3 Fingerprints
30.7.4 Google Maps
30.7.5 ECG Identification
30.7.6 ECG Security Application
30.7.7 EEG Signal Processing
30.7.8 Cybersecurity
30.7.9 Geophysical Applications
30.7.10 Mobile Internet Voting
30.8 Conclusion
References
31. Detection and Forecasting of Dengue Fever Using Data Mining Techniques
Kousik Bhattacharya, Anirban Das and Dilip K. Banerjee
31.1 Introduction
31.2 Review of Literature
31.3 Methodology
31.3.1 Support Vector Machine (SVM)
31.3.2 Decision Tree (DT)
31.3.3 Logistics Regression (LR)
31.3.4 Naïve Bayes (NB)
31.3.5 Random Forest (RF)
31.3.6 AdaBoost
31.3.7 Cohen’s Kappa (CK)
31.3.8 ROC_AUC
31.4 Data Sets
31.5 Results
31.5.1 Creating New Classifier with Ensemble
31.5.2 Summarized Report Using ERF Model
31.5.3 Ensemble New Model
31.6 Discussions of Results
31.6.1 Responses in Triangular Fuzzy Number Form (2024)
31.6.2 Responses in Triangular Fuzzy Number Form (2023)
31.7 Conclusion
31.8 Future Scope
References
32. Image Classification Using CNN for the Detection of Cancer Cells to Avoid Metastasis
Soumen Santra, Rohan Chakraborty, Dipankar Majumdar and Surajit Mandal
32.1 Introduction
32.2 Literature Review
32.3 Methodology
32.3.1 Data Collection and Preprocessing
32.3.2 Feature Extraction Using CNN
32.3.3 Model Training and Evaluation
32.3.4 Deployment and Future Work
32.4 Results and Discussion
32.5 Conclusion
References
33. Revolutionizing Industries: Addressing Challenges and Innovations from Industry 4.0 to Industry 5.0
Moutusi Mondal and Mauparna Nandan
33.1 Introduction
33.2 Literature Reviews
33.3 Research Objectives
33.4 Research Methodology
33.4.1 Industry 4.0
33.4.2 Internet of Things (IoT)
33.4.3 Artificial Intelligence (AI)
33.4.4 Computer Aided Designing (CAD)
33.4.5 Additive Manufacturing (AM)
33.4.6 Cloud Services
33.4.7 Cyber Physical Systems
33.4.8 Augmented Reality
33.4.9 Global Positioning System (GPS)
33.4.10 Nanotechnology
33.4.11 Sensors and Actuators
33.5 Challenges of Industry 4.0
33.6 Industry 5.0
33.6.1 Smart Additive Manufacturing
33.6.2 Collaborative Robots
33.6.3 Cognitive Systems
33.6.4 Hyper Customization
33.7 Industry 5.0 to Overcome the Challenges of Industry 4.0
33.7.1 Supply Chain Issue
33.7.2 The Challenge of Data Security
33.7.3 Issues of Technical Integration
33.7.4 Human Resource Issues
33.8 Results and Discussions
33.8.1 Limitation
33.9 Conclusion
References
34. Portfolio Optimization Using Genetic Algorithm
S. Sowmya, Rhimjhim Daftary, Soumya Banerjee and Abhishikta Basak
34.1 Introduction
34.2 Materials and Methods
34.2.1 Genetic Algorithm
34.3 Results and Discussion
34.4 Conclusion
Bibliography
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