Artificial Intelligence for Sustainable Applications E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Artificial Intelligence and Soft Computing for Industrial Transformation
• Sprache: Englisch

Seitenzahl: 469

Veröffentlichungsjahr: 2023

Beschreibung

ARTIFICAL INTELLIGENCE for SUSTAINABLE APPLICATIONS

The objective of this book is to leverage the significance of artificial intelligence in achieving sustainable solutions using interdisciplinary research through innovative ideas.

With the advent of recent technologies, the demand for Information and Communication Technology (ICT)-based applications such as artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), health care, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks, has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore.

This book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. It covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results.

Audience

AI researchers as well as engineers in information technology and computer science.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Part I: Medical Applications

1 Predictive Models of Alzheimer’s Disease Using Machine Learning Algorithms – An Analysis

1.1 Introduction

1.2 Prediction of Diseases Using Machine Learning

1.3 Materials and Methods

1.4 Methods

1.5 ML Algorithm and Their Results

1.6 Support Vector Machine (SVM)

1.7 Logistic Regression

1.8 K Nearest Neighbor Algorithm (KNN)

1.9 Naive Bayes

1.10 Finding the Best Algorithm Using Experimenter Application

1.11 Conclusion

1.12 Future Scope

References

2 Bounding Box Region-Based Segmentation of COVID-19 X-Ray Images by Thresholding and Clustering

2.1 Introduction

2.2 Literature Review

2.3 Dataset Used

2.4 Proposed Method

2.5 Experimental Analysis

2.6 Conclusion

References

3 Steering Angle Prediction for Autonomous Vehicles Using Deep Learning Model with Optimized Hyperparameters

3.1 Introduction

3.2 Literature Review

3.3 Methodology

3.4 Experiment and Results

3.5 Conclusion

References

4 Review of Classification and Feature Selection Methods for Genome-Wide Association SNP for Breast Cancer

4.1 Introduction

4.2 Literature Analysis

4.3 Comparison Analysis

4.4 Issues of the Existing Works

4.5 Experimental Results

4.6 Conclusion and Future Work

References

5 COVID-19 Data Analysis Using the Trend Check Data Analysis Approaches

5.1 Introduction

5.2 Literature Survey

5.3 COVID-19 Data Segregation Analysis Using the Trend Check Approaches

5.4 Results and Discussion

5.5 Conclusion

References

6 Analyzing Statewise COVID-19 Lockdowns Using Support Vector Regression

6.1 Introduction

6.2 Background

6.3 Proposed Work

6.4 Experimental Results

6.5 Discussion and Conclusion

References

7 A Systematic Review for Medical Data Fusion Over Wireless Multimedia Sensor Networks

7.1 Introduction

7.2 Literature Survey Based on Brain Tumor Detection Methods

7.3 Literature Survey Based on WMSN

7.4 Literature Survey Based on Data Fusion

7.5 Conclusions

References

Part II: Data Analytics Applications

8 An Experimental Comparison on Machine Learning Ensemble Stacking-Based Air Quality Prediction System

8.1 Introduction

8.2 Related Work

8.3 Proposed Architecture for Air Quality Prediction System

8.4 Results and Discussion

8.5 Conclusion

References

9 An Enhanced K-Means Algorithm for Large Data Clustering in Social Media Networks

9.1 Introduction

9.2 Related Work

9.3 K-Means Algorithm

9.4 Data Partitioning

9.5 Experimental Results

9.6 Conclusion

Acknowledgments

References

10 An Analysis on Detection and Visualization of Code Smells

10.1 Introduction

10.2 Literature Survey

10.3 Code Smells

10.4 Comparative Analysis

10.5 Conclusion

References

11 Leveraging Classification Through AutoML and Microservices

11.1 Introduction

11.2 Related Work

11.3 Observations

11.4 Conceptual Architecture

11.5 Analysis of Results

