Machine Learning and IoT for Intelligent Systems and Smart Applications

Machine Learning and IoT for Intelligent Systems and Smart Applications

by Madhumathy P, M Kumar, R. Umamaheswari
Released November 2021
Publisher(s): CRC Press
ISBN: 9781000484984

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Book description

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. It includes pertinent applications and case studies.

Table of contents

  1. Cover
  2. Half Title
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. Editors’ Biographies
  9. Contributors
  10. 1 A Study on Feature Extraction and Classification Techniques for Melanoma Detection
    1. 1.1 Introduction
    2. 1.2 Feature Extraction
      1. 1.2.1 Fourier Transform (FT)
        1. Drawbacks
      2. 1.2.2 Short Time Fourier Transform (STFT)
        1. Drawbacks
      3. 1.2.3 Wavelet Transform
        1. 1.2.3.1 Discrete Wavelet Transform
        2. Drawbacks
        3. 1.2.3.2 Discrete Curvelet Transform
        4. Drawbacks
        5. 1.2.3.3 Discrete Contourlet Transform
        6. Drawbacks
        7. 1.2.3.4 Discrete Shearlet Transform
        8. Drawbacks
        9. 1.2.3.5 Bendlet Transform
    3. 1.3 Classification
      1. 1.3.1 Logistic Regression
      2. 1.3.2 K-Nearest Neighbor
      3. 1.3.3 Decision Trees
      4. 1.3.4 Support Vector Machine
    4. 1.4 Skin Cancer Diagnostic System for Melanoma Detection
    5. 1.5 Conclusion
    6. References
  11. 2 Machine Learning Based Microstrip Antenna Design in Wireless Communications
    1. 2.1 Introduction
    2. 2.2 Machine Learning in MSA Design
    3. 2.3 Application of MSA in IOT
    4. 2.4 Design & Analysis of MSA Using ANN
      1. 2.4.1 Artificial Neural Network
    5. 2.5 Results and Discussion
    6. 2.6 Design of Microstrip Antenna and Characterization Using SVM Method
    7. 2.7 Design of MSA for IoT Applications
    8. 2.8 Conclusion
    9. References
  12. 3 LCL-T Filter Based Analysis of Two Stage Single Phase Grid Connected Module with Intelligent FANN Controllers
    1. 3.1 Introduction
    2. 3.2 Literature Survey
    3. 3.3 Proposed System
      1. 3.3.1 Mode of Operation-1: (t0-t1)
      2. 3.3.2 Mode of Operation-2: (t1-t2)
      3. 3.3.3 Mode of Operation-3: (t2-t3)
      4. 3.3.4 Mode of Operation-4: (t3-t4)
      5. 3.3.5 Mode of Operation-5: (t4-t5)
    4. 3.4 State Space Modeling and LCL-T Filter Design
      1. 3.4.1 Stability Analysis
      2. 3.4.2 Design of FANN Controller
    5. 3.5 Simulation Results
      1. 3.5.1 Hardware Implementation of Two Stage Single Phase LCL-T Inverter
    6. 3.6 Conclusion
    7. References
  13. 4 Motion Vector Analysis Using Machine Learning Models to Identify Lung Damages for COVID-19 Patients
    1. 4.1 Introduction
      1. 4.1.1 Background of the Study
      2. 4.1.2 Motivation and Problem Statement
      3. 4.1.3 Structure of the Chapter
    2. 4.2 Proposed Methodology
      1. 4.2.1 Data Collection and Pre-Processing
      2. 4.2.2 Feature Extraction
        1. 4.2.2.1 Block Matching Algorithm
        2. 4.2.2.2 Region-Wise Edge Factor-Based Motion Vector Extraction
      3. 4.2.3 Feature Processing
      4. 4.2.4 Classification
    3. 4.3 Results and Discussion
      1. 4.3.1 Feature Analysis
      2. 4.3.2 Classification Analysis
    4. 4.4 Conclusion
    5. References
  14. 5 Enhanced Effective Generative Adversarial Networks Based LRSD and SP Learned Dictionaries with Amplifying CS
    1. 5.1 Introduction
    2. 5.2 Related Work
      1. 5.2.1 SpR and DL
      2. 5.2.2 The Producer and the Discriminator
        1. 5.2.2.1 Discriminative LR and Sparse DL
    3. 5.3 Proposed Work
      1. 5.3.1 Decomposing LR and SP
      2. 5.3.2 Enhanced Effective Generative Adversarial Networks
      3. 5.3.3 The Producer and the Discriminator
      4. 5.3.4 Fusion Scheme
    4. 5.4 Experimental Setup
      1. 5.4.1 Applying Enhanced Effective Generative Adversarial Networks
    5. 5.5 Discussion
    6. 5.6 Conclusion
    7. References
  15. 6 Deep Learning Based Parkinson's Disease Prediction System
    1. 6.1 Introduction
    2. 6.2 Literature Survey
    3. 6.3 Proposed Methodology
    4. 6.4 Implementation
      1. 6.4.1 Data Collection
      2. 6.4.2 Data Preprocessing
      3. 6.4.3 Deep Learning Algorithm with RBM
      4. 6.4.4 Training Phase
      5. 6.4.5 Testing Phase
    5. 6.5 Result Analysis
    6. 6.6 Conclusion
    7. References
  16. 7 Non-uniform Data Reduction Technique with Edge Preservation to Improve Diagnostic Visualization of Medical Images
    1. 7.1 Introduction
