Deep Learning Techniques for Automation and Industrial Applications.

Format:

Book

Author/Creator:

Rathore, Pramod Singh.

Contributor:

Ahuja, Sachin.

Burri, Srinivasa Rao.

Khunteta, Ajay.

Baliyan, Anupam.

Kumar, Abhishek.

Language:

English

Subjects (All):

Automation.

Physical Description:

1 online resource (280 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

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.

Contents:

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

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

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

3.3.5 Accuracy and Loss Graph

3.4 Conclusion and Future Work

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

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

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

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

6.4 Results and Discussions

6.5 Output for Synthetic Scenes

6.6 Output for Real Scenes

6.7 Conclusions

Chapter 7 HOG and Haar Feature Extraction-Based Security System for Face Detection and Counting

7.1 Introduction

7.1.1 Need for a Better Security System

7.2 Literature Survey

7.3 Proposed Work

7.3.1 Tools Used

7.3.2 Algorithm of the Proposed System

7.3.2.1 HOG-Based Individual Counting

7.3.2.2 Haar-Based Individual Counting

7.3.2.3 Combination of HOG and Haar

7.3.2.4 AdaBoost Learning Technique

7.3.2.5 KLT Tracker

7.4 Experiments and Results

7.5 Conclusion and Scope of Future Work

Chapter 8 A Comparative Analysis of Different CNN Models for Spatial Domain Steganalysis

8.1 Introduction

8.2 General Framework

8.2.1 Dataset

8.2.2 Deep Learning CNN Models

8.2.2.1 XuNet.

8.2.2.2 Pretrained Networks

8.3 Experimental Results and Analysis

8.4 Conclusion and Discussion

Acknowledgments

Chapter 9 Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques-A Review

9.1 Introduction

9.1.1 Why is It Difficult to Measure Subsurface Groundwater?

9.1.2 What are High Level Tasks Involved in Groundwater Measurement?

9.2 Determination of Groundwater Potential (GWP) Parameters

9.2.1 Groundwater Potential (GWP) Parameters

9.2.2 Analysis of the Key GWP Parameters

9.3 GWP Determination: Methods and Techniques

9.4 GWP Output: Applications

9.5 GWP Research Gaps: Future Research Areas

9.6 Conclusion

Chapter 10 Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications

10.1 Introduction

10.1.1 Overview of Fruit Leaf Classification and Its Relevance in Automation and Industrial Applications

10.1.2 Challenges of Building a Classification Model from Scratch

10.1.3 Introduction to Transfer Learning as a Solution

10.1.4 Overview of Popular Pre-Trained Models

10.1.4.1 Visual Geometry Group

10.1.4.2 Residual Network

10.1.4.3 Inception

10.2 Data Collection and Preprocessing

10.2.1 Importance of Data Collection and Preprocessing

10.2.2 Data Augmentation in Fruit Leaf Classification

10.2.3 Normalization and Resizing in Fruit Leaf Classification

10.3 Loading a Pre-Trained Model for Fruit Leaf Classification Using Transfer Learning

10.3.1 Code Examples for Implementing Transfer Learning Using TensorFlow

10.4 Training and Evaluation

10.4.1 Explanation of Training and Evaluation Process

10.4.2 Metrics for Measuring Model Performance

10.5 Applications in Automation and Industry

10.5.1 Benefits of Using Transfer Learning in Automation and Industrial Settings.

10.5.2 Case Studies of Fruit Leaf Classification in Industry Using Transfer Learning

10.6 Conclusion

10.7 Future Work

Chapter 11 Green AI: Carbon-Footprint Decoupling System

11.1 Introduction

11.2 CO2 Emissions in Sectors

11.3 Heating and Cooking Emissions

11.4 Automobile Systems Emission

11.5 Power Systems Emission

11.5.1 Map

11.6 Total CO2 Emission

11.6.1 Relationship Between Tables

11.6.2 Group by Clause

11.6.3 Offshore Wind Storage Integration Method

11.6.4 Offshore Floating Wind and Power Generation Technology (OFWPP)

11.6.5 Wind Power Plant for Storage Mixing

11.6.6 The Effect on the Environment when Using Battery Storage

11.7 Green AI With a Control Strategy of Carbon Emission

11.8 Green Software

11.9 Conclusion

11.10 Future Scope and Limitation

Chapter 12 Review of State-of-Art Techniques for Political Polarization from Social Media Network

12.1 Introduction

12.1.1 Social Media

12.2 Political Polarization

12.2.1 Identification of the Parties

12.2.2 Definition of Political Ideology

12.2.3 Voting Conduct (Definition)

12.2.4 Definition of Policy Positions

12.2.5 Definition of Affective Polarization

12.2.6 Identifiability of Parties (Definition)

12.2.7 Definition of Political Ideology

12.2.8 Definition of Voting Behavior

12.2.9 Policy Positions (Definition)

12.2.10 Party Sorting

12.2.11 Affective Polarization (Definition)

12.3 State-of-the-Art Techniques

12.3.1 Word Embedding (WE)

12.3.2 Customary Models

12.3.3 Models of Deep Neural Networks (DNN)

12.3.4 Single and Hybrid ML Techniques

12.3.4.1 Single Methods

12.3.4.2 Hybrid Approaches

12.3.5 Multitask Learning (V)

12.3.5.1 Learning-Related Problem

12.3.5.2 Multi-Task Learning MTL

12.3.5.3 Architectures for Multiple Tasks.

12.3.5.4 Two MTL Deep Learning Methods

12.3.6 Techniques for Deep Learning

12.4 Literature Survey

12.5 Conclusion

Chapter 13 Collaborative Design and Case Analysis of Mobile Shopping Apps: A Deep Learning Approach

13.1 Introduction

13.1.1 Basic Rules in Shopping App Interaction Design

13.1.1.1 User-Centered Design Rules

13.1.2 Visual Interface Consistency

13.2 Personalized Interaction Design Framework for Mobile Shopping

13.2.1 Modelized Interaction Information Framework

13.2.2 Interactive Design Path Analysis

13.2.3 Optimization Design in the Page System

13.3 Case Analysis

13.4 Conclusions

Chapter 14 Exploring the Potential of Machine Learning and Deep Learning for COVID-19 Detection

14.1 Introduction

14.2 Supervised Learning Techniques

14.3 Unsupervised Learning Techniques

14.4 Deep Learning Techniques

14.5 Reinforcement Learning Techniques

14.6 Comparison of Machine Learning and Deep Learning Techniques

14.7 Challenges and Limitations

14.8 Conclusion and Future Directions

Index

EULA.

Notes:

Description based upon print version of record.

6.4 Results and Discussions

Description based on publisher supplied metadata and other sources.

ISBN:

9781394234257

1394234252

9781394234271

1394234279

9781394234264

1394234260

OCLC:

1436831895