11.6 Results and Discussion

References

Part III: E-Learning Applications

12 Virtual Teaching Activity Monitor

12.1 Introduction

12.2 Related Works

12.3 Methodology

12.4 Results and Discussion

12.5 Conclusions

References

13 AI-Based Development of Student E-Learning Framework

13.1 Introduction

13.2 Objective

13.3 Literature Survey

13.4 Proposed Student E-Learning Framework

13.5 System Architecture

13.6 Working Module Description

13.7 Conclusion

13.8 Future Enhancements

References

Part IV: Networks Application

14 A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks

14.1 Introduction

14.2 Anomaly Detection in WSN

14.3 Summary of Anomaly Detections Techniques Using Machine Learning Algorithms

14.4 Experimental Results and Challenges of Machine Learning Approaches

14.5 Performance Evaluation

14.6 Conclusion

References

15 Unique and Random Key Generation Using Deep Convolutional Neural Network and Genetic Algorithm for Secure Data Communication Over Wireless Network

15.1 Introduction

15.2 Literature Survey

15.3 Proposed Work

15.4 Genetic Algorithm (GA)

15.5 Conclusion

References

Part V: Automotive Applications

16 Review of Non-Recurrent Neural Networks for State of Charge Estimation of Batteries of Electric Vehicles

16.1 Introduction

16.2 Battery State of Charge Prediction Using Non-Recurrent Neural Networks

16.3 Evaluation of Charge Prediction Techniques

16.4 Conclusion

References

17 Driver Drowsiness Detection System

17.1 Introduction

17.2 Literature Survey

17.3 Components and Methodology

17.4 Conclusion

References

Part VI: Security Applications

18 An Extensive Study to Devise a Smart Solution for Healthcare IoT Security Using Deep Learning

18.1 Introduction

18.2 Related Literature

18.3 Proposed Model

18.4 Conclusions and Future Works

References

19 A Research on Lattice-Based Homomorphic Encryption Schemes

19.1 Introduction

19.2 Overview of Lattice-Based HE

19.3 Applications of Lattice HE

19.4 NTRU Scheme

19.5 GGH Signature Scheme

19.6 Related Work

19.7 Conclusion

References

20 Biometrics with Blockchain: A Better Secure Solution for Template Protection

20.1 Introduction

20.2 Blockchain Technology

20.3 Biometric Architecture

20.4 Blockchain in Biometrics

20.5 Conclusion

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Gives the percentage of the total dataset which were correctly and i...

Table 1.2 The table summarizes the metrics obtained from all the models.

Table 1.3 Area under ROC for all the algorithms.

Chapter 2

Table 2.1 Segmentation of images using various techniques.

Table 2.2 MPA and IoU scores of different segmentation algorithms.

Table 2.3 MPA and IoU scores of different segmentation algorithms.

Chapter 3

Table 3.1 Comparison of predicted angle vs. actual steering angle.

Chapter 4

Table 4.1 Benefits and drawbacks of current SNP-based risk.

Table 4.2 Two-class classification confusion matrix.

Chapter 6

Table 6.1 Comprehensive survey of ML applications.

Table 6.2 Comprehensive survey of ML/DL algorithms.

Table 6.3 Comprehensive survey of COVID-19 ML/DL.

Table 6.4 Comparison of various regression techniques.

Table 6.5 Duration of state-wide lockdowns in Maharashtra.

Table 6.6 Duration of state-wide lockdowns in Tamil Nadu.

Table 6.7 Duration of state-wide lockdowns in Odisha.

Table 6.8 Duration of state-wide lockdowns in Punjab.

Table 6.9 Population density and confirmed COVID-19 cases of the selected stat...

Chapter 7

Table 7.1 Comparisons of the existing methods for brain tumor detection with t...

Table 7.2 Comparisons of the existing methods for analyzing the quality of ser...

Table 7.3 Comparisons of the existing methods for data fusion with their limit...

Chapter 8

Table 8.1 AQI rank categories.

Table 8.2 Accuracy comparison of machine learning regression models with stack...

Chapter 9

Table 9.1 Description of data sets of various social media networks.

