AI and IoT-Based Intelligent Health Care and Sanitation / edited by Shashank Awasthi [and three others].

Format:

Book

Contributor:

Awasthi, Shashank, editor.

Language:

English

Subjects (All):

Artificial intelligence.

Medical informatics.

Physical Description:

1 online resource (310 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Pte. Ltd., 2023.

Summary:

The book aims to provide a deeper understanding of the synergistic impact of Artificial intelligence (AI) and the Internet of Things (IoT) for disease detection. It presents a collection of topics designed to explain methods to detect different diseases in humans and plants. Chapters are edited by experts in IT and machine learning, and are structured to make the volume accessible to a wide range of readers. Key Features: - 17 Chapters present information about the applications of AI and IoT in clinical medicine and plant biology - Provides examples of algorithms for heart diseases, Alzheimer's disease, cancer, pneumonia and more - Includes techniques to detect plant disease - Includes information about the application of machine learning in specific imaging modalities - Highlights the use of a variety of advanced Deep learning techniques like Mask R-CNN - Each chapter provides an introduction and literature review and the relevant protocols to follow The book is an informative guide for data and computer scientists working to improve disease detection techniques in medical and life sciences research. It also serves as a reference for engineers working in the healthcare delivery sector.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Preface

KEY FEATURES

List of Contributors

IoT Based Website for Identification of Acute Lymphoblastic Leukemia using DL

R. Ambika1,*, S. Thejaswini2,*, N. Ramesh Babu3,*, Tariq Hussain Sheikh4, Nagaraj Bhat5 and Zafaryab Rasool6

1. INTRODUCTION

2. LITERATURE SURVEY

3. MATERIALS AND METHODS

4. DATA COLLECTION

5. DATA PREPROCESSING

5.1. Resizing

5.2. Image Augmentation

6. DL - VGG 16

7. WEB DEVELOPMENT

8. RESULTS AND DISCUSSION

9. ADVANTAGES OF THE STUDY

CONCLUSION

CONSENT FOR PUBLICATON

CONFLICT OF INTEREST

ACKNOWLEDGEMENT

REFERENCES

AI and IoT-based Intelligent Management of Heart Rate Monitoring Systems

Vedanarayanan Venugopal1,*, Sujata V. Mallapur2, T.N.R. Kumar3, V. Shanmugasundaram4, M. Lakshminarayana5 and Ajit Kumar6

3. PROPOSED SYSTEM

4. ARTIFICIAL NEURAL NETWORK

5. IMPLEMENTATION

6. RESULT AND DISCUSSION

7. STATE OF ART

Deep Learning Applications for IoT in Healthcare Using Effects of Mobile Computing

Koteswara Rao Vaddempudi1,*, K.R. Shobha2, Ahmed Mateen Buttar3, Sonu Kumar4, C.R. Aditya5 and Ajit Kumar6

4. DATASET DESCRIPTION

5. ARTIFICIAL NEURAL NETWORK

6. IMPLEMENTATION

7. RESULT AND DISCUSSION

8. NOVELTY OF THE STUDY

Innovative IoT-Based Wearable Sensors for the Prediction &amp

Analysis of Diseases in the Healthcare Sector.

Koteswara Rao Vaddempudi1, Abdul Rahman H Ali2, Abdullah Al-Shenqiti3, Christopher Francis Britto4, N. Krishnamoorthy5 and Aman Abidi6,*

3.1. Block Diagram

3.2. Flow Diagram

3.3. Hardware Implementation

3.4. IoT Cloud

4. RESULTS AND DISCUSSION

5. CONTRIBUTION TO THE HEALTH SECTOR

Construction and Evaluation of Deep Neural Network-based Predictive Controller for Drug Preparation

K. Sheela Sobana Rani1, Dattathreya 2, Shubhi Jain3, Nayani Sateesh4, M. Lakshminarayana5 and Dimitrios Alexios Karras6,*

2. MODEL DESCRIPTION

3. SYSTEM IDENTIFICATION

3.1. Material Balance Equation for Drug Preparation

3.2. Mass Flow Rate for Drug Preparation

4. PREDICTIVE CONTROLLER

5. RESULTS

5.1. Simulation Results of Drug Preparation for NN Controller and PID Controller

6. DISCUSSION

Machine Learning based Predictive Analysis and Algorithm for Analysis Severity of Breast Cancer

B. Radha1,*, Chandra Sekhar Kolli2, K R Prasanna Kumar3, Perumalraja Rengaraju4, S. Kamalesh4 and Ahmed Mateen Buttar5

2. MATERIALS AND METHODS

3. FE

3.1. DB4

3.2. HAAR

4. CLASSIFICATION TECHNIQUES

4.1. SVM

4.2. RF

4.3. LDA

5. RESULT AND DISCUSSION

6. GAPS FILLED

Glaucoma Detection Using Retinal Fundus Image by Employing Deep Learning Algorithm

K.T. Ilayarajaa1, M. Sugadev1, Shantala Devi Patil1, V. Vani2, H. Roopa2 and Sachin Kumar2,*

