{"product_id":"emerging-technologies-for-healthcare-internet-of-things-and-deep-learning-models-hardback-9781119791720","title":"Emerging Technologies for Healthcare; Internet of Things and Deep Learning Models (Hardback) 9781119791720","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eEmerging Technologies for Healthcare\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eInternet of Things and Deep Learning Models\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMonika Mangla (Edited by), M Mangla (Author), Nonita Sharma (Edited by), Poonam Garg (Edited by), Vaishali Wadhwa (Edited by), Thirunavukkarasu K. (Edited by), Shahnawaz Khan (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119791720, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 17 September 2021\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e?Emerging Technologies for Healthcare? beginnt mit einer IoT-basierten Lösung für die Automatisierung im Gesundheitssektor, wodurch Verfahren auf Grundlage von fortschrittlichen Deep-Learning-Techniken ermöglicht werden.\u003cbr\u003e \u003cbr\u003e Praktische Lösungen, die auf verschiedenen Ansätzen des maschinellen Lernens beruhen, werden vorgestellt und auf die Analyse und Vorhersage von Krankheiten angewandt. Ein Beispiel ist die Nutzung einer dreidimensionalen Matrix für die Behandlung chronischer Nierenerkrankungen, die Diagnose und Prognose des erworbenen demyelinisierenden Syndroms und von Autismus-Spektrum-Störungen sowie die Erkennung von Lungenentzündungen. Außerdem werden verschiedene geeignete Ansätze vorgestellt, wie die Gesundheitssysteme mit COVID-19-Fällen umgehen können. Daneben wird ein detaillierter Erkennungsmechanismus dargelegt, mit dessen Hilfe Lösungen entwickelt werden können, um von der Handschrift auf die Persönlichkeit zu schließen, und es werden neuartige Ansätze für die Stimmungsanalyse aufgezeigt, die mit ausreichenden Daten und verschiedenen Betrachtungsweisen untermauert sind.\u003cbr\u003e \u003cbr\u003e Dieses Buch enthält nicht nur theoretische Ansätze und Algorithmen, sondern zeigt auch auf, welche Schritte bei der Problemanalyse mithilfe von Daten, Prozessen, Berichten und Optimierungstechniken durchlaufen werden. Es ist ein umfassendes Nachschlagewerk für die Lösung verschiedener Probleme anhand von Algorithmen für das maschinelle Lernen.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Basics of Smart Healthcare 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 An Overview of IoT in Health Sectors 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSheeba P. S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Influence of IoT in Healthcare Systems 6\u003c\/p\u003e \u003cp\u003e1.2.1 Health Monitoring 6\u003c\/p\u003e \u003cp\u003e1.2.2 Smart Hospitals 7\u003c\/p\u003e \u003cp\u003e1.2.3 Tracking Patients 7\u003c\/p\u003e \u003cp\u003e1.2.4 Transparent Insurance Claims 8\u003c\/p\u003e \u003cp\u003e1.2.5 Healthier Cities 8\u003c\/p\u003e \u003cp\u003e1.2.6 Research in Health Sector 8\u003c\/p\u003e \u003cp\u003e1.3 Popular IoT Healthcare Devices 9\u003c\/p\u003e \u003cp\u003e1.3.1 Hearables 9\u003c\/p\u003e \u003cp\u003e1.3.2 Moodables 9\u003c\/p\u003e \u003cp\u003e1.3.3 Ingestible Sensors 9\u003c\/p\u003e \u003cp\u003e1.3.4 Computer Vision 10\u003c\/p\u003e \u003cp\u003e1.3.5 Charting in Healthcare 10\u003c\/p\u003e \u003cp\u003e1.4 Benefits of IoT 10\u003c\/p\u003e \u003cp\u003e1.4.1 Reduction in Cost 10\u003c\/p\u003e \u003cp\u003e1.4.2 Quick Diagnosis and Improved Treatment 10\u003c\/p\u003e \u003cp\u003e1.4.3 Management of Equipment and Medicines 11\u003c\/p\u003e \u003cp\u003e1.4.4 