Table 9.2 Information of the real-world datasets and results of estimating.

Chapter 10

Table 10.1 Summary of latest machine learning detection techniques in code sme...

Table 10.2 Comparison of latest code smells detection tool publications.

Chapter 11

Table 11.1 Characteristics of classification and regression problems.

Table 11.2 Classification and regression algorithms supported by auto-sklearn....

Table 11.3 Layers and corresponding responsibilities.

Chapter 13

Table 13.1 List of keywords in pre-processing.

Table 13.2 Experimental results of various test cases.

Chapter 14

Table 14.1 Summary of anomaly detection techniques that adopts machine learnin...

Table 14.2 Summary of adaptive machine learning algorithms performance metrics...

Chapter 16

Table 16.1 Open-source data of batteries.

Chapter 19

Table 19.1 Security status of various crypto-systems.

List of Illustrations

Chapter 1

Figure 1.1 (a) Decision tree of j48 algorithm. (b) ROC curve for J48 algorithm...

Figure 1.2 (a) Pseudocode for Random Forest algorithm. (b) ROC curve for Rando...

Figure 1.3 ROC curve for SVM algorithm.

Figure 1.4 (a) Workflow for linear regression. (b) ROC curve for logistic regr...

Figure 1.5 ROC curve for Random Forest algorithm.

Figure 1.6 ROC curve for Naive Bayes algorithm.

Figure 1.7 ROC curve for KNN, NB, and RF.

Chapter 2

Figure 2.1 Proposed method.

Figure 2.2 Contrast limited adaptive histogram equalization.

Figure 2.3 (i) Sample image 1. (ii) Histogram of image 1. (iii) Image*1 after ...

Figure 2.4 Segmented images of image 1 and image*2. (i) Image1. (ii) Image wit...

Chapter 3

Figure 3.1 Example of lane marking detection.

Figure 3.2 System design.

Figure 3.3 Sample of a pre-processed image.

Figure 3.4 Affected elements for each hyperparameter.

Figure 3.5 Iterative process to fine-tune hyperparameters.

Figure 3.6 Convolution neural network.

Figure 3.7 Steering angle predictions.

Figure 3.8 Tendency of predicted angles.

Figure 3.9 Tendency of errors.

Figure 3.10 Comparison of train loss vs. test loss.

Chapter 4

Figure 4.1 Accuracy comparison results of different techniques.

Figure 4.2 Precision comparison results of different techniques.

Figure 4.3 Recall comparison results of different techniques.

Figure 4.4 F-measure comparison results of different techniques.

Chapter 5

Figure 5.1 Track check analysis system.

Graph 5.1 Track check segregation analysis 1.

Graph 5.2 Trend check analysis 2.

Graph 5.3 Visualization chart of world COVID cases.

Graph 5.4 Comparison of cases in hotspot countries.

Graph 5.5 Death rate per million hotspot countries.

Chapter 6

Figure 6.1 Context of the work.

Figure 6.2 The MVC architecture.

Figure 6.3 The conceptual architecture.

Figure 6.4 Illustration of SVR.

Figure 6.5 The predicted cases before and after Lockdown 1 in Maharashtra.

Figure 6.6 The predicted cases before and after Lockdown 2 in Maharashtra.

Figure 6.7 The predicted cases before and after Lockdown 1 in Tamil Nadu.

Figure 6.8 The predicted cases before and after Lockdown 2 in Tamil Nadu.

Figure 6.9 The predicted cases before and after Lockdown 1 in Odisha.

Figure 6.10 The predicted cases before and after Lockdown 2 in Odisha.

Figure 6.11 The predicted cases before and after Lockdown 1 in Punjab.

Figure 6.12 The predicted cases before and after Lockdown 2 in Punjab.

Figure 6.13 Area chart to show the predicted cases before and after Lockdown 1...

Figure 6.14 Area chart to show the predicted cases before and after Lockdown 2...

Chapter 8

Figure 8.1 Air quality prediction architecture methods. This layer is heart of...

