Deep learning techniques for automation and industrial applications

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 le...

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Bibliographic Details
Other Authors: Rathore, Pramod Singh (Editor), Ahuja, Sachin, Burri, Srinivasa Rao, Khunteta, Ajay
Format: eBook
Language:English
Published: Hoboken, NJ John Wiley & Sons, Inc. 2024
Subjects:
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • 1.7.2 Hardware Requirements
  • 1.8 Testing
  • 1.9 Result
  • 1.10 Future Scope
  • 1.11 Conclusion
  • References
  • Chapter 2 Chili Leaf Classification Using Deep Learning Techniques
  • 2.1 Introduction
  • 2.2 Objectives
  • 2.3 Literature Survey
  • 2.4 About the Dataset
  • 2.5 Methodology
  • 2.6 Result
  • 2.7 Conclusion and Future Work
  • References
  • Chapter 3 Fruit Leaf Classification Using Transfer Learning Techniques
  • 3.1 Introduction
  • 3.2 Literature Review
  • 3.3 Methodology
  • 3.3.1 Image Preprocessing
  • 3.3.2 Data Augmentation
  • 3.3.3 Deep Learning Models
  • 3.3.4 Accuracy Chart
  • 5.2.2 Literature Review of Sarcasm Detection with Machine Learning Algorithms and Based on Manual Feature Engineering Approach
  • 5.3 Research Gap
  • 5.4 Objective
  • 5.5 Proposed Methodology
  • 5.6 Expected Outcomes
  • References
  • Chapter 6 Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques
  • 6.1 Introduction
  • 6.2 Formation of a Haze Model
  • 6.3 Different Techniques of Single-Image Dehazing
  • 6.3.1 Contrast Enhancement
  • 6.3.2 Dark Channel Prior
  • 6.3.3 Color Attenuation Prior
  • 6.3.4 Fusion Techniques
  • 6.3.5 Deep Learning
  • 3.3.5 Accuracy and Loss Graph
  • 3.4 Conclusion and Future Work
  • References
  • Chapter 4 Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models
  • 4.1 Introduction
  • 4.2 Motivation and Contribution
  • 4.2.1 Related Work
  • 4.3 Methodology
  • 4.3.1 Pre-Trained Models
  • 4.3.2 Dataset
  • 4.3.3 Training Processes
  • 4.4 Experiments and Results
  • 4.4.1 VGG Family
  • 4.4.2 ResNet Family
  • 4.4.2.1 ResNet101
  • 4.4.2.2 ResNet152
  • 4.4.3 MobileNet Family
  • 4.4.4 Inception Family
  • 4.4.5 Xception Family
  • 4.4.6 DenseNet Family
  • 4.4.7 NasNet Family
  • 4.4.8 EfficientNet Family
  • 4.4.9 ResNet Version 2
  • 4.5 Conclusion
  • References
  • Chapter 5 Sarcastic and Phony Contents Detection in Social Media Hindi Tweets
  • 5.1 Introduction
  • 5.1.1 Sarcasm in Social Media Hindi Tweets
  • 5.2 Literature Review
  • 5.2.1 Literature Review of Sarcasm Detection Based on Data Analysis Without Machine Learning Algorithms
  • 5.2.1.1 Other Related Works without Machine Learning Algorithms for Sarcasm Detection
  • Cover
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 Text Extraction from Images Using Tesseract
  • 1.1 Introduction
  • 1.1.1 Areas
  • 1.1.2 Why Text Extraction?
  • 1.1.3 Applications of OCR
  • 1.2 Literature Review
  • 1.3 Development Areas
  • 1.3.1 React JavaScript (JS)
  • 1.3.2 Flask
  • 1.4 Existing System
  • 1.5 Enhancing Text Extraction Using OCR Tesseract
  • 1.6 Unified Modeling Language (UML) Diagram
  • 1.6.1 Use Case Diagram
  • 1.6.2 Model Architecture
  • 1.6.3 Pseudocode
  • 1.7 System Requirements
  • 1.7.1 Software Requirements