(Ebook PDF) Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems 1st edition by Parvathaneni Naga Srinivasu 1681089556 9781681089553 full chapters

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ISBN-10 ‏ : ‎1681089556 

ISBN-13 ‏ : ‎ 9781681089553

Author :  Parvathaneni Naga Srinivasu 

Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems explains the emerging technology that currently drives computer-aided diagnosis, medical analysis and other electronic healthcare systems. 11 book chapters cover advances in biomedical engineering fields achieved through deep learning and soft-computing techniques. Readers are given a fresh perspective of how intelligent systems impact patient outcomes for healthcare professionals who are assisted by advanced computing algorithms. Key Features: – Covers emerging technologies in biomedical engineering and healthcare that assist physicians in diagnosis, treatment, and surgical planning in a multidisciplinary context – Provides examples of technical use cases for artificial intelligence, machine learning and deep learning in medicine, with examples of different algorithms – Introduces readers to the concept of telemedicine and electronic healthcare systems – Provides implementations of disease prediction models for different diseases including cardiovascular diseases, diabetes and Alzheimer’s disease – Summarizes key information for learners – Includes references for advanced readers The book serves as an essential reference for academic readers, as well as computer science enthusiasts who want to familiarize themselves with the practical computing techniques in the field of biomedical engineering (with a focus on medical imaging) and medical informatics.

Computational Intelligence and Machine Learning Approaches in Biomedical Engineering and Health Care Systems 1st Table of contents:

Convolutional Neural Network for Denoising Left Ventricle Magnetic Resonance Images
Zakarya Farea Shaaf1, Muhammad Mahadi Abdul Jamil1,*, Radzi Ambar1 and Mohd Helmy Abd Wahab1
INTRODUCTION
DENOISING MRI IMAGES USING CONVOLUTIONAL NEURAL NETWORKS
CONVOLUTIONAL NEURAL NETWORK FOR DENOISING
Building Blocks of Convolutional Neural Network
Network Architecture
Implementation Platform
Data Description
RESULT AND DISCUSSION
Quantitative Measurements
Confusion Matrix
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Early Diabetic Retinopathy Detection Using Elevated Continuous Particle Swarm Optimization Clusterin
Bhimavarapu Usharani1,*
INTRODUCTION
Heart
Kidney
Eye
BACKGROUND
Particle Swarm Optimization Clustering
Particle Swarm Optimization Clustering Algorithm
Raspberry PI
LITERATURE REVIEW
PROPOSED MODEL
Pre-processing
Segmentation
Elevated Continuous Particle Swarm Optimization Clustering
Fitness Measures
Feature Extraction
RESULTS AND DISCUSSION
Results
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
E-Health System and Telemedicine: An Overview and its Applications in Health Care and Medicine
Ranjitha Vijay Anand1, Harshavardhini Parthiban1, Karthikeyan Subbiahanadar Chelladurai1, Jackson Du
INTRODUCTION
E-HEALTH
ELECTRONIC HEALTH SYSTEM
Health Care Informatics or Health Information Technology (HIT)
Electronic Health Records (EHR)
Medical Information System
Biomedical Informatics
COMPONENCE OF E-HEALTH
TELEMEDICINE
OTHER FUNCTIONAL SYSTEMS OF E-HEALTH SYSTEM[Clinical ICT System]Clinical ICT System
Integrated Healthcare System
Health IT Support System
Online Health Information System
Public Health Data Collection and Analysis
Consumer Health Informatics Applications
Telemedicine in Developing Countries
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Fuzzy Logic Implementation in Patient Monitoring System for Lymphatic Treatment of Leg Pain
Fauziah Abdul Wahid1, Noor Anita Khairi2, Siti Aishah Muhammed Suzuki1,*, Rafidah Hanim Mokhtar1, No
INTRODUCTION
LITERATURE REVIEW
Definition of Computational Intelligence
Definition of Lymphatic System
Lymphatic Treatment for Leg Pain
Health Information Management System
Security Issues Related to Data Breach
Introduction to Double-Loop Learning
Characteristics of Double-Loop Learning
The Approach of Double-Loop in Patient Monitoring System
Fuzzy Logic
Application of Fuzzy Logic in Patient Monitoring System
THE PROPOSED APPROACH
Agile Model
Agile SDLC Implementation in Double-Loop
Patient Monitoring System with Fuzzy Logic Implementation
Basic Algorithm Function (Fuzzy Logic)
Fuzzy Logic Architecture
A. Fuzzification Module
B. Knowledge-based
C. Inference Engine
D. Defuzzification Module
Development of Patient Monitoring System
A. Define Variables
B. Construct Membership Function
C. Construct the Base Rules
D. Fuzzification
E. Defuzzification
ACCURACY OF PROPOSED MODEL
Software and Hardware Requirements
Security Implementation in Health Monitoring System
Digital Signature
No Right-Click Script
CONCLUSION AND FUTURE WORKS
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Safe Distance and Face Mask Detection using OpenCV and MobileNetV2
B.S. Maya1,*, T. Asha1, P. Prajwal1, P.N. Revanth1, Pratik R Pailwan1 and Rahul Kumar Gupta1
INTRODUCTION
LITERATURE REVIEW
DATASET DESCRIPTION
SYSTEM ARCHITECTURE
Applying the Face Mask and Safe Distance Detector
METHODOLOGY ON FACE-MASK-NET MODEL
Details of Facemasknet Detection Model
Convolutional Neural Network
MobileNetV2
Learning Rate
SOCIAL DISTANCING MONITORING
Distance Measurement using OpenCV
Social Distance Based on YOLO-v3
RESULTS AND DISCUSSION
CONCLUSION
FUTURE SCOPE
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Performance Evaluation of ML Algorithms for Disease Prediction Using DWT and EMD Techniques
Reddy K. Viswavardhan1,*, B. Hemapriya1, B. Roja Reddy1 and B.S. Premananda1
INTRODUCTION
Discrete Wavelet Transformation
Empirical Mode Decomposition
Kth-Nearest Neighbor
Decision Tree Classifier
Random Forest
Support Vector Machine
Stochastic Gradient Descent (SGD)
Multi-level Perception
Performance Evaluation
Accuracy
Precision Score
Recall Score
F1. Score
RESULTS
Feature Extraction using DWT and EMD
ML Algorithms for disease prediction using DWT and EMD tech, CI and M, approaches in the field of BM
ML Algorithms for Disease Prediction Using DWT and EMD Tech, CI and ML, Approaches in the Field of B
CI and ML approaches in the field of BM and HC Viswavardhan et al.
ML Algorithms for Disease Prediction Using DWT and EMD Tech, CI and ML, Approaches in the Field of B
CONCLUDING REMARKS
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Cardiovascular Disease Preventive Prediction and Medication (CVDPPM) – A Model Based on AI Technique
Y.V. Nagesh Meesala1,*, Sheik Khadar Ahmad Manoj1 and Ganapati Bhavana2
INTRODUCTION
LITERATURE SURVEY
IoT Based CVD
Cloud Storage of Health Records
CARDIOVASCULAR DISEASE PREVENTIVE PREDICTION AND MEDICATION (CVDPPM) MODEL
IMPLEMENTATION OF ML ALGORITHMS FOR PREDICTION OF CVD
Data Set Predictors:
Information by positive and negative heart disease patients are given in Tables 3 and 4.
Model 1: Logistic Regression
Model 2: K-NN (K-Nearest Neighbors)
Model 3: SVM (Support Vector Machine)
Model 4: Naive Bayes Classifier
Model 5: Decision Trees
Model 6: Random Forests
Model 7: XGBoost
Making the Confusion Matrix
Instructions to Deduce Information from the Confusion Matrix
Highlight Importance
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Personalized Smart Diabetic System Using Hybrid Models of Neural Network Algorithms
K. Abirami1, P. Deepalakshmi2,* and Shanmuk Srinivas Amiripalli3
INTRODUCTION
CHALLENGES IN HEALTHCARE
LITERATURE SURVEY
HYBRID NEURAL NETWORK APPROACH
Dataset Collection
Data Processing
Personalized Analysis Layer
Common Users
Healthcare Providers
Diabetics Patients
Data Sharing Layer
RESULTS AND DISCUSSION
CONCLUSION AND FUTURE SCOPE
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
A Framework of Smart Mobile Application for Vehicle Health Monitoring
K. Aswarth1 and S. Vasavi1,*
INTRODUCTION
LITERATURE SURVEY
Challenges in VMMS Architecture System
Challenges in the DTC Coding System
Challenges in Automotive Diagnostic Command Set
Challenges in Digital Twin Model
APPROACH
Architecture
Methodology
Cloud Application Deployment
Cloud Application
Identity and Access Management
RESULTS AND ANALYSIS
DATASET DESCRIPTION
SENSOR PREDICTED
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Progression Prediction and Classification of Alzheimer’s Disease using MRI
Sruthi Mohan1,* and S. Naganandhini2
INTRODUCTION
REVIEW OF THE LITERATURE
METHODOLOGY OF PPC-AD-MRI
Random Forest Classifier (RFC)
Algorithm for Random Forest
K Nearest Neighbor (KNN) Classifier
Extreme Gradient Boosting (XGB)
RESULTS AND DISCUSSION
Dataset Description
Performance Evaluation Matrices
CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES

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