Error Reduction 11\u003c\/p\u003e \u003cp\u003e1.4.5 Data Assortment and Analysis 11\u003c\/p\u003e \u003cp\u003e1.4.6 Tracking and Alerts 11\u003c\/p\u003e \u003cp\u003e1.4.7 Remote Medical Assistance 11\u003c\/p\u003e \u003cp\u003e1.5 Challenges of IoT 12\u003c\/p\u003e \u003cp\u003e1.5.1 Privacy and Data Security 12\u003c\/p\u003e \u003cp\u003e1.5.2 Multiple Devices and Protocols Integration 12\u003c\/p\u003e \u003cp\u003e1.5.3 Huge Data and Accuracy 12\u003c\/p\u003e \u003cp\u003e1.5.4 Underdeveloped 12\u003c\/p\u003e \u003cp\u003e1.5.5 Updating the Software Regularly 12\u003c\/p\u003e \u003cp\u003e1.5.6 Global Healthcare Regulations 13\u003c\/p\u003e \u003cp\u003e1.5.7 Cost 13\u003c\/p\u003e \u003cp\u003e1.6 Disadvantages of IoT 13\u003c\/p\u003e \u003cp\u003e1.6.1 Privacy 13\u003c\/p\u003e \u003cp\u003e1.6.2 Access by Unauthorized Persons 13\u003c\/p\u003e \u003cp\u003e1.7 Applications of IoT 13\u003c\/p\u003e \u003cp\u003e1.7.1 Monitoring of Patients Remotely 13\u003c\/p\u003e \u003cp\u003e1.7.2 Management of Hospital Operations 14\u003c\/p\u003e \u003cp\u003e1.7.3 Monitoring of Glucose 14\u003c\/p\u003e \u003cp\u003e1.7.4 Sensor Connected Inhaler 15\u003c\/p\u003e \u003cp\u003e1.7.5 Interoperability 15\u003c\/p\u003e \u003cp\u003e1.7.6 Connected Contact Lens 15\u003c\/p\u003e \u003cp\u003e1.7.7 Hearing Aid 16\u003c\/p\u003e \u003cp\u003e1.7.8 Coagulation of Blood 16\u003c\/p\u003e \u003cp\u003e1.7.9 Depression Detection 16\u003c\/p\u003e \u003cp\u003e1.7.10 Detection of Cancer 17\u003c\/p\u003e \u003cp\u003e1.7.11 Monitoring Parkinson Patient 17\u003c\/p\u003e \u003cp\u003e1.7.12 Ingestible Sensors 18\u003c\/p\u003e \u003cp\u003e1.7.13 Surgery by Robotic Devices 18\u003c\/p\u003e \u003cp\u003e1.7.14 Hand Sanitizing 18\u003c\/p\u003e \u003cp\u003e1.7.15 Efficient Drug Management 19\u003c\/p\u003e \u003cp\u003e1.7.16 Smart Sole 19\u003c\/p\u003e \u003cp\u003e1.7.17 Body Scanning 19\u003c\/p\u003e \u003cp\u003e1.7.18 Medical Waste Management 20\u003c\/p\u003e \u003cp\u003e1.7.19 Monitoring the Heart Rate 20\u003c\/p\u003e \u003cp\u003e1.7.20 Robot Nurse 20\u003c\/p\u003e \u003cp\u003e1.8 Global Smart Healthcare Market 21\u003c\/p\u003e \u003cp\u003e1.9 Recent Trends and Discussions 22\u003c\/p\u003e \u003cp\u003e1.10 Conclusion 23\u003c\/p\u003e \u003cp\u003eReferences 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 IoT-Based Solutions for Smart Healthcare 25\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePankaj Jain, Sonia F Panesar, Bableen Flora Talwar and Mahesh Kumar Sah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 26\u003c\/p\u003e \u003cp\u003e2.1.1 Process Flow of Smart Healthcare System 26\u003c\/p\u003e \u003cp\u003e2.1.1.1 Data Source 26\u003c\/p\u003e \u003cp\u003e2.1.1.2 Data Acquisition 27\u003c\/p\u003e \u003cp\u003e2.1.1.3 Data Pre-Processing 27\u003c\/p\u003e \u003cp\u003e2.1.1.4 Data Segmentation 28\u003c\/p\u003e \u003cp\u003e2.1.1.5 Feature Extraction 28\u003c\/p\u003e \u003cp\u003e2.1.1.6 Data Analytics 28\u003c\/p\u003e \u003cp\u003e2.2 IoT Smart Healthcare System 29\u003c\/p\u003e \u003cp\u003e2.2.1 System Architecture 30\u003c\/p\u003e \u003cp\u003e2.2.1.1 Stage 1: Perception Layer 30\u003c\/p\u003e \u003cp\u003e2.2.1.2 Stage 2: Network Layer 32\u003c\/p\u003e \u003cp\u003e2.2.1.3 Stage 3: Data Processing Layer 32\u003c\/p\u003e \u003cp\u003e2.2.1.4 Stage 4: Application Layer 33\u003c\/p\u003e \u003cp\u003e2.3 Locally and Cloud-Based IoT Architecture 33\u003c\/p\u003e \u003cp\u003e2.3.1 System Architecture 33\u003c\/p\u003e \u003cp\u003e2.3.1.1 Body Area Network (BAN) 34\u003c\/p\u003e \u003cp\u003e2.3.1.2 Smart