Artificial Intelligence in Healthcare

Description

This book highlights the analytics and optimization issues in healthcare systems, proposes new approaches, and presents applications of innovative approaches in real facilities. In the past few decades, there has been an exponential rise in the application of swarm intelligence techniques for solving complex and intricate problems arising in healthcare. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. The primary objective of this book is to bring forward thorough, in-depth, and well-focused developments of hybrid variants of swarm intelligence algorithms and their applications in healthcare systems. Preface Contents About the Editors Geospatial Modelling and Trend Analysis of Coronavirus Outbreaks Using Sentiment Analysis and Intelligent Algorithms 1 Introduction 1.1 Rule-Based Approaches 1.2 Automated Approaches 1.3 Hybrid Approaches 1.4 Geo-Spatial Analysis 2 Literature Review 3 Methodology 3.1 Data Collection 3.2 Data Pre-Processing 3.3 Spatial Analysis 3.4 Sentiment Analysis 4 Results 5 Conclusion References A Particle Swarm Optimization Based ANN Predictive Model for Statistical Detection of COVID-19 1 Introduction 2 Related Work 3 Particle Swarm Optimization 4 Artificial Neural Network 5 Methodology 5.1 Dataset Description 5.2 Proposed Model 6 Result and Discussion 7 Conclusion References Identifying Malignancy of Lung Cancer Using Deep Learning Concepts 1 Introduction 2 Literature Survey 3 Experiment 3.1 Dataset 3.2 Implementation 3.3 Result 4 Conclusion References Protecting ECG Signals with Hybrid Swarm Intelligence Algorithm 1 Introduction 2 Related Work 3 Proposed Work 3.1 Hybrid Firefly Particle Swarm Optimization 3.2 QR Code 3.3 QR Decomposition 4 Embedding and Extraction 4.1 Embedding Process 4.2 Extraction Process 5 Results and Discussion 6 Conclusion References Human Eye Vision Algorithm (HEVA): A Novel Approach for the Optimization of Combinatorial Problems 1 Introduction 2 Related Work 2.1 Genetic Algorithm 2.2 Artificial Neural Network 2.3 Simulated Annealing 3 Proposed Optimization Algorithm 3.1 Proposed Algorithm 3.2 Framework for the Proposed Algorithm 3.3 Details of the Proposed Algorithm 3.4 Proposed Algorithm Pseudo-code 3.5 Illustration of Proposed Algorithm 3.6 Steps Followed in Proposed Algorithm 4 Result and Analysis 5 Conclusion References Analytics and Big Data in the Health Domain 1 Introduction 2 Theoretical Background 2.1 Role of Produced Data in Today’s Era 2.2 Data Intelligence and Computational Intelligence 3 Deep Learning 4 Application to Health Management 5 Autoencoders 6 Result Discussion and Conclusion References Pneumonia Prediction Using Swarm Intelligence Algorithms 1 Introduction 2 Methodology 2.1 Deep Convolutional Neural Networks 2.2 Convolutional Neural Networks 3 Experimental Work Pneumonia Dataset 4 Conclusion References Predictive Analysis in Health Care System Using AI 1 Introduction 2 Related Research 3 AI Framework in Healthcare 3.1 Data of Healthcare 3.2 AI Devices 3.3 Focus on Disease 3.4 Prevailing Tendency in Medical AI 3.5 Computer Based Intelligence Exceed Expectations At All Around Characterized Errands 3.6 Computer Based Intelligence is Supporting Specialists, Not Supplanting Them 3.7 Simulated Intelligence Bolsters Inadequately Resourced Administrations 3.8 Computer Based Intelligence is an Exceptionally Critical Eater 4 Applications of AI 4.1 Banking 4.2 Finance 4.3 Healthcare 4.4 Astrophysics 4.5 Gaming 4.6 Data Security 4.7 Robotics 4.8 System for Maintenance 5 Machine Learning and Deep Learning 6 Importance of AI 7 Conclusion and Future Scope References Applications of Swarm Intelligent and Deep Learning Algorithms for Image-Based Cancer Recognition 1 Introduction 2 Overview of Swarm Intelligent and Deep Learning Algorithms 2.1 Particle Swarm Optimization (PSO) 2.2 Genetic Algorithm (GA) 2.3 Ant Colony Optimization (ACO) 2.4 Convolutional Neural Networks (CNNs) 2.5 Fully Convolutional Neural Network (FCNs) 2.6 Auto-encoders Neural Networks 3 Cancer Recognition Using Hybrid Swarm Intelligent and Deep Learning Algorithms 3.1 Brain Cancer 3.2 Breast Cancer 3.3 Skin Cancer 3.4 Lung Cancer 3.5 Prostate Cancer 4 Conclusion References