Machine Learning and IoT for Intelligent Systems and Smart Applications
- Length: 272 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-11-18
- ISBN-10: 1032047232
- ISBN-13: 9781032047232
- Sales Rank: #0 (See Top 100 Books)
The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications. It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects.
Features:
- Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications.
- Discusses supervised and unsupervised machine learning for IoT data and devices.
- Presents an overview of the different algorithms related to Machine learning and IoT.
- Covers practical case studies on industrial and smart home automation.
- Includes implementation of AI from case studies in personal and industrial IoT.
This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering.
Cover Half Title Series Page Title Page Copyright Page Contents Preface Editors' Biographies Contributors 1. A Study on Feature Extraction and Classification Techniques for Melanoma Detection 1.1 Introduction 1.2 Feature Extraction 1.2.1 Fourier Transform (FT) Drawbacks 1.2.2 Short Time Fourier Transform (STFT) Drawbacks 1.2.3 Wavelet Transform 1.2.3.1 Discrete Wavelet Transform Drawbacks 1.2.3.2 Discrete Curvelet Transform Drawbacks 1.2.3.3 Discrete Contourlet Transform Drawbacks 1.2.3.4 Discrete Shearlet Transform Drawbacks 1.2.3.5 Bendlet Transform 1.3 Classification 1.3.1 Logistic Regression 1.3.2 K-Nearest Neighbor 1.3.3 Decision Trees 1.3.4 Support Vector Machine 1.4 Skin Cancer Diagnostic System for Melanoma Detection 1.5 Conclusion References 2. Machine Learning Based Microstrip Antenna Design in Wireless Communications 2.1 Introduction 2.2 Machine Learning in MSA Design 2.3 Application of MSA in IOT 2.4 Design & Analysis of MSA Using ANN 2.4.1 Artificial Neural Network 2.5 Results and Discussion 2.6 Design of Microstrip Antenna and Characterization Using SVM Method 2.7 Design of MSA for IoT Applications 2.8 Conclusion References 3. LCL-T Filter Based Analysis of Two Stage Single Phase Grid Connected Module with Intelligent FANN Controllers 3.1 Introduction 3.2 Literature Survey 3.3 Proposed System 3.3.1 Mode of Operation-1: (t0-t1) 3.3.2 Mode of Operation-2: (t1-t2) 3.3.3 Mode of Operation-3: (t2-t3) 3.3.4 Mode of Operation-4: (t3-t4) 3.3.5 Mode of Operation-5: (t4-t5) 3.4 State Space Modeling and LCL-T Filter Design 3.4.1 Stability Analysis 3.4.2 Design of FANN Controller 3.5 Simulation Results 3.5.1 Hardware Implementation of Two Stage Single Phase LCL-T Inverter 3.6 Conclusion References 4. Motion Vector Analysis Using Machine Learning Models to Identify Lung Damages for COVID-19 Patients 4.1 Introduction 4.1.1 Background of the Study 4.1.2 Motivation and Problem Statement 4.1.3 Structure of the Chapter 4.2 Proposed Methodology 4.2.1 Data Collection and Pre-Processing 4.2.2 Feature Extraction 4.2.2.1 Block Matching Algorithm 4.2.2.2 Region-Wise Edge Factor-Based Motion Vector Extraction 4.2.3 Feature Processing 4.2.4 Classification 4.3 Results and Discussion 4.3.1 Feature Analysis 4.3.2 Classification Analysis 4.4 Conclusion References 5. Enhanced Effective Generative Adversarial Networks Based LRSD and SP Learned Dictionaries with Amplifying CS 5.1 Introduction 5.2 Related Work 5.2.1 SpR and DL 5.2.2 The Producer and the Discriminator 5.2.2.1 Discriminative LR and Sparse DL 5.3 Proposed Work 5.3.1 Decomposing LR and SP 5.3.2 Enhanced Effective Generative Adversarial Networks 5.3.3 The Producer and the Discriminator 5.3.4 Fusion Scheme 5.4 Experimental Setup 5.4.1 Applying Enhanced Effective Generative Adversarial Networks 5.5 Discussion 5.6 Conclusion References 6. Deep Learning Based Parkinson's Disease Prediction System 6.1 Introduction 6.2 Literature Survey 6.3 Proposed Methodology 6.4 Implementation 6.4.1 Data Collection 6.4.2 Data Preprocessing 6.4.3 