Figure 8.2 Dataset splitting.

Figure 8.3 Algorithm for bagging.

Figure 8.4 Structure for bagging.

Figure 8.5 Algorithm for stacking.

Figure 8.6 Structure for stacking.

Figure 8.7 Algorithm for boosting.

Figure 8.8 Structure for boosting.

Figure 8.9 Sample data.

Figure 8.10 Comparison of RMSE value.

Figure 8.11 Comparison of MSE value.

Figure 8.12 Comparison of MAE value.

Figure 8.13 Prediction analysis of machine learning methods based on accuracy....

Chapter 9

Figure 9.1 (a) The K-means algorithm perform for cluster 0 to cluster4 for dif...

Chapter 10

Figure 10.1 A mind map of all the concepts covered in the literature review.

Chapter 11

Figure 11.1 Block diagram of ML incorporated micro application.

Figure 11.2 Conceptual architecture.

Figure 11.3 Best model for Case 1 – Sonar dataset.

Figure 11.4 Model performance metrics for Case 1 – Sonar dataset.

Figure 11.5 Best model for Case 2 – Liver Patients dataset.

Figure 11.6 Model performance metrics for Case 2 – Liver Patients dataset.

Figure 11.7 Best model for Case 3: Automobile Insurance claim dataset – Regres...

Chapter 12

Figure 12.1 Schematic depiction of the detection cascade.

Figure 12.2 Alert system block diagram.

Figure 12.3 Sixty-eight facial landmark coordinates from the iBUG 300-W datase...

Figure 12.4 The first two Haar-like features.

Figure 12.5 The six facial landmarks associated with the opening of an eye.

Figure 12.6 Dataset used for building our model.

Figure 12.7 One hundred twenty-eight feature values have been extracted from s...

Figure 12.8 Attention states.

Figure 12.9 Attendances using facial recognition (Excel sheet and real-time ex...

Figure 12.10 Network speed.

Figure 12.11 Text classification results.

Chapter 13

Figure 13.1 Student e-learning framework.

Figure 13.2 Detailed view of chatbot using ML andNLP.

Figure 13.3 AI model of query process.

Figure 13.4 Steps involved in data preprocessing.

Figure 13.5 Dataset training.

Chapter 14

Figure 14.1 Anomaly detection system setup.

Figure 14.2 Boxplot view to analyze the distribution of data attributes.

Figure 14.3 Dimensionality reduction using PCA with two principal components....

Figure 14.4 Scatter plots of (a) data with dimensionality reduction (b) PC aft...

Figure 14.5 Design of multi-layer neural network model for outlier detection....

Figure 14.6 (a) SVM Linear Classifier. (b) SVM Non-Linear Classifier with opti...

Figure 14.7 A Bayesian network in which each node is associated with a variabl...

Figure 14.8 A bar plot depicting the comparative analysis of adapted machine l...

Chapter 15

Figure 15.1 Flowchart of genetic algorithm.

Figure 15.2 Point addition.

Figure 15.3 Point doubling.

Figure 15.4 ECDH.

Figure 15.5 Flow diagram of the DCNN.

Figure 15.6 Deep CNN model.

Figure 15.7 Encryption time.

Figure 15.8 Decryption time.

Figure 15.9 Encryption performance.

Figure 15.10 Decryption performance.

Chapter 16

Figure 16.1 Hierarchy of methods of estimation of State-of-Charge and State-of...

Chapter 17

Figure 17.1 Facial landmark model.

Figure 17.2 Eye aspect ratio positions and formula.

Figure 17.3 Mouth aspect ratio position and formula.

Figure 17.4 Eye aspect ratio at threshold.

Figure 17.5 Eye aspect ratio below threshold value.

Figure 17.6 Mouth aspect ratio below threshold value.

Chapter 18

Figure 18.1 Schematic architecture diagram.

Chapter 19

Figure 19.1 Structure of lattice dimensions.

Figure 19.2 Lattice-based HE on cloud.

Figure 19.3 Encryption and decryption in NTRU.

