Artificial Intelligence for the Internet of Health Things - Artificial Intelligence for the Internet of Health Things -

Artificial Intelligence (AI) in the Healthcare Domain Evolution of AI in the Medical Sector Use of AI Devices for Clinical Data Generation Types of AI of Relevance to Healthcare Natural Language Processing Rule-Based Expert Systems Physical Robots Robotic Process Automation AI-Based Applications in Healthcare Administrative Applications Implications for the Healthcare Workforce Ethical Implications Conclusion References Introduction IoT-Based Healthcare Framework Cloud Computing for Healthcare IoT-Based Healthcare Services and Applications IoT Healthcare Services The Internet of m-Health Things Adverse Drug Reactions Community Healthcare Children’s Health Information IoT Healthcare Applications Glucose Level Sensing Electrocardiogram Monitoring Blood Pressure Monitoring Body Temperature Monitoring Cost Analysis Continuous Monitoring Identification Mobility Components in IoT-Based Healthcare Services Wireless Technologies for IoT Web Technologies for IoT Conclusion References : An Extensive Overview of Wearable Technologies in the Healthcare Sector Introduction Background Information Quantified Self Wearable Technology Advantages of Wearable Technology Challenges Sustainability Digital Divide Failure Rates Lack of Predictive Comparability Privacy and Security Typical Wearable Devices With Applications Wearable Devices Used for General Health Management Wearable Biosensors Revolutionizing In-Clinic/Hospital Care Wearable Biosensors Revolutionizing Specific Fields of Healthcare Outside of the Hospital and Clinic Regulatory Oversight and Economic Impact Conclusion References : IoHT and Cloud-Based Disease Diagnosis Model Using Particle Swarm Optimization with Artificial Neural Networks Introduction The Proposed Model Data Collection PSO-ANN Model ANN Model Parameter Optimization of ANN Using PSO Proposed Diagnostic Model Performance Validation Conclusion References : IoHT-Based Improved Grey Optimization with Support Vector Machine for Gastrointestinal Hemorrhage Detection and Diagnosis Model Introduction Proposed Method Preprocessing Feature Extraction Process Using NGLCM NGLCM-IGWO-SVM–Based Classification Experimental Validation Data Set Used Results Analysis Conclusion References An Effective-Based Personalized Medicine Recommendation System Using an Ensemble of Extreme Learning Machine Model Introduction The Proposed Medical Recommender System Database System Module Data Preparation Module Recommendation Model Module Model Evaluation Module Proposed Recommendation Model Extreme Learning Machine (ELM)b-ELM Classifier Experimental Results and Discussion Conclusion References A Novel MapReduce- Based Hybrid Decision Tree with TFIDF Algorithm for Public Sentiment Mining of Diabetes Mellitus Introduction The Proposed Model Data Collection Data Preprocessing and Integration Data Tokenization Generating and Removing Stop Words Detecting Stop Words with SentiCircles Stemming and Lemmatization Corpus Generation Tagging Data Analysis Stage MPHDT-T-Based Opinion Mining Decision Tree (DT)Term Frequency-Inverse Document Frequency (TFIDF)Performance Validation Conclusion References : IoHT with Artificial Intelligence–Based Breast Cancer Diagnosis Model Introduction Related Works The Proposed Model Image Acquisition Preprocessing HFE Model Homogeneity Energy HOG Features GA-SVM-Based Classification Chromosome Design Fitness Function Hybridization of the GA-SVM Algorithm Performance Validation Conclusion References : Artificial Intelligence with a Cloud-Based Medical Image Retrieval System Using a Deep Neural Network Introduction The Proposed DC-DNN Model DLTerQEP-Based Texture Feature Extraction Crest Line–Based Shape Feature Extraction Curvature Approximation Crest Point Classification Crest Lines Euclidean Distance–Based Similarity DNN-Based Classification Performance Validation Data Set Used Results Analysis Conclusion References : IoHT with Cloud-Based Brain Tumor Detection Using Particle Swarm Optimization with Support Vector Machine Introduction The Proposed GLCM-PSO-SVM Model Preprocessing Feature Extraction PSO-SVM-Based Classification SVM Classifier Parameter Optimization of SVM Using the PSO Algorithm Experimental Analysis Data Set Description Evaluation Metrics Results Analysis Conclusion References : Artificial Intelligence-Based Hough Transform with an Adaptive Neuro-Fuzzy Inference System for a Diabetic Retinopathy Classification Model Introduction Proposed Method Preprocessing Watershed-Based Segmentation Hough Transform–Based Feature Extraction ANFIS-Based Classification Performance Analysis Results Analysis Data Set Description Conclusion References : An IoHT–Based Intelligent Skin Lesion Detection and Classification Model in Dermoscopic Images Introduction The SIFT-SVM Bilateral Filtering–Based Preprocessing Image Segmentation Feature Extraction Detect Scale-Space Extrema Localized Feature Points Assignment of Orientation Feature Point Descriptor Image Classification Performance Validation Data Set Used Results Analysis Conclusion References : An IoHT-Based Image Compression Model Using Modified Cuckoo Search Algorithm with Vector Quantization Introduction Vector Quantization and LBG Algorithm Proposed MCS-LBG Algorithm–Based VQ CS Algorithm MCS Algorithm Working Process of MCS-LBG Algorithm Performance Validation Conclusion References : An Effective Secure Medical Image Transmission Using Improved Particle Swarm Optimization and Wavelet Transform Introduction The IPSO-DWT Method Embedded Procedure Representation of the Stego Key Embedded Process Extraction of the Secret Message IPSO-Based Pixel Selection Process Performance Validation Conclusion References : IoHT with Wearable Devices–Based Feature Extraction and a Deep Neural Networks Classification Model for Heart Disease Diagnosis Introduction Proposed Model IoHT-Based Patient Data Collection IoHT Medical Data Preprocessing Heart Feature Extraction DNN for Data Classification Training of Layers The Stacked Autoencoder The Softmax Layer Performance Validation Conclusion References