دانلود کتاب Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications (Smart Engineering Systems: Design and Applications)

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Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface Editors Contributors Chapter 1 A Review of Automated Sleep Apnea Detection Using Deep Neural Network 1.1 Introduction 1.2 Materials and Methods 1.3 Signal and Dataset 1.3.1 Based on Pulse Oxygen Saturation Signal 1.3.2 Based on Electrocardiogram (ECG) 1.3.3 Based on Airflow (AF) 1.3.4 Based on Sound 1.4 Data Preprocessing 1.4.1 Raw Signal 1.4.2 Filtered Signal 1.4.3 Signal Normalization 1.4.4 Spectrogram 1.4.5 Feature Analyses 1.5 Performance Metrics 1.6 Classifiers 1.6.1 CNN 1.6.1.1 D1CNN 1.6.1.2 D2CNN 1.6.2 RNN 1.6.2.1 LSTM 1.6.2.2 GRU 1.6.3 Deep Vanilla Neural Network (DVNN) 1.6.3.1 MHLNN 1.6.3.2 SSAE 1.6.3.3 DBN 1.6.4 Combined DNN Approach 1.7 Discussion 1.8 Conclusion References Chapter 2 Optimization of Tool Wear Rate Using Artificial Intelligence–Based TLBO and Cuckoo Search Approach 2.1 Introduction 2.2 Artificial Intelligence 2.3 Electric Discharge Machining (EDM) 2.4 Analysis of Variance (ANOVA) 2.5 Optimization 2.5.1 Cuckoo Search Algorithm 2.5.2 Teaching–Learning-Based Optimization 2.6 Experimental Details and Results 2.7 Conclusion References Chapter 3 Lung Tumor Segmentation Using a 3D Densely Connected Convolutional Neural Network 3.1 Introduction 3.2 Literature Survey 3.2.1 Traditional vs Deep Learning Approaches 3.2.2 Lung Nodule Detection 3.2.3 Lung Tumor Detection 3.3 Related Work 3.3.1 U-Net Segmentation Model 3.3.2 DenseNet Model 3.4 Proposed Methodology 3.4.1 Dataset 3.4.1.1 Dataset Description 3.4.1.2 Data Preprocessing 3.4.2 Segmentation Model 3.4.2.1 Model Architecture 3.4.2.2 Model Training 3.5 Experimental Results 3.5.1 Evaluation Criteria 3.5.2 Results 3.6 Discussion 3.7 Conclusion and Future Scope Acknowledgment References Chapter 4 Day-Ahead Solar Power Forecasting Using Artificial Neural Network with Outlier Detection 4.1 Introduction 4.2 Literature Review 4.3 Electrical Characteristics of a PV Module 4.3.1 Correlation of Temperature and Irradiance to the Output Power of a PV Module 4.3.2 Variation of Current and Voltage with Irradiance and Temperature 4.3.3 Studied PV System and Data 4.3.4 Data Pre-Processing 4.4 Overview to ANN 4.5 Methodology 4.5.1 Interpolation for Imputation of Missing Values 4.5.2 Exponential Smoothing for Imputation of Missing Values 4.5.3 Design of ANN Structure 4.5.4 Evaluation of the Forecasting Model 4.6 Results and Discussion 4.7 Conclusion Acknowledgement References Chapter 5 Fuzzy-Inspired Three-Dimensional DWT and GLCM Framework for Pixel Characterization of Hyperspectral Images 5.1 Introduction 5.2 Experimentation 5.2.1 3D DWT and 3D GLCM-Based Approach for Hyperspectral Image Classification 5.2.1.1 3D DWT Decomposition 5.2.1.2 3D GLCM Feature Extraction 5.2.2 Support Vector Machine (SVM) 5.2.2.1 SVM for Nonlinear and Nonseparable Classes 5.2.3 3D DWT and 3D GLCM-Based Hyperspectral Image Classification Method 5.2.4 Proposed Fuzzy-Inspired Image