Figure 19.4 NTRU in healthcare domain.

Figure 19.5 Lattice points in GGH scheme.

Chapter 20

Figure 20.1 Blockchain architecture.

Figure 20.2 Types of blockchain.

Figure 20.3 Traditional architecture of biometric recognition system.

Figure 20.4 Various attacks.

Figure 20.5 Various types of challenges in blockchain.

Figure 20.6 Architecture of biometrics with blockchain.

Guide

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

Also of Interest

End User License Agreement

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

Artificial Intelligence and Soft Computing for Industrial Transformation

Series Editor: S. Balamurugan

Scope: Artificial Intelligence and Soft Computing Techniques play an impeccable role in industrial transformation. The topics to be covered in this book series include Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Fuzzy Logic, Genetic Algorithms, Particle Swarm Optimization, Evolutionary Algorithms, Nature Inspired Algorithms, Simulated Annealing, Metaheuristics, Cuckoo Search, Firefly Optimization, Bio-inspired Algorithms, Ant Colony Optimization, Heuristic Search Techniques, Reinforcement Learning, Inductive Learning, Statistical Learning, Supervised and Unsupervised Learning, Association Learning and Clustering, Reasoning, Support Vector Machine, Differential Evolution Algorithms, Expert Systems, Neuro Fuzzy Hybrid Systems, Genetic Neuro Hybrid Systems, Genetic Fuzzy Hybrid Systems and other Hybridized Soft Computing Techniques and their applications for Industrial Transformation. The book series is aimed to provide comprehensive handbooks and reference books for the benefit of scientists, research scholars, students and industry professional working towards next generation industrial transformation.

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

Artificial Intelligence for Sustainable Applications

Edited by

K. Umamaheswari,

B. Vinoth Kumar

and

S. K. Somasundaram

Department of Information Technology at PSG College of Technology, Coimbatore, Tamil Nadu, India

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 9781394174584

Cover image: Pixabay.ComCover design by Russell Richardson

Preface

With the advent of recent technologies, the demand for Information and Communication Technology (ICT) based applications such as artificial intelligence (AI), machine learning, Internet of Things (IoT), healthcare, data analytics, augmented reality/virtual reality, cyber-physical systems, and future generation networks has increased drastically. In recent years, artificial intelligence has played a more significant role in everyday activities. While AI creates opportunities, it also presents greater challenges in the sustainable development of engineering applications. Therefore, the association between AI and sustainable applications is an essential field of research. Moreover, the applications of sustainable products have come a long way in the past few decades, driven by social and environmental awareness, and abundant modernization in the pertinent field. New research efforts are inevitable in the ongoing design of sustainable applications, which makes the study of communication between them a promising field to explore.

The book highlights the recent advances in AI and its allied technologies with a special focus on sustainable applications. The content is structured as six different parts focusing on medical, data analytics, e-learning, network, automotive and security applications. The goal of the book is to help researchers and practitioners enrich their knowledge and provide a learning resource for scholars to enhance their latest research ideas and developments. The book covers theoretical background, a hands-on approach, and real-time use cases with experimental and analytical results. A brief introduction to each chapter is as follows:

Chapter 1 discusses the prospect of a predictive machine-learning model for Alzheimer’s disease with the help of a minimally invasive blood-based biomarker.

Chapter 2 presents the Bounding Box-based segmentation methods through thresholding, K-Means, and Fuzzy K-Means clustering to segment the COVID-19 chest X-ray images through simple calculations and fast operation.

Chapter 3 describes a model that can anticipate steering angles nearly identical to how a human would manage a car’s steering wheel.

Chapter 4 aims to determine the most effective approach to analyzing Single-Nucleotide Polymorphism (SNP) data by combining several Feature Selection (FS) and classification methods. This chapter describes genome-wide prediction analysis to construct methods that predict and diagnose breast cancer dependent on the SNP set.

Chapter 5 discusses the coronavirus spread analysis with the day-to-day ascending behavior analysis by employing trend-check segregation algorithms. Using this approach, the collected COVID-19 data has been analyzed and visualized with respect to the affected cases.

