Machine Intelligence for Internet of Medical Things : Applications and Future Trends / edited by Mariya Ouaissa [and four others].

Format:

Book

Author/Creator:

Ouaissa, Mariya, Author.

Contributor:

Ouaissa, Mariya, editor.

Series:

Computational Intelligence for Data Analysis Series

Computational Intelligence for Data Analysis Series ; Volume 2

Language:

English

Subjects (All):

Machine learning.

Medical informatics.

Physical Description:

1 online resource (288 pages)

Edition:

First edition.

Place of Publication:

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

Summary:

This book presents use-cases of IoT, AI and Machine Learning (ML) for healthcare delivery and medical devices. It compiles 15 topics that discuss the applications, opportunities, and future trends of machine intelligence in the medical domain. The objective of the book is to demonstrate how these technologies can be used to keep patients safe and healthy and, at the same time, to empower physicians to deliver superior care. Readers will be familiarized with core principles, algorithms, protocols, emerging trends, security problems, and the latest concepts in e-healthcare services. It also includes a quick overview of deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, practical methodology, and how they can be used to provide better solutions to healthcare related issues. The book is a timely update for basic and advanced readers in medicine, biomedical engineering, and computer science. Key topics covered in the book: - An introduction to the concept of the Internet of Medical Things (IoMT) - Cloud-edge based IoMT architecture and performance optimization in the context of Medical Big Data - A comprehensive survey on different IoMT interference mitigation techniques for Wireless Body Area Networks (WBANs) - Artificial Intelligence and the Internet of Medical Things - A review of new machine learning and AI solutions in different medical areas. - A Deep Learning based solution to optimize obstacle recognition for visually impaired patients - A survey of the latest breakthroughs in Brain-Computer Interfaces and their applications - Deep Learning for brain tumor detection - Blockchain and patient data management.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Foreword

Preface

OBJECTIVE OF THE BOOK

ORGANIZATION OF THE BOOK

List of Contributors

Internet of Medical Things &amp

Machine Intelligence

Health Services and Applications Powered by the Internet of Medical Things

Briska Jifrina Premnath1 and Namasivayam Nalini1,*

INTRODUCTION

CONCEPT FOR INTERNET-OF-THINGS-BASED HEALTHCARE

TECHNOLOGIES FOR HEALTHCARE SERVICE

Cloud Computing

Grid Computing

Big Data

Networks

Ambient Intelligence

Augmented Reality

Wearable

IOT'S HEALTHCARE BENEFITS

DIFFICULTIES IN IOMT

Confidentiality and Security of Data

Data Management

Scalability, Optimization, Regulation, and Standardization

Interoperability

Business Viability

Power Consumption

Environmental Consequences

SECURITY FOR THE INTERNET OF THINGS IN HEALTHCARE

Security Prerequisites

Security Challenges Memory Limitations

A Threat Model

Attack Types

Security Model Proposal

IOMT APPLICATIONS

Medical-Smart Technology

Ingestible Cameras

Monitoring of Patients in Real-Time is Number (RTPM)

System for Monitoring Cardiovascular Health

Skin Condition Monitoring Systems

Use of an IoMT Device as a Movement Detector

Wearable Sensors for Monitoring your Health from Afar

IOMT'S PART IN COVID-19

Technologies Collaborated with IoMT to Develop a Smart Healthcare System at COVID-19

Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)

CONCLUSION

CONSENT FOR PUBLICATON

CONFLICT OF INTEREST

ACKNOWLEDGEMENT

REFERENCES

An Approach to the Internet of Medical Things(IoMT): IoMT-Enabled Devices, Issues, andChallenges in Cybersecurity

Internet of Medical Things in Cloud Edge Computing

G. Sumathi1,*, S. Rajesh2, R. Ananthakumar2 and K. Kartheeban2.

INTRODUCTION

MEDICAL INTERNET OF THINGS

IOMT ARCHITECTURE

IOMT TECHNOLOGIES

Radio Frequency Identification (RFID)

Wireless Sensor Network (WSN)

MIDDLEWARE

IOMT IN CLOUD

IOMT CLOUD ARCHITECTURE

HEALTHCARE SERVICE LAYER

SERVICE-MANAGEMENT-LAYER

USER LAYER

IOMT CLOUD TECHNOLOGIES

Artificial Intelligence (AI)

IOMT CLOUD APPLICATIONS

IOMT EDGE CLOUD

IOMT EDGE-CLOUD ARCHITECTURE

Computational Offload

IOMT EDGE CLOUD APPLICATIONS

CONCLUSION &amp

FUTURE WORK

Survey of IoMT Interference Mitigation Techniques for Wireless Body Area Networks (WBANs)

Izaz Ahmad1, Muhammad Abul Hassan1,*, Inam Ullah Khan2 and Farhatullah3

Difference Between WBAN vs. WSN Concerning IoMT

WBAN ARCHITECTURE

WBAN APPLICATIONS

Rehabilitation and Therapy

Wearable Health Monitoring System

Disaster Aid Network

TECHNOLOGIES

Bluetooth

Low Energy Bluetooth

ZigBee

IEEE 802.11

IEEE 802.15.4

IEEE 802.15.6

TECHNIQUES AND COMPARISON

Artificial Intelligence-Based IoT Applications in Future Pandemics

Tarun Virmani1,*, Anjali Sharma2, Ashwani Sharma3, Girish Kumar3 and Meenu Bhati3

IOT AND AI IN HEALTH CARE

IOT AND AI: APPLICATIONS

AI AND IOT-ENABLED REMOTE SCREENING

Patients and IoT

IoT for Doctors

IoT in Hospitals

Diagnosis

MONITORING AND CONTROL OF EPIDEMIC VIA ML-BASED IOT

Drug Discovery and Vaccine Research

Applicability of AI-Enabled System

FUTURE PANDEMIC PREDICTION

ACKNOWLEDGEMENT.

