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Deep Learning Techniques for Automation and Industrial Applications

Edited by Pramod Singh Rathore, Sachin Ahuja, Srinivasa Rao Burri, Ajay Khunteta, Anupam Baliyan and Abhishek Kumar
Copyright: 2024   |   Status: Published
ISBN: 9781394234240  |  Hardcover  |  
278 pages
Price: $225 USD
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One Line 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.

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.

Description
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.

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Author / Editor Details
Pramod Singh Rathore is an assistant professor in the Department of Computer and Communication Engineering, Manipal University Jaipur, India. He has teaching experience of more than 10 years and has 45 publications in peer-reviewed national and international journals.

Sachin Ahuja, PhD, is a professor in the Department of Computer Science, Chandigarh University, Punjab, India. He has guided several ME and PhD scholars in artificial intelligence, machine learning, and data mining.

Srinivasa Rao Burri is a senior software engineering manager at Western Union, Denver, Colorado. He completed an MS degree in software development from Boston University. He also has received his certifications in Data Science and Machine Learning from Stanford University, Harvard University and Johns Hopkins University. He started his career as a test automation architect in 2004, and has since worked as a leader for many Fortune 500 Organizations advising them on global compliance, data privatization, cloud migration, and AI & ML. He has published multiple articles in international journals.

Ajay Khunteta, PhD, is a dean and professor of computer science and engineering, Poornima University, Jaipur, Rajasthan, India. His research focuses on AI, machine learning, and distributing systems. He has published more than 100 articles in international and national journals and guided 44 M.Tech projects.

Anupam Baliyan, PhD, is a professor in the Department of Computer Science, Chandigarh University, Punjab, India. His research focuses on artificial intelligence, computer networks, computer vision, and machine learning. Along with being a chair and keynote speaker at international conferences, Baliyan has guided more than 20 M.Tech projects and theses.

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Table of Contents
Preface
1. Text Extraction from Images Using Tesseract
Santosh Kumar, Nilesh Kumar Sharma, Mridul Sharma and Nikita Agrawal
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
2. Chili Leaf Classification Using Deep Learning Techniques
Chenchupalli Chathurya, Diksha Sachdeva and Mamta Arora
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
3. Fruit Leaf Classification Using Transfer Learning Techniques
Taha Siddiqui, Surbhit Chopra and Mamta Arora
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
References
4. Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models
Abhishek Maurya, Akashdeep and Rohit Kumar
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
5. Sarcastic and Phony Contents Detection in Social Media Hindi Tweets
Surbhi Sharma and Nisheeth Joshi
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
References
6. Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques
Pushpa Koranga, Ravindra Singh Koranga, Sumitra Singar and Sandeep Gupta
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
References
7. HOG and Haar Feature Extraction-Based Security System for Face Detection and Counting
Prachi Soni and Viplav Soni
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
References
8. A Comparative Analysis of Different CNN Models for Spatial Domain Steganalysis
Ankita Gupta, Rita Chhikara and Prabha Sharma
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
References
9. Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques–A Review
Dineshkumar Singh and Vishnu Sharma
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
References
10. Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications
Inam Ul Haq, Gursimran Kaur and Adil Husain Rather
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
References
11. Green AI: Carbon-Footprint Decoupling System
Bindiya Jain and Shikha Sharma
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
References
12. Review of State-of-Art Techniques for Political Polarization from Social Media Network
Akshita Bhatnagar and B.K. Sharma
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
References
13. Collaborative Design and Case Analysis of Mobile Shopping Apps: A Deep Learning Approach
Santosh Kumar, Vipul Jain, Abhishek Bairwa and Pradeep Saharan
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
References
14. Exploring the Potential of Machine Learning and Deep Learning for COVID-19 Detection
Saimul Bashir, Faisal Firdous and Syed Zoofa Rufai
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
References
Index

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