Chapter 6 focuses on analyzing the effectiveness of statewide lockdowns by using Support Vector Regression (SVR) to forecast COVID-19 trends at different intervals, and uses the results generated to understand the effect of these lockdowns on the COVID-19 cases across various states in India.

Chapter 7 presents various existing methodologies for brain tumor detection and segmentation, quality of service, and the improvement of routing paths in Wireless Multimedia Sensor Networks (WMSN), as well as various data fusion methods.

Chapter 8 applies the ensemble method of machine-learning algorithms to predict the air quality and analyze these results in accordance with the comparison of other regression algorithms.

Chapter 9 proposes a new k-means algorithm for huge information grouping that utilizes refined charts in a web-based media organization and information bunching to track down the number of bunches in a set of information.

Chapter 10 provides an up-to-date review of the recent developments in code-smell detection algorithms that employ machine-learning techniques. The chapter covers various aspects, from finding code-smells in Machine-Learning based projects to the detection of code-smells in API documentation.

Chapter 11 explains how to methodically obtain datasets and domain knowledge from consumers and compose corresponding micro-apps for them by using a micro-intelligence application platform capable of classification or regression. This is done while simultaneously guaranteeing that the complexities of ML code, such as model selection and hyperparameter tuning, are abstracted from the client side using AutoML.

Chapter 12 suggests attention estimation techniques to bridge the gap between an online classroom and a traditional offline classroom.

Chapter 13 explores a full-on educational-oriented chatbot, describing various experiences for the users and also precisely answering related questions. The proposed framework provides the student with a specific solution to his or her issue, rather than getting multiple solutions on the internet.

Chapter 14 discusses machine-learning techniques that are suitable for anomaly detection and their challenges based on performance metrics factors. The research issues of various anomaly detection techniques are presented with a brief discussion on certain adaptive algorithms.

Chapter 15 focuses on cryptographic key generation using Elliptic Curve Diffie-Hellman (ECDH) with Deep Convolutional Neural Network (DCNN) and Genetic Algorithm (GA), and how high data confidentiality and data integrity are achieved by preventing unauthorized manipulations and message denial.

Chapter 16 reviews non-recurrent State-of-Charge (SoC) estimation techniques such as Feed-forward Neural Networks (FNNs), Radial Basis Functions (RBF), Extreme Learning Machines (ELM), and Support Vector Machines (SVM). It is recommended that the SoC Estimation Techniques under comparison should share common data sets (both training and testing) and learnable parameters, or else the comparison may be biased.

Chapter 17 introduces a novel system that helps to avoid accidents by selecting two parameters, such as eye and mouth, that help to locate the facial landmarks. Based on that, the eye and mouth aspect ratio are tracked, which helps to identify drowsiness sooner and avoid accidents.

Chapter 18 proposes a smart solution to the security of healthcare IoT-based systems using deep learning-based techniques.

Chapter 19 gives a clear, detailed view of how lattice-based homomorphic encryption works and outlines its uses. In addition, this chapter also aims to discuss the applications that use lattice-based homomorphic encryption and their significance in the recently growing domains of protecting and securing a large amount of data from unauthorized break-ins and destruction.

Chapter 20 focuses on biometric template storage and preservation, and the advantages and challenges of merging blockchain with biometrics. The suggested approach demonstrates that merging biometrics with blockchain improves biometric template protection.

We are grateful to the authors and reviewers for their excellent contributions in making this book possible. Our special thanks go to Mr. Martin Scrivener, Scrivener Publishing, Beverly, MA, for the opportunity to organize this edited book. We are obliged to Dr. S. Balamurugan, Director - Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India for an excellent collaboration.

We hope this book will inspire researchers and practitioners from academia and industry alike, and spur further advances in the field.

Dr. K. Umamaheswari

Dr. B. Vinoth Kumar

Dr. S. K. Somasundaram

July 2023

Part IMEDICAL APPLICATIONS