REFERENCES

Cyber Secure AIoT Applications in Future Pandemics

Maria Nawaz Chohan1,* and Sana Nawaz Chohan2

LITERATURE STUDY

ARTIFICIAL INTERNET OF THINGS APPLICATIONS FOR HEALTHCARE

H-AIoT Based Hardware

H-AIoT Based Software

Communication/Routing Protocols

UAV's/Drones in the Healthcare Industry

Wearable AI-IoT Sensors

AI-IoT-Based Monitoring System

Detection of Cyber-Attacks in IoMT

Machine Learning Techniques for COVID-19

Industry 5.0 for Smart Healthcare Systems

Industry 5.0 Related Challenges

Using Flying Vehicles in Health Industry

Future Challenges

CONSENT FOR PUBLICATION

Machine Learning Solution for Orthopedics: A Comprehensive Review

Muhammad Imad1,*, Muhammad Abul Hassan1, Shah Hussain Bangash1 and Naimullah1

LITERATURE REVIEW

METHODOLOGY

A Review of Machine Learning Approaches for Identification of Health-Related Diseases

Muhammad Yaseen Ayub1,*, Farman Ali Khan1, Syeda Zillay Nain Zukhraf2 and Muhammad Hamza Akhlaq3

Supervised Learning

Unsupervised Learning

MOTIVATION

Heart Diseases Detection

Lung Diseases Detection

Skin Disease Detection

Brain Diseases Detection

Liver Diseases Detection

ALGORITHMS EXPLOITED FOR VARIOUS DISEASES DETECTION

TOOLS AND LIBRARIES USED FOR DISEASE DETECTION

CONCLUSION AND FUTURE TRENDS

Machine Learning in Detection of Disease: Solutions and Open Challenges

Tayyab Rehman1, Noshina Tariq1, Ahthasham Sajid2,* and Muhammad Hamza Akhlaq3

MACHINE LEARNING APPROACHES.

Supervised Learning (SL)

Reinforcement Learning (RL)

Data Mining (DM)

DETECTION OF DISEASE BY USING DIFFERENT MACHINE-LEARNING CLASSIFICATION

CHRONIC DISEASE: DETECTION OF HEART DISEASE

Naive Bayes (NB)

Issues and Challenges

CHRONIC DISEASE: DETECTION OF DISEASE BREAST CANCER

CAD System

Deep Learning

Machine-Learning Techniques

Convolutional Neural Network Model (CNN)

Logistic Regression (LR)

Random Forest Classifier (RFC)

Gradient Boosted Trees (GBT)

Weighted Ensemble Model (WEM)

CHRONIC DISEASE: DETECTION OF LIVER DISEASE

Data Selection and Pre-Processing

Feature Selection

Classification Algorithm

Supervised Learning and Unsupervised Learning

Performance Metrics Analysis

Predicted Results

SEASONAL DISEASE: DETECTION OF DENGUE DISEASE

SEASONAL DISEASE: DETECTION OF COVID-19 DISEASE

Breakthrough in Management of Cardiovascular Diseases by Artificial Intelligence in Healthcare Settings

Lakshmi Narasimha Gunturu1,*, Girirajasekhar Dornadula2 and Raghavendra Naveen Nimbagal3

MATERIALS AND METHODS

ALGORITHMS USED IN CARDIOVASCULAR DISEASES

K-Nearest Neighbour (KNN)

Artificial Neural Network (ANN)

Decision Tree (DT)

AdaBoost (AB)

Support Vector Machine (SVM)

RESULTS AND DISCUSSION

Impact of AI on Echocardiography (ECG)

Role of AI on Magnetic Resonance Imaging (MRI)

Use of AI on Cardiac Computed Tomography (CT)

Impact of AI on Electrocardiography

CHALLENGES

REFERENCES.

Smart Cane: Obstacle Recognition for Visually Impaired People Based on Convolutional Neural Network

Adnan Hussain1, Bilal Ahmad1 and Muhammad Imad2,*

Dataset Description

Methods

Ultrasonic Sensors

Visual Sensor

Buzzer Sensor

Jumper Wires

Breadboard

Bus Strip

Socket Strip

Power Bank

Earphone/Speaker

Traditional Cane

Smart/Modern Cane

Proposed Device Architecture

Deep Convolutional Neural Network

EXPERIMENTAL RESULTS ANALYSIS

A Survey on Brain-Computer Interface and Related Applications

Krishna Pai1,*, Rakhee Kallimani1, Sridhar Iyer1, B. Uma Maheswari2, Rajashri Khanai1 and Dattaprasad Torse2

RELATED WORKS

APPLICATIONS OF BCI

Data Augmentation with Image Fusion Techniques for Brain Tumor Classification using Deep Learning

Tarik Hajji1,*, Ibtissam Elhassani1, Tawfik Masrour1, Imane Tailouloute1 and Mouad Dourhmi1

BACKGROUND

Data Augmentation

Image Fusion

Related Work

Dataset

Deep Learning Approach with Classical Data Augmentation

Data Pre-Processing for the Model

Generation of many Manipulated Images from a Directory

Design of the Model Architecture

Convolution Layer

Pooling Layer

Flatten Layer

Dense Layer

Learning and Same Parameters

Data Augmentation: A Comparative Study

Data Augmentation with Image Fusion

Auto-Encoder Architecture

CNN Result without Data Augmentation

CNN Result with Data Augmentation Automatic Generator

CNN Result-Based DA using IF with BWT.

CNN Result-Based DA using IF with Auto-Encoder Proposed Approach.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

981-5080-44-X

OCLC:

1382694540