AI and IoT-based Intelligent Health Care & Sanitation

Description:

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.

Preface

The book aims to provide a deeper understanding of the relevant aspects of AI and IoT impacting each other’s efficacy for better output. Readers may discover a reliable and accessible one-stop resource; An introduction to Artificial Intelligence presents the first full examination of applications of AI, as well as IoT presents the smart objects, sensors or actuators using a secure protocol to the Artificial Intelligence approaches. They are designed in a way to provide an understanding of the foundations of artificial intelligence. It examines Education powered by AI, Entertainment and Artificial Intelligence, Home and Service Robots, Healthcare re-imagined, Predictive Policing, Space Exploration with AI, and weaponry in the world of AI. Through the volume, the authors provide detailed, wellillustrated treatments of each topic with abundant examples and exercises.

KEY FEATURES
● This book contains different topics about the most important areas and challenges in the Internet of Things. In this book, you will be able to read about the different subparts that compose the Internet of Things and the different ways to create a better IoT network or platform.
● This book will contain the possible architecture and middleware that you can use to create a platform and how to manage the data that you obtain from different objects (Smart Objects, sensors, or actuators) using a secure protocol or messages when you send the data through insecure protocols.
● These improvements can be applied in different ways. We can use different technologies and create different applications, or we can use the research of other fields like Big Data to process the massive data of the devices, Artificial Intelligence to create smarter objects or algorithms, Model-Driven Engineering to facilitate the use of any people, Cloud Computing to send the computation and the applications to the cloud, and Cloud Robotics to manage and interconnect the Robots between themselves and with
other objects.

Table of 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
1. INTRODUCTION
2. LITERATURE SURVEY
3. PROPOSED SYSTEM
4. ARTIFICIAL NEURAL NETWORK
5. IMPLEMENTATION
6. RESULT AND DISCUSSION
7. STATE OF ART
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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
1. INTRODUCTION
2. LITERATURE SURVEY
3. MATERIALS AND METHODS
4. DATASET DESCRIPTION
5. ARTIFICIAL NEURAL NETWORK
6. IMPLEMENTATION
7. RESULT AND DISCUSSION
8. NOVELTY OF THE STUDY
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Innovative IoT-Based Wearable Sensors for the Prediction & 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,*
1. INTRODUCTION
2. LITERATURE SURVEY
3. PROPOSED SYSTEM
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
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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,*
1. INTRODUCTION
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
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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
1. INTRODUCTION
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
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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,*
1. INTRODUCTION
2. LITERATURE SURVEY
3. MATERIALS AND METHODS
4. PREPROCESSING TECHNIQUES
5. DL MODEL
5.1. Transfer Learning (VGG-19)
5.2. CNN
6. RESULT ANALYSIS
7. DISCUSSION
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Texture Analysis-based Features Extraction & Classification of Lung Cancer Using Machine Learning
Korla Swaroopa1,*, N. Chaitanya Kumar2, Christopher Francis Britto3, M. Malathi4, Karthika Ganesan5 and Sachin Kumar6
1. INTRODUCTION
2. METHODOLOGY
3. FE
3.1. GLCM
3.2. GLRM
4. CLASSIFICATION METHODS
4.1. SVM
4.2. KNN
5. RESULTS AND DISCUSSION
6. GAPS FILLED
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Implementation of the Deep Learning-based Website For Pneumonia Detection & Classification
V. Vedanarayanan1, Nagaraj G. Cholli2, Merin Meleet2, Bharat Maurya3, G. Appasami4 and Madhu Khurana5,*
1. INTRODUCTION
2. MATERIALS AND METHODS
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
6. RESULT AND DISCUSSION
7. STATE OF ART
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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
1. INTRODUCTION
2. METHODOLOGY
3. PREPROCESSING
4. PREDICTION METHODS
5. DEPLOYMENT
6. RESULT
7. DISCUSSION
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Feature Extraction and Diagnosis of Dementia using Magnetic Resonance Imaging
Praveen Gupta1,*, Nagendra Kumar2, Ajad2, N. Arulkumar3 and Muthukumar Subramanian4
1. INTRODUCTION
2. MATERIALS AND METHODS
3. FE TECHNIQUES
3.1. GLCM
3.2. GLRM
4. CLASSIFICATION TECHNIQUES
4.1. Support Vector Machine (SVM)
4.2. K-Nearest Neighbor (KNN)
4.3. Random Forest (RF)
4.4. Proposed Method
5. RESULT AND DISCUSSION
6. PROPOSED IDEA
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Deep Learning-Based Regulation of Healthcare Efficiency and Medical Services
T. Vamshi Mohana1,*, Mrunalini U. Buradkar2, Kamal Alaskar3, Tariq Hussain Sheikh4 and Makhan Kumbhkar5
1. INTRODUCTION
2. IOT IN MEDICAL CARE SERVICES
3. RELATED WORKS
4. PROPOSED SYSTEM
4.1. Interaction of RNN with LSTM
5. RESULTS AND DISCUSSION
6. NOVELTY OF THE PROPOSED WORK
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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. INTRODUCTION
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
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Deep Learning Based Object Detection Using Mask R-CNN
Vinit Gupta1,*, Aditya Mandloi1, Santosh Pawar2, T.V Aravinda3 and K.R Krishnareddy3
1. INTRODUCTION
2. LITERATURE SURVEY
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
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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
1. INTRODUCTION
1.1. Literature Survey
2. METHODOLOGY
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. RESULT AND DISCUSSION
5.1. CNN Performance Measure
5.2. CNN- LSTM
6. NOVELTY
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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
1. INTRODUCTION
2. LITERATURE SURVEY
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
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
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
1. INTRODUCTION
2. LITERATURE SURVEY
3. METHODOLOGIES
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
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Classification and Diagnosis of Alzheimer’s Disease using Magnetic Resonance Imaging
K.R. Shobha1,*, Vaishali Gajendra Shende2, Anuradha Patil2, Jagadeesh Kumar Ega3 and Kaushalendra Kumar4
1. INTRODUCTION
2. METHODS AND MATERIALS
3. PREPROCESSING TECHNIQUES
3.1. Image Resizing
3.2. Smoothing using the Gaussian Method
4. FEATURE EXTRACTION
4.1. GLCM
4.2. HAAR
5. CLASSIFICATION TECHNIQUES
5.1. SVM
5.2. LDA
6. RESULT COMPARISON
7. DISCUSSION
CONCLUSION
CONSENT FOR PUBLICATON
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Subject Index