Classification Method 5.2.4.1 Mixed Pixel Identification 5.2.4.2 Fuzzicatfiion 5.2.4.3 Membership Function 5.2.4.4 Reclassification 5.2.4.5 Fuzzy-Inspired Process 5.3 Results and Discussion 5.3.1 Results Obtained for Simple 3D DWT and GLCM Method 5.3.2 Results Obtained for Fuzzy-Inspired 3D DWT and 3D GLCM Method 5.4 Conclusion 5.5 Scope References Chapter 6 Painless Machine Learning Approach to Estimate Blood Glucose Level with Non-Invasive Devices 6.1 Introduction 6.2 Types of Glucose Monitoring Techniques 6.2.1 Invasive Method for Glucose Measurement 6.2.2 Non-Invasive Method for Glucose Measurement 6.3 Painless Non-Invasive Glucometer Using Machine Learning Approach 6.4 Results and Discussion 6.4.1 Channel Estimation for Finding Glucose Level 6.4.2 Model Validation 6.4.3 Fast-Tree Regression Machine Learning Technique 6.5 Conclusion References Chapter 7 Artificial Intelligence and Machine Learning in Biomedical Applications 7.1 Introduction 7.1.1 Innovations of Technology 7.2 Challenges and Issues 7.2.1 Data Collection 7.2.2 Poor Quality of Data 7.2.3 Interpretability 7.2.4 Domain Complexity 7.2.5 Feature Enrichment 7.2.6 Temporal Modelling 7.2.7 Balancing Model Accuracy and Interpretability 7.2.8 Legal Issues 7.3 Artificial Intelligence and Machine Learning Applications in Biomedical 7.3.1 Precision Medicine 7.3.2 Genetics-Based Solutions 7.3.3 Drug Improvement and Discovery 7.3.4 Prediction of Protein Structure 7.3.5 Medical Image Recognition 7.3.6 Health Monitoring and Wearables 7.3.7 Minimally Invasive Surgery (MIS) 7.3.8 Monitoring by Biosensor 7.4 Success Elements for AI in Biomedical Engineering 7.4.1 Assessment of Condition 7.4.2 Managing Complications 7.4.3 Patient-Care Assistance 7.4.4 Medical Research 7.5 Conclusion References Chapter 8 The Use of Artificial Intelligence-Based Models for Biomedical Application 8.1 Introduction 8.2 AI Methods and Applications 8.2.1 Machine Learning (ML) 8.2.2 Natural Language Processing (NLP) 8.2.3 Neural Network (NN) 8.2.4 Deep Learning (DL) 8.2.5 Machine Vision/Computer Vision 8.3 Robotic-Assisted Surgical Systems (RASS) and Computer-Assisted Surgery (CAS) 8.4 Virtual Nurse Assistants (VNAs) for Healthcare 8.4.1 Medication Management and Medication Error Reduction (MMMER) 8.4.2 Improving Medical Safety 8.4.3 Monitoring Medication Non-Adherence 8.4.4 Clinical Trial Participation (CTP) 8.5 Preliminary Diagnosis and Prediction (PDP) 8.5.1 Diabetes Prediction 8.5.2 Cancer Prediction 8.5.3 Tuberculosis Diagnosis 8.5.4 Psychiatric Diagnosis 8.6 Medical Imaging and Image Diagnostics (MID) 8.6.1 Medical Imaging with Deep Learning 8.6.2 Image Diagnosis for Oncology 8.6.3 Optical Coherence Tomography (OCT) Diagnosis 8.7 Patient Health Monitoring (PHM) 8.7.1 Heart Failure Monitoring 8.7.2 Health Monitoring After Surgery 8.7.3 Health Monitoring for Oncology Patients 8.8 Additional Quantitative Methods Used in Biomedical Application 8.8.1 Neural Network-Based ECG Anomaly Detection 8.8.2 A Fuzzy Neural Network Model for Post-surgery Risk Prediction 8.8.3 Heart Stroke Prediction with GUI Using Artificial Intelligence 8.9 