Server 34\u003c\/p\u003e \u003cp\u003e2.3.1.3 Care Unit 35\u003c\/p\u003e \u003cp\u003e2.4 Cloud Computing 35\u003c\/p\u003e \u003cp\u003e2.4.1 Infrastructure as a Service (IaaS) 37\u003c\/p\u003e \u003cp\u003e2.4.2 Platform as a Service (PaaS) 37\u003c\/p\u003e \u003cp\u003e2.4.3 Software as a Service (SaaS) 37\u003c\/p\u003e \u003cp\u003e2.4.4 Types of Cloud Computing 37\u003c\/p\u003e \u003cp\u003e2.4.4.1 Public Cloud 37\u003c\/p\u003e \u003cp\u003e2.4.4.2 Private Cloud 38\u003c\/p\u003e \u003cp\u003e2.4.4.3 Hybrid Cloud 38\u003c\/p\u003e \u003cp\u003e2.4.4.4 Community Cloud 38\u003c\/p\u003e \u003cp\u003e2.5 Outbreak of Arduino Board 38\u003c\/p\u003e \u003cp\u003e2.6 Applications of Smart Healthcare System 39\u003c\/p\u003e \u003cp\u003e2.6.1 Disease Diagnosis and Treatment 41\u003c\/p\u003e \u003cp\u003e2.6.2 Health Risk Monitoring 42\u003c\/p\u003e \u003cp\u003e2.6.3 Voice Assistants 42\u003c\/p\u003e \u003cp\u003e2.6.4 Smart Hospital 42\u003c\/p\u003e \u003cp\u003e2.6.5 Assist in Research and Development 43\u003c\/p\u003e \u003cp\u003e2.7 Smart Wearables and Apps 43\u003c\/p\u003e \u003cp\u003e2.8 Deep Learning in Biomedical 44\u003c\/p\u003e \u003cp\u003e2.8.1 Deep Learning 46\u003c\/p\u003e \u003cp\u003e2.8.2 Deep Neural Network Architecture 47\u003c\/p\u003e \u003cp\u003e2.8.3 Deep Learning in Bioinformatic 49\u003c\/p\u003e \u003cp\u003e2.8.4 Deep Learning in Bioimaging 49\u003c\/p\u003e \u003cp\u003e2.8.5 Deep Learning in Medical Imaging 50\u003c\/p\u003e \u003cp\u003e2.8.6 Deep Learning in Human-Machine Interface 53\u003c\/p\u003e \u003cp\u003e2.8.7 Deep Learning in Health Service Management 53\u003c\/p\u003e \u003cp\u003e2.9 Conclusion 55\u003c\/p\u003e \u003cp\u003eReferences 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning 69\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVinayak Bharadi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 70\u003c\/p\u003e \u003cp\u003e3.1.1 Machine Learning Models 70\u003c\/p\u003e \u003cp\u003e3.2 Machine Learning Model Lifecycle 71\u003c\/p\u003e \u003cp\u003e3.2.1 Steps in Machine Learning Lifecycle 71\u003c\/p\u003e \u003cp\u003e3.2.1.1 Data Preparation 72\u003c\/p\u003e \u003cp\u003e3.2.1.2 Building the Machine Learning Model 72\u003c\/p\u003e \u003cp\u003e3.2.1.3 Model Training 72\u003c\/p\u003e \u003cp\u003e3.2.1.4 Parameter Selection 72\u003c\/p\u003e \u003cp\u003e3.2.1.5 Transfer Learning 73\u003c\/p\u003e \u003cp\u003e3.2.1.6 Model Verification 73\u003c\/p\u003e \u003cp\u003e3.2.1.7 Model Deployment 74\u003c\/p\u003e \u003cp\u003e3.2.1.8 Monitoring 74\u003c\/p\u003e \u003cp\u003e3.3 A Model Deployment in Keras 75\u003c\/p\u003e \u003cp\u003e3.3.1 Pima Indian Diabetes Dataset 75\u003c\/p\u003e \u003cp\u003e3.3.2 Multi-Layered Perceptron Implementation in Keras 76\u003c\/p\u003e \u003cp\u003e3.3.3 Multi-Layered Perceptron Implementation With Dropout and Added Noise 77\u003c\/p\u003e \u003cp\u003e3.4 QLattice Environment 80\u003c\/p\u003e \u003cp\u003e3.4.1 Feyn Models 80\u003c\/p\u003e \u003cp\u003e3.4.1.1 Semantic Types 82\u003c\/p\u003e \u003cp\u003e3.4.1.2 Interactions 83\u003c\/p\u003e \u003cp\u003e3.4.1.3 Generating QLattice 83\u003c\/p\u003e \u003cp\u003e3.4.2 QLattice Workflow 83\u003c\/p\u003e \u003cp\u003e3.4.2.1 Preparing the Data 84\u003c\/p\u003e \u003cp\u003e3.4.2.2 Connecting to QLattice 84\u003c\/p\u003e \u003cp\u003e3.4.2.3 Generating QGraphs 84\u003c\/p\u003e \u003cp\u003e3.4.2.4 Fitting, Sorting, and Updating QGraphs 85\u003c\/p\u003e \u003cp\u003e3.4.2.5 Model Evaluation 86\u003c\/p\u003e \u003cp\u003e3.5 Using QLattice Environment and QGraph Models for COVID-19 Impact