3. MATERIALS AND METHODS.

4. PREPROCESSING TECHNIQUES

5. DL MODEL

5.1. Transfer Learning (VGG-19)

5.2. CNN

6. RESULT ANALYSIS

7. DISCUSSION

Texture Analysis-based Features Extraction &amp

Classification of Lung Cancer Using Machine Learning

Korla Swaroopa1,*, N. Chaitanya Kumar2, Christopher Francis Britto3, M. Malathi4, Karthika Ganesan5 and Sachin Kumar6

2. METHODOLOGY

3.1. GLCM

3.2. GLRM

4. CLASSIFICATION METHODS

4.2. KNN

5. RESULTS AND DISCUSSION

Implementation of the Deep Learning-based Website For Pneumonia Detection &amp

Classification

V. Vedanarayanan1, Nagaraj G. Cholli2, Merin Meleet2, Bharat Maurya3, G. Appasami4 and Madhu Khurana5,*

3. PREPROCESSING TECHNIQUES

3.1. Data Resizing

3.2. Data Augmentation

4. DL TECHNIQUES

4.1. VGG-16

4.2. RESNET-50

5. WEB DEVELOPMENT

Design and Development of Deep Learning Model For Predicting Skin Cancer and Deployed Using a Mobile App

Shweta M Madiwal1,*, M. Sudhakar2, Muthukumar Subramanian3, B. Venkata Srinivasulu4, S. Nagaprasad5 and Madhu Khurana6

3. PREPROCESSING

4. PREDICTION METHODS

5. DEPLOYMENT

6. RESULT

Feature Extraction and Diagnosis of Dementia using Magnetic Resonance Imaging.

Praveen Gupta1,*, Nagendra Kumar2, Ajad2, N. Arulkumar3 and Muthukumar Subramanian4

3. FE TECHNIQUES

4.1. Support Vector Machine (SVM)

4.2. K-Nearest Neighbor (KNN)

4.3. Random Forest (RF)

4.4. Proposed Method

6. PROPOSED IDEA

Deep Learning-Based Regulation of Healthcare Efficiency and Medical Services

T. Vamshi Mohana1,*, Mrunalini U. Buradkar2, Kamal Alaskar3, Tariq Hussain Sheikh4 and Makhan Kumbhkar5

2. IOT IN MEDICAL CARE SERVICES

3. RELATED WORKS

4. PROPOSED SYSTEM

4.1. Interaction of RNN with LSTM

6. NOVELTY OF THE PROPOSED WORK

An Efficient Design and Comparison of Machine Learning Model for Diagnosis of Cardiovascular Disease

Dillip Narayan Sahu1,*, G. Sudhakar2, Chandrakala G Raju3, Hemlata Joshi4 and Makhan Kumbhkar5

1.1. Literature Survey

2. ML

3. METHODOLOGY

3.1. Source of Data

3.2. Data Pre-processing

4. ML ALGORITHM

4.1. NB Classifier

4.2. SVM

4.3 KNN

5. RESULT AND ANALYSIS

5.1. Performance Measures

5.2. Confusion Matrix: NB Classifier

5.3. Confusion Matrix: SVM

5.4. Confusion Matrix: KNN

5.5. ML Algorithm Comparison

6. IMPORTANCE OF THE STUDY

Deep Learning Based Object Detection Using Mask R-CNN

Vinit Gupta1,*, Aditya Mandloi1, Santosh Pawar2, T.V Aravinda3 and K.R Krishnareddy3

3. METHODOLOGIES.

3.1. Data Collection and Preprocessing

3.2. Construction of Mask R-CNN

3.3. Training of Mask R-CNN

4. RESULT ANALYSIS

5. DISCUSSION

Design and Comparison Of Deep Learning Architecture For Image-based Detection of Plant Diseases

Makarand Upadhyaya1,*, Naveen Nagendrappa Malvade2, Arvind Kumar Shukla3, Ranjan Walia4 and K Nirmala Devi5

3. DATA COLLECTION AND PREPARATION

3.1. Data Collection

3.2. Data Preprocessing

3.3. Data Augmentation

4. DEEP LEARNING NETWORKS

4.1. Convolution Neural Network

4.2. CNN-Long Short-Term Memory

5.1. CNN Performance Measure

Discernment of Paddy Crop Disease by Employing CNN and Transfer Learning Methods of Deep Learning

Arvind Kumar Shukla1,*, Naveen Nagendrappa Malvade2, Girish Saunshi3, P. Rajasekar4 and S.V. Vijaya Karthik4

3. METHODOLOGIES

4. DISEASE AND PREPROCESSING

4.1. Rescaling

4.2. Image Shearing

4.3. Zooming

4.4. Horizontal Flip

5. DL METHODS

5.1. CNN

5.2. Transfer Learning

6. RESULTS AND DISCUSSION

7. IDENTIFICATION

Deploying Deep Learning Model on the Google Cloud Platform For Disease Prediction

C.R. Aditya1,*, Chandra Sekhar Kolli2, Korla Swaroopa3, S. Hemavathi4 and Santosh Karajgi5

3.1. Brain Tumor Data

3.2. Image Resizing

3.3. Image Rescaling

3.4. Image Data Generator

3.5. Construct VGG-16

3.6. Train VGG-16

3.7. Validate VGG-16

3.8. Deploy in GCP

4. Results and Discussion

5. REAL-TIME IMPLEMENTATIONS.

CONCLUSION.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

9789815136531

9815136534

OCLC:

1379467895