Key Elements for Successful Implementation of AI-Based Services in Healthcare 8.10 Opportunities and Challenges 8.11 Conclusion and Future Work Acknowledgment References Chapter 9 Role of Artificial Intelligence in Transforming Agriculture 9.1 Introduction 9.2 Role of AI in Determining the Nature of the Soil and Recommending Suitable Plants 9.3 Role of AI in Estimating the Water Requirement for the Crops and the Determining the Availability of Water in Water Bodies and the Expected Amount of Rain 9.4 Role of IoT in Retrieving the Mineral Contents in the Soil Regularly and Alerting the Farmers to Add Suitable Minerals Whenever Required 9.5 Use of IoT and CNN in Protecting Crops from Being Affected by Animals, Birds and Pests 9.6 Role of IoT and Image Processing in Detecting the Diseases in Plants and Alerting the Farmers to Apply Pesticides to Save the Affected Plants and to Avoid Further Spreading of the Disease 9.7 ML in Forecasting the Cost of the Agricultural Products and Recommending Suitable Season for Planting and Harvesting to Make Better Profits 9.7.1 Crop Harvesting Using AI 9.7.2 Agricultural Product Grading Using AI 9.8 Conclusion References Chapter 10 Internet of Things (IoT) and Artificial Intelligence for Smart Communications 10.1 Introduction 10.2 Application Scenarios of IoT and AI 10.3 Related Work 10.4 IoT Road Map and Service Model 10.5 IoT and AI Enabling Technologies 10.6 Proposals for Enhancement of AI-IoT with Challenges 10.7 Conclusions References Chapter 11 Cyber-Security in the Internet of Things 11.1 Introduction 11.1.1 Cyber Threats in IoT 11.2 Security Issues in IoT 11.2.1 IoT Generic Architecture 11.2.2 Reasons for Cyber-Attacks in IoT Network 11.3 Potential Cyber-Attacks in IoT 11.4 Need of Cyber-Security in IoT 11.4.1 Need of Standardization 11.4.2 Data Issues 11.5 Mitigation Techniques 11.5.1 Strong Authentication Solutions 11.5.2 Access Control Mechanism 11.5.3 Intrusion Detection System (IDS) 11.5.4 Software-Defined Networking (SDN) 11.5.5 Light-Weight Cryptography 11.6 Conclusion References Chapter 12 Smart Materials for Electrochemical Water Oxidation 12.1 Introduction (Is There Any Alternative to Fossil Fuels?) 12.2 Electrochemical Water Splitting 12.3 Mechanism of Oxygen Evolution Reaction (OER) and Evaluation Parameters 12.3.1 Overpotential (η) 12.3.2 Tafel Slope (b) 12.3.3 Electrochemical Active Surface Area (ECSA) 12.4 Electrocatalysts for OER 12.4.1 Metal Oxides 12.4.2 Metal Sulfides 12.4.3 Metal Phosphides 12.4.4 Layered Double Hydroxide (LDH) 12.5 Summary and Future Perspective Acknowledgments References Chapter 13 Innovative Approach for Real-Time P–V Curve Identification: Design-to-Application 13.1 Introduction 13.2 PV Module Characteristics and MPPT 13.3 Experimental Prototype and System Parameters 13.3.1 Boost Converter for MPPT 13.3.2 Design of 40 W Boost Converter for MPPT 13.3.3 Control Circuit Implementation 13.4 Results and Discussion 13.4.1 Boost Converter in an Open Loop 13.4.2 Boost Converter in a Closed Loop 13.4.3 Boost Converter for Capturing I–V/P–V Characteristics 13.4.4 Boost Converter for