Deep Learning Algorithm with RBM 6.4.4 Training Phase 6.4.5 Testing Phase 6.5 Result Analysis 6.6 Conclusion References 7. Non-uniform Data Reduction Technique with Edge Preservation to Improve Diagnostic Visualization of Medical Images 7.1 Introduction 7.2 Methodology 7.2.1 Algorithm of the Data Reduction Algorithm 7.2.2 Algorithm of the Proposed Data Reduction Method with Enhanced Edge Information 7.3 Results and Discussion 7.3.1 Regression Analysis 7.4 Conclusion References 8. A Critical Study on Genetically Engineered Bioweapons and Computer-Based Techniques as Counter Measure 8.1 Introduction and History 8.2 Genetically Engineered Pathogen 8.2.1 Designer Genes 8.2.2 Binary Bioweapon 8.2.3 Gene Therapy as Bioweapon 8.2.4 Stealth Virus 8.2.5 Hot Swapping Disease 8.2.6 Designer Disease 8.3 Computer-Based Detection and Counter Measure Techniques 8.3.1 Computer and Artificial Intelligence-Based Counter Measure Techniques 8.3.2 Computer-Assisted Surgery as Counter Measure 8.3.3 Big Data as Healthcare 8.3.4 Computer-Assisted Decision Making 8.3.5 Computer Vision-Based Techniques as Counter Measure 8.3.6 IoT-Based System as Counter Measure for Bioweapon Against Crop War 8.4 Conclusion References 9. An Automated Hybrid Transfer Learning System for Detection and Segmentation of Tumor in MRI Brain Images with UNet and VGG-19 Network 9.1 Introduction 9.2 Related Works 9.3 Proposed System 9.4 Experimental Setup and Results 9.5 Discussion 9.6 Conclusion and Future Work References 10. Deep Learning-Computer Aided Melanoma Detection Using Transfer Learning 10.1 Introduction 10.2 Related Research Work 10.2.1 Benign Sample Image 10.2.2 Benign Sample Image 10.2.3 Melanoma Sample Image 10.2.4 Melanoma Sample Image 10.3 Transfer Learning CAD SCC Model 10.3.1 Model Summary 10.3.2 Sample Images 10.4 Accuracy Results Achieved Through the Proposed Processing 10.4.1 Loss Results Achieved Through the Proposed Processing 10.4.2 Confusion Matrix 10.4.3 Classification Report 10.5 Conclusion References 11. Development of an Agent-Based Interactive Tutoring System for Online Teaching in School Using Classter 11.1 Introduction 11.2 Literature Review 11.3 Methods and Materials 11.3.1 Standard Intelligent Learning System 11.4 Implementation 11.4.1 Student Enrollment 11.4.2 Standard Intelligent Learning System 11.4.3 Evaluation System 11.5 Result and Discussion 11.5.1 Classter Student Performance Assessment 11.5.2 RNN Network 11.6 Conclusion References 12. Fusion of Datamining and Artificial Intelligence in Prediction of Hazardous Road Accidents 12.1 Introduction 12.2 Related Works on Prediction of Road Accidents 12.3 Motivation and Problem Statement 12.4 Proposed Methodology 12.5 Kaggle and Government Statistical Data 12.6 Dark Sky 12.6.1 The Datasets Help to Assume the Constant Weather Conditions on the Whole Day 12.6.2 The Environmental Factors Depend on Previous Environmental Datasets 12.6.3 Apriori Algorithm for Road Accident Prediction 12.6.4 Road Accident Analysis and Classification Using Apriori Algorithm 12.6.5 Strong Association Rule Mining for Road Accidents 12.6.6 Naïve Bayes Algorithm for Prevention of Road Accidents 12.6.7 Sample Example 12.6.8 Training Dataset 12.7 Software Used for Prediction 12.7.1 Jupyter 12.7.2 Python 12.7.3 HTML and CSS 12.8 Results and Discussion 12.9 Graphical Representation 12.10 Road Category and Road Features 12.11 Accidents by Road Environment 12.12 Accidents by Weather Condition 12.13 Types of Vehicles Involved in Road Accidents 12.14 Prevention 12.14.1 Using AI Techniques to Predict and Prevent Road Accidents 12.14.2 Machine Learning Process Reduces the Life Risk 12.14.3 Avoid the Rush and Drunk Driving 12.15 Limitation 12.16 Recommendation 12.17 Significance of the Study 12.18 Conclusion References Index
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