Prediction 87\u003c\/p\u003e \u003cp\u003eReferences 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Sensitive Healthcare Data: Privacy and Security Issues and Proposed Solutions 93\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAbhishek Vyas, Satheesh Abimannan and Ren-Hung Hwang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 94\u003c\/p\u003e \u003cp\u003e4.1.1 Types of Technologies Used in Healthcare Industry 94\u003c\/p\u003e \u003cp\u003e4.1.2 Technical Differences Between Security and Privacy 95\u003c\/p\u003e \u003cp\u003e4.1.3 HIPAA Compliance 95\u003c\/p\u003e \u003cp\u003e4.2 Medical Sensor Networks\/Medical Internet of Things\/Body Area Networks\/WBANs 97\u003c\/p\u003e \u003cp\u003e4.2.1 Security and Privacy Issues in WBANs\/WMSNs\/WMIOTs 101\u003c\/p\u003e \u003cp\u003e4.3 Cloud Storage and Computing on Sensitive Healthcare Data 112\u003c\/p\u003e \u003cp\u003e4.3.1 Security and Privacy in Cloud Computing and Storage for Sensitive Healthcare Data 114\u003c\/p\u003e \u003cp\u003e4.4 Blockchain for Security and Privacy Enhancement in Sensitive Healthcare Data 119\u003c\/p\u003e \u003cp\u003e4.5 Artificial Intelligence, Machine Learning, and Big Data in Healthcare and Its Efficacy in Security and Privacy of Sensitive Healthcare Data 122\u003c\/p\u003e \u003cp\u003e4.5.1 Differential Privacy for Preserving Privacy of Big Medical Healthcare Data and for Its Analytics 124\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 124\u003c\/p\u003e \u003cp\u003eReferences 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Employment of Machine Learning in Disease Detection 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Diabetes Prediction Model Based on Machine Learning 131\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAyush Kumar Gupta, Sourabh Yadav, Priyanka Bhartiya and Divesh Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 131\u003c\/p\u003e \u003cp\u003e5.2 Literature Review 133\u003c\/p\u003e \u003cp\u003e5.3 Proposed Methodology 135\u003c\/p\u003e \u003cp\u003e5.3.1 Data Accommodation 135\u003c\/p\u003e \u003cp\u003e5.3.1.1 Data Collection 135\u003c\/p\u003e \u003cp\u003e5.3.1.2 Data Preparation 136\u003c\/p\u003e \u003cp\u003e5.3.2 Model Training 138\u003c\/p\u003e \u003cp\u003e5.3.2.1 K Nearest Neighbor Classification Technique 139\u003c\/p\u003e \u003cp\u003e5.3.2.2 Support Vector Machine 140\u003c\/p\u003e \u003cp\u003e5.3.2.3 Random Forest Algorithm 142\u003c\/p\u003e \u003cp\u003e5.3.2.4 Logistic Regression 144\u003c\/p\u003e \u003cp\u003e5.3.3 Model Evaluation 145\u003c\/p\u003e \u003cp\u003e5.3.4 User Interaction 145\u003c\/p\u003e \u003cp\u003e5.3.4.1 User Inputs 146\u003c\/p\u003e \u003cp\u003e5.3.4.2 Validation Using Classifier Model 146\u003c\/p\u003e \u003cp\u003e5.3.4.3 Truth Probability 146\u003c\/p\u003e \u003cp\u003e5.4 System Implementation 147\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 153\u003c\/p\u003e \u003cp\u003eReferences 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Lung Cancer Detection Using 3D CNN Based on Deep Learning 157\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSiddhant Panda, Vasudha Chhetri, Vikas Kumar Jaiswal and Sourabh Yadav\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 157\u003c\/p\u003e \u003cp\u003e6.2 Literature Review 159\u003c\/p\u003e \u003cp\u003e6.3 Proposed Methodology 161\u003c\/p\u003e \u003cp\u003e6.3.1 Data Handling 161\u003c\/p\u003e \u003cp\u003e6.3.1.1 Data Gathering 161\u003c\/p\u003e \u003cp\u003e6.3.1.2 Data Pre-Processing 162\u003c\/p\u003e \u003cp\u003e6.3.2 Data Visualization and Data Split 162\u003c\/p\u003e \u003cp\u003e6.3.2.1 Data Visualization 162\u003c\/p\u003e \u003cp\u003e6.3.2.2 Data Split 162\u003c\/p\u003e \u003cp\u003e6.3.3 Model Training 163\u003c\/p\u003e \u003cp\u003e6.3.3.1 Training Neural Network 163\u003c\/p\u003e \u003cp\u003e6.3.3.2 Model Optimization 166\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussion 168\u003c\/p\u003e \u003cp\u003e6.4.1 Gathering and Pre-Processing of Data 169\u003c\/p\u003e \u003cp\u003e6.4.1.1 Gathering and Handling Data 169\u003c\/p\u003e \u003cp\u003e6.4.1.2 Pre-Processing of Data 170\u003c\/p\u003e \u003cp\u003e6.4.2 Data Visualization 171\u003c\/p\u003e \u003cp\u003e6.4.2.1 Resampling 173\u003c\/p\u003e \u003cp\u003e6.4.2.2 3D Plotting Scan 173\u003c\/p\u003e \u003cp\u003e6.4.2.3 Lung Segmentation 173\u003c\/p\u003e \u003cp\u003e6.4.3 Training and Testing of Data in 3D Architecture 175\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 178\u003c\/p\u003e \u003cp\u003eReferences 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Pneumonia Detection Using CNN and ANN Based on Deep Learning Approach 181\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePriyanka Bhartiya, Sourabh Yadav, Ayush Gupta and Divesh Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 182\u003c\/p\u003e \u003cp\u003e7.2 Literature Review 183\u003c\/p\u003e \u003cp\u003e7.3 Proposed Methodology 185\u003c\/p\u003e \u003cp\u003e7.3.1 Data Gathering 185\u003c\/p\u003e \u003cp\u003e7.3.1.1 Data Collection 185\u003c\/p\u003e \u003cp\u003e7.3.1.2 Data Pre-Processing 186\u003c\/p\u003e \u003cp\u003e7.3.1.3 Data Split 186\u003c\/p\u003e \u003cp\u003e7.3.2 Model Training 187\u003c\/p\u003e \u003cp\u003e7.3.2.1 Training of Convolutional Neural Network 189\u003c\/p\u003e \u003cp\u003e7.3.2.2 Training of Artificial Neural Network 191\u003c\/p\u003e \u003cp\u003e7.3.3 Model Fitting 193\u003c\/p\u003e \u003cp\u003e7.3.3.1 Fit Generator 193\u003c\/p\u003e \u003cp\u003e7.3.3.2 Validation of Accuracy and Loss Plot 193\u003c\/p\u003e \u003cp\u003e7.3.3.3 Testing and Prediction 193\u003c\/p\u003e \u003cp\u003e7.4 System Implementation 194\u003c\/p\u003e \u003cp\u003e7.4.1 Data Gathering, Pre-Processing, and Split 194\u003c\/p\u003e \u003cp\u003e7.4.1.1 Data Gathering 194\u003c\/p\u003e \u003cp\u003e7.4.1.2 Data Pre-Processing 195\u003c\/p\u003e \u003cp\u003e7.4.1.3 Data Split 196\u003c\/p\u003e \u003cp\u003e7.4.2 Model Building 196\u003c\/p\u003e \u003cp\u003e7.4.3 Model Fitting 197\u003c\/p\u003e \u003cp\u003e7.4.3.1 Fit Generator 197\u003c\/p\u003e \u003cp\u003e7.4.3.2 Validation of Accuracy and Loss Plot 197\u003c\/p\u003e \u003cp\u003e7.4.3.3 Testing and Prediction 198\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 199\u003c\/p\u003e \u003cp\u003eReferences 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Personality Prediction and Handwriting Recognition Using Machine Learning 203\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVishal Patil and Harsh Mathur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction to the System 204\u003c\/p\u003e \u003cp\u003e8.1.1 Assumptions and Limitations 206\u003c\/p\u003e \u003cp\u003e8.1.1.1 Assumptions 206\u003c\/p\u003e \u003cp\u003e8.1.1.2 Limitations 206\u003c\/p\u003e \u003cp\u003e8.1.2 Practical Needs 206\u003c\/p\u003e \u003cp\u003e8.1.3 Non-Functional Needs 206\u003c\/p\u003e \u003cp\u003e8.1.4 Specifications for Hardware 207\u003c\/p\u003e \u003cp\u003e8.1.5 Specifications for Applications 207\u003c\/p\u003e \u003cp\u003e8.1.6 Targets 207\u003c\/p\u003e \u003cp\u003e8.1.7 Outcomes 207\u003c\/p\u003e \u003cp\u003e8.2 Literature Survey 208\u003c\/p\u003e \u003cp\u003e8.2.1 Computerized