MPPT 13.5 Conclusions References Chapter 14 Superhydrophobic Coatings of Silica NPs on Cover Glass of Solar Cells for Self-Cleaning Applications 14.1 Introduction 14.2 Experimental Section 14.2.1 Materials 14.2.2 Preparation of Superhydrophobic 14.2.3 Characterization 14.3 Result and Discussion 14.3.1 Surface Structure and Wettability 14.3.2 Durability of Superhydrophobic Coating 14.3.3 Self-Cleaning Property 14.4 Conclusion Highlights Acknowledgments References Chapter 15 Carbonaceous Composites of Rare Earth Metal Chalcogenides: Synthesis, Properties and Supercapacitive Applications 15.1 Introduction 15.2 Principle and Mechanism of Supercapacitor 15.2.1 Electric Double-Layer Capacitance (EDLC) 15.2.2 Pseudocapacitor 15.3 Factors Affecting Supercapacitor Performance 15.3.1 Chemical Composition of Material 15.3.2 Electrolyte 15.3.3 Temperature 15.3.4 Crystal Structure and Crystallinity 15.3.5 Morphology 15.3.6 Specific Surface Area and Pore Structure 15.3.7 Thickness of the Electrode 15.4 Rare Earth Metal Chalcogenides–Based Carbonaceous Composites 15.4.1 Cerium Chalcogenides Composites 15.4.2 Lanthanum Chalcogenides Composites 15.4.3 Samarium Chalcogenide Composites 15.4.4 Europium Chalcogenides Composites 15.4.5 Dysprosium Chalcogenides Composites 15.5 Summary and Conclusions Acknowledgement References Chapter 16 Low-Stress Abrasion Response of Heat-Treated LM25–SiCp Composite 16.1 Introduction 16.2 Experiments 16.2.1 Synthesis of the Materials 16.2.2 Microstructure Analysis 16.2.3 Evaluation of Densities and Hardnesses 16.2.4 Low-Stress Abrasion 16.3 Result and Discussion 16.3.1 Microstructure Characterisation 16.3.2 Density and Hardness Analysis 16.3.3 Low-Stress Abrasion 16.3.4 Abrasive Worn Surface 16.4 Conclusions References Chapter 17 Post-Annealing Influence on Structural, Surface and Optical Properties of Cu[sub(3)]BiS[sub(3)] Thin Films for Photovoltaic Solar Cells 17.1 Introduction 17.2 Experimental Section 17.2.1 Resources 17.2.2 Preparation of Cu[sub(3)]BiS[sub(3)] Precursor Solution 17.2.3 Cu[sub(3)]BiS[sub(3)] Thin-Film Deposition 17.2.4 Cu[sub(3)]BiS[sub(3)] Thin-Film Characterization 17.3 Results and Discussion 17.3.1 Structural Analysis 17.3.2 Raman Spectroscopy 17.3.3 Scanning Electron Microscopy 17.3.4 Water Contact Angle Studies 17.3.5 Optical Studies 17.4 Conclusions Acknowledgements References Chapter 18 Self-Cleaning Antireflection Coatings on Glass for Solar Energy Applications 18.1 Introduction 18.1.1 Theoretical Aspects of Antireflection and Non-Wettability 18.1.1.1 Antireflection 18.1.1.2 Non-Wettability 18.1.2 Fabrication Technique of Hydrophobic Antireflection Coatings 18.1.2.1 Spin-Coating Technique 18.1.2.2 Dip-Coating Technique 18.1.3 Recent Progress towards the Self-Cleaning Antireflection Coatings 18.2 Fabrication of Hydrophobic Antireflection Coating 18.2.1 Materials 18.2.2 Preparation of Sol and Deposition of Coating 18.3 Results and Discussion 18.3.1 Optical Performance of the Coating 18.3.2 Structural Determination Using FTIR Spectroscopy 18.3.3 Wetting Property of the Coating 18.4 Conclusion References Index