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