Human Behavior Identification Through Handwriting Samples 208\u003c\/p\u003e \u003cp\u003e8.2.2 Behavior Prediction Through Handwriting Analysis 209\u003c\/p\u003e \u003cp\u003e8.2.3 Handwriting Sample Analysis for a Finding of Personality Using Machine Learning Algorithms 209\u003c\/p\u003e \u003cp\u003e8.2.4 Personality Detection Using Handwriting Analysis 210\u003c\/p\u003e \u003cp\u003e8.2.5 Automatic Predict Personality Based on Structure of Handwriting 210\u003c\/p\u003e \u003cp\u003e8.2.6 Personality Identification Through Handwriting Analysis: A Review 210\u003c\/p\u003e \u003cp\u003e8.2.7 Text Independent Writer Identification Using Convolutional Neural Network 210\u003c\/p\u003e \u003cp\u003e8.2.8 Writer Identification Using Machine Learning Approaches 211\u003c\/p\u003e \u003cp\u003e8.2.9 Writer Identification from HandwrittenText Lines 211\u003c\/p\u003e \u003cp\u003e8.3 Theory 212\u003c\/p\u003e \u003cp\u003e8.3.1 Pre-Processing 212\u003c\/p\u003e \u003cp\u003e8.3.2 Personality Analysis 215\u003c\/p\u003e \u003cp\u003e8.3.3 Personality Characteristics 216\u003c\/p\u003e \u003cp\u003e8.3.4 Writer Identification 217\u003c\/p\u003e \u003cp\u003e8.3.5 Features Used 219\u003c\/p\u003e \u003cp\u003e8.4 Algorithm To Be Used 220\u003c\/p\u003e \u003cp\u003e8.5 Proposed Methodology 224\u003c\/p\u003e \u003cp\u003e8.5.1 System Flow 225\u003c\/p\u003e \u003cp\u003e8.6 Algorithms \u003ci\u003evs\u003c\/i\u003e. Accuracy 226\u003c\/p\u003e \u003cp\u003e8.6.1 Implementation 228\u003c\/p\u003e \u003cp\u003e8.7 Experimental Results 231\u003c\/p\u003e \u003cp\u003e8.8 Conclusion 232\u003c\/p\u003e \u003cp\u003e8.9 Conclusion and Future Scope 232\u003c\/p\u003e \u003cp\u003eAcknowledgment 232\u003c\/p\u003e \u003cp\u003eReferences 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Risk Mitigation in Children With Autism Spectrum Disorder Using Brain Source Localization 237\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJoy Karan Singh, Deepti Kakkar and Tanu Wadhera\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 238\u003c\/p\u003e \u003cp\u003e9.2 Risk Factors Related to Autism 239\u003c\/p\u003e \u003cp\u003e9.2.1 Assistive Technologies for Autism 240\u003c\/p\u003e \u003cp\u003e9.2.2 Functional Connectivity as a Biomarker for Autism 241\u003c\/p\u003e \u003cp\u003e9.2.3 Early Intervention and Diagnosis 242\u003c\/p\u003e \u003cp\u003e9.3 Materials and Methodology 243\u003c\/p\u003e \u003cp\u003e9.3.1 Subjects 243\u003c\/p\u003e \u003cp\u003e9.3.2 Methods 243\u003c\/p\u003e \u003cp\u003e9.3.3 Data Acquisition and Processing 243\u003c\/p\u003e \u003cp\u003e9.3.4 sLORETA as a Diagnostic Tool 244\u003c\/p\u003e \u003cp\u003e9.4 Results and Discussion 245\u003c\/p\u003e \u003cp\u003e9.5 Conclusion and Future Scope 247\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Predicting Chronic Kidney Disease Using Machine Learning 251\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMonika Gupta and Parul Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 252\u003c\/p\u003e \u003cp\u003e10.2 Machine Learning Techniques for Prediction of Kidney Failure 253\u003c\/p\u003e \u003cp\u003e10.2.1 Analysis and Empirical Learning 254\u003c\/p\u003e \u003cp\u003e10.2.2 Supervised Learning 255\u003c\/p\u003e \u003cp\u003e10.2.3 Unsupervised Learning 256\u003c\/p\u003e \u003cp\u003e10.2.3.1 Understanding and Visualization 257\u003c\/p\u003e \u003cp\u003e10.2.3.2 Odd Detection 257\u003c\/p\u003e \u003cp\u003e10.2.3.3 Object Completion 258\u003c\/p\u003e \u003cp\u003e10.2.3.4 Information Acquisition 258\u003c\/p\u003e \u003cp\u003e10.2.3.5 Data Compression 258\u003c\/p\u003e \u003cp\u003e10.2.3.6 Capital Market 258\u003c\/p\u003e \u003cp\u003e10.2.4 Classification 259\u003c\/p\u003e \u003cp\u003e10.2.4.1 Training Process 260\u003c\/p\u003e \u003cp\u003e10.2.4.2 Testing Process 260\u003c\/p\u003e \u003cp\u003e10.2.5 Decision Tree 261\u003c\/p\u003e \u003cp\u003e10.2.6 Regression Analysis 263\u003c\/p\u003e \u003cp\u003e10.2.6.1 Logistic Regression 263\u003c\/p\u003e \u003cp\u003e10.2.6.2 Ordinal Logistic Regression 265\u003c\/p\u003e \u003cp\u003e10.2.6.3 Estimating Parameters 266\u003c\/p\u003e \u003cp\u003e10.2.6.4 Multivariate Regression 268\u003c\/p\u003e \u003cp\u003e10.3 Data Sources 269\u003c\/p\u003e \u003cp\u003e10.4 Data Analysis 272\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 274\u003c\/p\u003e \u003cp\u003e10.6 Future Scope 274\u003c\/p\u003e \u003cp\u003eReferences 274\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Advanced Applications of Machine Learning in Healthcare 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Behavioral Modeling Using Deep Neural Network Framework for ASD Diagnosis and Prognosis 281\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTanu Wadhera, Deepti Kakkar and Rajneesh Rani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 282\u003c\/p\u003e \u003cp\u003e11.2 Automated Diagnosis of ASD 284\u003c\/p\u003e \u003cp\u003e11.2.1 Deep Learning 289\u003c\/p\u003e \u003cp\u003e11.2.2 Deep Learning in ASD 290\u003c\/p\u003e \u003cp\u003e11.2.3 Transfer Learning Approach 290\u003c\/p\u003e \u003cp\u003e11.3 Purpose of the Chapter 292\u003c\/p\u003e \u003cp\u003e11.4 Proposed Diagnosis System 293\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 294\u003c\/p\u003e \u003cp\u003eReferences 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Random Forest Application of Twitter Data Sentiment Analysis in Online Social Network Prediction 299\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eArnav Munshi, M. Arvindhan and Thirunavukkarasu K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 300\u003c\/p\u003e \u003cp\u003e12.1.1 Motivation 300\u003c\/p\u003e \u003cp\u003e12.1.2 Domain Introduction 300\u003c\/p\u003e \u003cp\u003e12.2 Literature Survey 302\u003c\/p\u003e \u003cp\u003e12.3 Proposed Methodology 304\u003c\/p\u003e \u003cp\u003e12.4 Implementation 311\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 311\u003c\/p\u003e \u003cp\u003eReferences 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Remedy to COVID-19: Social Distancing Analyzer 315\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSourabh Yadav\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 315\u003c\/p\u003e \u003cp\u003e13.2 Literature Review 318\u003c\/p\u003e \u003cp\u003e13.3 Proposed Methodology 321\u003c\/p\u003e \u003cp\u003e13.3.1 Person Detection 321\u003c\/p\u003e \u003cp\u003e13.3.1.1 Frame Creation 324\u003c\/p\u003e \u003cp\u003e13.3.1.2 Contour Detection 325\u003c\/p\u003e \u003cp\u003e13.3.1.3 Matching with COCO Model 326\u003c\/p\u003e \u003cp\u003e13.3.2 Distance Calculation 326\u003c\/p\u003e \u003cp\u003e13.3.2.1 Calculation of Centroid 326\u003c\/p\u003e \u003cp\u003e13.3.2.2 Distance Among Adjacent Centroids 327\u003c\/p\u003e \u003cp\u003e13.4 System Implementation 328\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 333\u003c\/p\u003e \u003cp\u003eReferences 334\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 IoT-Enabled Vehicle Assistance System of Highway Resourcing for Smart Healthcare and Sustainability 337\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShubham Joshi and Radha Krishna Rambola\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 