    2. 7.2 Methodology
      1. 7.2.1 Algorithm of the Data Reduction Algorithm
      2. 7.2.2 Algorithm of the Proposed Data Reduction Method with Enhanced Edge Information
    3. 7.3 Results and Discussion
      1. 7.3.1 Regression Analysis
    4. 7.4 Conclusion
    5. References
  17. 8 A Critical Study on Genetically Engineered Bioweapons and Computer-Based Techniques as Counter Measure
    1. 8.1 Introduction and History
    2. 8.2 Genetically Engineered Pathogen
      1. 8.2.1 Designer Genes
      2. 8.2.2 Binary Bioweapon
      3. 8.2.3 Gene Therapy as Bioweapon
      4. 8.2.4 Stealth Virus
      5. 8.2.5 Hot Swapping Disease
      6. 8.2.6 Designer Disease
    3. 8.3 Computer-Based Detection and Counter Measure Techniques
      1. 8.3.1 Computer and Artificial Intelligence-Based Counter Measure Techniques
      2. 8.3.2 Computer-Assisted Surgery as Counter Measure
      3. 8.3.3 Big Data as Healthcare
      4. 8.3.4 Computer-Assisted Decision Making
      5. 8.3.5 Computer Vision-Based Techniques as Counter Measure
      6. 8.3.6 IoT-Based System as Counter Measure for Bioweapon Against Crop War
    4. 8.4 Conclusion
    5. References
  18. 9 An Automated Hybrid Transfer Learning System for Detection and Segmentation of Tumor in MRI Brain Images with UNet and VGG-19 Network
    1. 9.1 Introduction
    2. 9.2 Related Works
    3. 9.3 Proposed System
    4. 9.4 Experimental Setup and Results
    5. 9.5 Discussion
    6. 9.6 Conclusion and Future Work
    7. References
  19. 10 Deep Learning-Computer Aided Melanoma Detection Using Transfer Learning
    1. 10.1 Introduction
    2. 10.2 Related Research Work
      1. 10.2.1 Benign Sample Image 1
      2. 10.2.2 Benign Sample Image 2
      3. 10.2.3 Melanoma Sample Image 1
      4. 10.2.4 Melanoma Sample Image 2
    3. 10.3 Transfer Learning CAD SCC Model
      1. 10.3.1 Model Summary
      2. 10.3.2 Sample Images
    4. 10.4 Accuracy Results Achieved Through the Proposed Processing
      1. 10.4.1 Loss Results Achieved Through the Proposed Processing
      2. 10.4.2 Confusion Matrix
      3. 10.4.3 Classification Report
    5. 10.5 Conclusion
    6. References
  20. 11 Development of an Agent-Based Interactive Tutoring System for Online Teaching in School Using Classter
    1. 11.1 Introduction
    2. 11.2 Literature Review
    3. 11.3 Methods and Materials
      1. 11.3.1 Standard Intelligent Learning System
    4. 11.4 Implementation
      1. 11.4.1 Student Enrollment
      2. 11.4.2 Standard Intelligent Learning System
      3. 11.4.3 Evaluation System
    5. 11.5 Result and Discussion
      1. 11.5.1 Classter Student Performance Assessment
      2. 11.5.2 RNN Network
    6. 11.6 Conclusion
    7. References
  21. 12 Fusion of Datamining and Artificial Intelligence in Prediction of Hazardous Road Accidents
    1. 12.1 Introduction
    2. 12.2 Related Works on Prediction of Road Accidents
    3. 12.3 Motivation and Problem Statement
    4. 12.4 Proposed Methodology
    5. 12.5 Kaggle and Government Statistical Data
    6. 12.6 Dark Sky
      1. 12.6.1 The Datasets Help to Assume the Constant Weather Conditions on the Whole Day
      2. 12.6.2 The Environmental Factors Depend on Previous Environmental Datasets
      3. 12.6.3 Apriori Algorithm for Road Accident Prediction
      4. 12.6.4 Road Accident Analysis and Classification Using Apriori Algorithm
      5. 12.6.5 Strong Association Rule Mining for Road Accidents
      6. 12.6.6 Naïve Bayes Algorithm for Prevention of Road Accidents
      7. 12.6.7 Sample Example
      8. 12.6.8 Training Dataset
    7. 12.7 Software Used for Prediction
      1. 12.7.1 Jupyter
      2. 12.7.2 Python
      3. 12.7.3 HTML and CSS
    8. 12.8 Results and Discussion
    9. 12.9 Graphical Representation
    10. 12.10 Road Category and Road Features
    11. 12.11 Accidents by Road Environment
    12. 12.12 Accidents by Weather Condition
    13. 12.13 Types of Vehicles Involved in Road Accidents
    14. 12.14 Prevention
      1. 12.14.1 Using AI Techniques to Predict and Prevent Road Accidents
      2. 12.14.2 Machine Learning Process Reduces the Life Risk
      3. 12.14.3 Avoid the Rush and Drunk Driving
    15. 12.15 Limitation
    16. 12.16 Recommendation
    17. 12.17 Significance of the Study
    18. 12.18 Conclusion
    19. References
  22. Index

Product information

  • Title: Machine Learning and IoT for Intelligent Systems and Smart Applications
  • Author(s): Madhumathy P, M Kumar, R. Umamaheswari
  • Release date: November 2021
  • Publisher(s): CRC Press
  • ISBN: 9781000484984