Deep Learning Techniques for Automation and Industrial Applications

Deep Learning Techniques for Automation and Industrial Applications

by Pramod Singh Rathore, Sachin Ahuja, Srinivasa Rao Burri, Ajay Khunteta, Anupam Baliyan, Abhishek Kumar
Released July 2024
Publisher(s): Wiley-Scrivener
ISBN: 9781394234240

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Book description

This book provides state-of-the-art approaches to deep learning in areas of detection and prediction, as well as future framework development, building service systems and analytical aspects in which artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used.

Deep learning algorithms and techniques are found to be useful in various areas, such as automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delays in children. “Deep Learning Techniques for Automation and Industrial Applications” presents a concise introduction to the recent advances in this field of artificial intelligence (AI). The broad-ranging discussion covers the algorithms and applications in AI, reasoning, machine learning, neural networks, reinforcement learning, and their applications in various domains like agriculture, manufacturing, and healthcare. Applying deep learning techniques or algorithms successfully in these areas requires a concerted effort, fostering integrative research between experts from diverse disciplines from data science to visualization.

This book provides state-of-the-art approaches to deep learning covering detection and prediction, as well as future framework development, building service systems, and analytical aspects. For all these topics, various approaches to deep learning, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms, are explained.

Audience

The book will be useful to researchers and industry engineers working in information technology, data analytics network security, and manufacturing. Graduate and upper-level undergraduate students in advanced modeling and simulation courses will find this book very useful.

Table of contents

  1. Cover
  2. Table of Contents
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Preface
  7. 1 Text Extraction from Images Using Tesseract
    1. 1.1 Introduction
    2. 1.2 Literature Review
    3. 1.3 Development Areas
    4. 1.4 Existing System
    5. 1.5 Enhancing Text Extraction Using OCR Tesseract
    6. 1.6 Unified Modeling Language (UML) Diagram
    7. 1.7 System Requirements
    8. 1.8 Testing
    9. 1.9 Result
    10. 1.10 Future Scope
    11. 1.11 Conclusion
    12. References
  8. 2 Chili Leaf Classification Using Deep Learning Techniques
    1. 2.1 Introduction
    2. 2.2 Objectives
    3. 2.3 Literature Survey
    4. 2.4 About the Dataset
    5. 2.5 Methodology
    6. 2.6 Result
    7. 2.7 Conclusion and Future Work
    8. References
  9. 3 Fruit Leaf Classification Using Transfer Learning Techniques
    1. 3.1 Introduction
    2. 3.2 Literature Review
    3. 3.3 Methodology
    4. 3.4 Conclusion and Future Work
    5. References
  10. 4 Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models
    1. 4.1 Introduction
    2. 4.2 Motivation and Contribution
    3. 4.3 Methodology
    4. 4.4 Experiments and Results
    5. 4.5 Conclusion
    6. References
  11. 5 Sarcastic and Phony Contents Detection in Social Media Hindi Tweets
    1. 5.1 Introduction
    2. 5.2 Literature Review
    3. 5.3 Research Gap
    4. 5.4 Objective
    5. 5.5 Proposed Methodology
    6. 5.6 Expected Outcomes
    7. References
  12. 6 Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques
    1. 6.1 Introduction
    2. 6.2 Formation of a Haze Model
    3. 6.3 Different Techniques of Single-Image Dehazing
    4. 6.4 Results and Discussions
    5. 6.5 Output for Synthetic Scenes
    6. 6.6 Output for Real Scenes
    7. 6.7 Conclusions
    8. References
  13. 7 HOG and Haar Feature Extraction-Based Security System for Face Detection and Counting
    1. 7.1 Introduction
    2. 7.2 Literature Survey
    3. 7.3 Proposed Work
    4. 7.4 Experiments and Results
    5. 7.5 Conclusion and Scope of Future Work
    6. References
  14. 8 A Comparative Analysis of Different CNN Models for Spatial Domain Steganalysis
    1. 8.1 Introduction
    2. 8.2 General Framework
    3. 8.3 Experimental Results and Analysis
    4. 8.4 Conclusion and Discussion
    5. Acknowledgments
    6. References
  15. 9 Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques–A Review
    1. 9.1 Introduction
    2. 9.2 Determination of Groundwater Potential (GWP) Parameters
    3. 9.3 GWP Determination: Methods and Techniques
    4. 9.4 GWP Output: Applications
    5. 9.5 GWP Research Gaps: Future Research Areas
    6. 9.6 Conclusion
    7. References
  16. 10 Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications
    1. 10.1 Introduction
    2. 10.2 Data Collection and Preprocessing
    3. 10.3 Loading a Pre-Trained Model for Fruit Leaf Classification Using Transfer Learning
    4. 10.4 Training and Evaluation
    5. 10.5 Applications in Automation and Industry
    6. 10.6 Conclusion
    7. 10.7 Future Work
    8. References
  17. 11 Green AI: Carbon-Footprint Decoupling System
    1. 11.1 Introduction
    2. 11.2 CO2 Emissions in Sectors
    3. 11.3 Heating and Cooking Emissions
    4. 11.4 Automobile Systems Emission
    5. 11.5 Power Systems Emission
    6. 11.6 Total CO2 Emission
    7. 11.7 Green AI With a Control Strategy of Carbon Emission
    8. 11.8 Green Software
    9. 11.9 Conclusion
    10. 11.10 Future Scope and Limitation
    11. References
  18. 12 Review of State-of-Art Techniques for Political Polarization from Social Media Network
    1. 12.1 Introduction
    2. 12.2 Political Polarization
    3. 12.3 State-of-the-Art Techniques
    4. 12.4 Literature Survey
    5. 12.5 Conclusion
    6. References
  19. 13 Collaborative Design and Case Analysis of Mobile Shopping Apps: A Deep Learning Approach
    1. 13.1 Introduction
    2. 13.2 Personalized Interaction Design Framework for Mobile Shopping
    3. 13.3 Case Analysis
    4. 13.4 Conclusions
    5. References
  20. 14 Exploring the Potential of Machine Learning and Deep Learning for COVID-19 Detection
    1. 14.1 Introduction
    2. 14.2 Supervised Learning Techniques
    3. 14.3 Unsupervised Learning Techniques
    4. 14.4 Deep Learning Techniques
    5. 14.5 Reinforcement Learning Techniques
    6. 14.6 Comparison of Machine Learning and Deep Learning Techniques
    7. 14.7 Challenges and Limitations
    8. 14.8 Conclusion and Future Directions
    9. References
  21. Index
  22. End User License Agreement

Product information

  • Title: Deep Learning Techniques for Automation and Industrial Applications
  • Author(s): Pramod Singh Rathore, Sachin Ahuja, Srinivasa Rao Burri, Ajay Khunteta, Anupam Baliyan, Abhishek Kumar
  • Release date: July 2024
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781394234240