338\u003c\/p\u003e \u003cp\u003e14.2 Related Work 340\u003c\/p\u003e \u003cp\u003e14.2.1 Adoption of IoT in Vehicle to Ensure Driver Safety 341\u003c\/p\u003e \u003cp\u003e14.2.2 IoT in Healthcare System 341\u003c\/p\u003e \u003cp\u003e14.2.3 The Technology Used in Assistance Systems 343\u003c\/p\u003e \u003cp\u003e14.2.3.1 Adaptive Cruise Control (ACC) 343\u003c\/p\u003e \u003cp\u003e14.2.3.2 Lane Departure Warning 343\u003c\/p\u003e \u003cp\u003e14.2.3.3 Parking Assistance 343\u003c\/p\u003e \u003cp\u003e14.2.3.4 Collision Avoidance System 343\u003c\/p\u003e \u003cp\u003e14.2.3.5 Driver Drowsiness Detection 344\u003c\/p\u003e \u003cp\u003e14.2.3.6 Automotive Night Vision 344\u003c\/p\u003e \u003cp\u003e14.3 Objectives, Context, and Ethical Approval 344\u003c\/p\u003e \u003cp\u003e14.4 Technical Background 345\u003c\/p\u003e \u003cp\u003e14.4.1 IoT With Health 345\u003c\/p\u003e \u003cp\u003e14.4.2 Machine-to-Machine (M2M) Communication 345\u003c\/p\u003e \u003cp\u003e14.4.3 Device-to-Device (D2D) Communication 345\u003c\/p\u003e \u003cp\u003e14.4.4 Wireless Sensor Network 346\u003c\/p\u003e \u003cp\u003e14.4.5 Crowdsensing 346\u003c\/p\u003e \u003cp\u003e14.5 IoT Infrastructural Components for Vehicle Assistance System 346\u003c\/p\u003e \u003cp\u003e14.5.1 Communication Technology 346\u003c\/p\u003e \u003cp\u003e14.5.2 Sensor Network 347\u003c\/p\u003e \u003cp\u003e14.5.3 Infrastructural Component 348\u003c\/p\u003e \u003cp\u003e14.5.4 Human Health Detection by Sensors 348\u003c\/p\u003e \u003cp\u003e14.6 IoT-Enabled Vehicle Assistance System of Highway Resourcing for Smart Healthcare and Sustainability 349\u003c\/p\u003e \u003cp\u003e14.7 Challenges in Implementation 353\u003c\/p\u003e \u003cp\u003e14.8 Conclusion 353\u003c\/p\u003e \u003cp\u003eReferences 354\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Aids of Machine Learning for Additively Manufactured Bone Scaffold 359\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNimisha Rahul Shirbhate and Sanjay Bokade\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 360\u003c\/p\u003e \u003cp\u003e15.1.1 Bone Scaffold 360\u003c\/p\u003e \u003cp\u003e15.1.2 Bone Grafting 362\u003c\/p\u003e \u003cp\u003e15.1.3 Comparison Bone Grafting and Bone Scaffold 363\u003c\/p\u003e \u003cp\u003e15.2 Research Background 364\u003c\/p\u003e \u003cp\u003e15.3 Statement of Problem 364\u003c\/p\u003e \u003cp\u003e15.4 Research Gap 365\u003c\/p\u003e \u003cp\u003e15.5 Significance of Research 366\u003c\/p\u003e \u003cp\u003e15.6 Outline of Research Methodology 366\u003c\/p\u003e \u003cp\u003e15.6.1 Customized Design of Bone Scaffold 366\u003c\/p\u003e \u003cp\u003e15.6.2 Manufacturing Methods and Biocompatible Material 367\u003c\/p\u003e \u003cp\u003e15.6.2.1 Conventional Scaffold Fabrication 368\u003c\/p\u003e \u003cp\u003e15.6.2.2 Additive Manufacturing 369\u003c\/p\u003e \u003cp\u003e15.6.2.3 Application of Additive Manufacturing\/3D Printing in Healthcare 370\u003c\/p\u003e \u003cp\u003e15.6.2.4 Automated Process Monitoring in 3D Printing Using Supervised Machine Learning 376\u003c\/p\u003e \u003cp\u003e15.7 Conclusion 377\u003c\/p\u003e \u003cp\u003eReferences 377\u003c\/p\u003e \u003cp\u003eIndex 381\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430957609240,"sku":"9781119791720","price":140.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119791720.jpg?v=1784766165","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/emerging-technologies-for-healthcare-internet-of-things-and-deep-learning-models-hardback-9781119791720","provider":"Freshly Printed Books","version":"1.0","type":"link"}