book

Deep Learning Techniques for Automation and Industrial Applications

Name: Deep Learning Techniques for Automation and Industrial Applications
ISBN: 9781394234240

by Pramod Singh Rathore, Sachin Ahuja, Srinivasa Rao Burri, Ajay Khunteta, Anupam Baliyan, Abhishek Kumar

July 2024

Intermediate to advanced

288 pages

7h 14m

English

Wiley-Scrivener

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Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Text Extraction from Images Using Tesseract
1.1 Introduction1.2 Literature Review1.3 Development Areas1.4 Existing System1.5 Enhancing Text Extraction Using OCR Tesseract1.6 Unified Modeling Language (UML) Diagram1.7 System Requirements1.8 Testing1.9 Result1.10 Future Scope1.11 ConclusionReferences
2 Chili Leaf Classification Using Deep Learning Techniques
2.1 Introduction2.2 Objectives2.3 Literature Survey2.4 About the Dataset2.5 Methodology2.6 Result2.7 Conclusion and Future WorkReferences
3 Fruit Leaf Classification Using Transfer Learning Techniques
3.1 Introduction3.2 Literature Review3.3 Methodology3.4 Conclusion and Future WorkReferences
4 Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models
4.1 Introduction4.2 Motivation and Contribution4.3 Methodology4.4 Experiments and Results4.5 ConclusionReferences

5 Sarcastic and Phony Contents Detection in Social Media Hindi Tweets
5.1 Introduction5.2 Literature Review5.3 Research Gap5.4 Objective5.5 Proposed Methodology5.6 Expected OutcomesReferences
6 Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques
6.1 Introduction6.2 Formation of a Haze Model6.3 Different Techniques of Single-Image Dehazing6.4 Results and Discussions6.5 Output for Synthetic Scenes6.6 Output for Real Scenes6.7 ConclusionsReferences
7 HOG and Haar Feature Extraction-Based Security System for Face Detection and Counting
7.1 Introduction7.2 Literature Survey7.3 Proposed Work7.4 Experiments and Results7.5 Conclusion and Scope of Future WorkReferences
8 A Comparative Analysis of Different CNN Models for Spatial Domain Steganalysis
8.1 Introduction8.2 General Framework8.3 Experimental Results and Analysis8.4 Conclusion and DiscussionAcknowledgmentsReferences
9 Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques–A Review
9.1 Introduction9.2 Determination of Groundwater Potential (GWP) Parameters9.3 GWP Determination: Methods and Techniques9.4 GWP Output: Applications9.5 GWP Research Gaps: Future Research Areas9.6 ConclusionReferences
10 Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications
10.1 Introduction10.2 Data Collection and Preprocessing10.3 Loading a Pre-Trained Model for Fruit Leaf Classification Using Transfer Learning10.4 Training and Evaluation10.5 Applications in Automation and Industry10.6 Conclusion10.7 Future WorkReferences
11 Green AI: Carbon-Footprint Decoupling System
11.1 Introduction11.2 CO2 Emissions in Sectors11.3 Heating and Cooking Emissions11.4 Automobile Systems Emission11.5 Power Systems Emission11.6 Total CO2 Emission11.7 Green AI With a Control Strategy of Carbon Emission11.8 Green Software11.9 Conclusion11.10 Future Scope and LimitationReferences
12 Review of State-of-Art Techniques for Political Polarization from Social Media Network
12.1 Introduction12.2 Political Polarization12.3 State-of-the-Art Techniques12.4 Literature Survey12.5 ConclusionReferences
13 Collaborative Design and Case Analysis of Mobile Shopping Apps: A Deep Learning Approach
13.1 Introduction13.2 Personalized Interaction Design Framework for Mobile Shopping13.3 Case Analysis13.4 ConclusionsReferences
14 Exploring the Potential of Machine Learning and Deep Learning for COVID-19 Detection
14.1 Introduction14.2 Supervised Learning Techniques14.3 Unsupervised Learning Techniques14.4 Deep Learning Techniques14.5 Reinforcement Learning Techniques14.6 Comparison of Machine Learning and Deep Learning Techniques14.7 Challenges and Limitations14.8 Conclusion and Future DirectionsReferences
Index
End User License Agreement

Content preview from Deep Learning Techniques for Automation and Industrial Applications

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

Saimul Bashir¹^*, Faisal Firdous² and Syed Zoofa Rufai¹

¹Computer Science and Engineering, Chandigarh University, Punjab, India

²Computer Science and Engineering, iNurture Education Solutions Pvt Ltd, Bangalore, India

Abstract

This book chapter presents an extensive review of the application of machine learning and deep learning methods in detecting COVID-19. It emphasizes the significance of early and accurate diagnosis in effectively managing the disease during the COVID-19 pandemic. The chapter encompasses various aspects of machine learning and deep learning techniques, including supervised and unsupervised learning, convolutional neural networks, recurrent neural networks, reinforcement learning, and a comparative analysis of machine learning and deep learning methods for COVID-19 detection.

Moreover, the chapter discusses the challenges and limitations associated with employing machine learning and deep learning techniques for COVID-19 detection. It also explores future research directions in this domain. The primary objective of this chapter is to offer a comprehensive understanding of the potential applications, limitations, and challenges of machine learning and deep learning techniques in COVID-19 disease detection. It aims to serve as a valuable resource for researchers and practitioners involved in the field of COVID-19 diagnosis and machine learning.

Keywords: COVID-19, deep ...

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Deep Learning Techniques for Automation and Industrial Applications

by Pramod Singh Rathore, Sachin Ahuja, Srinivasa Rao Burri, Ajay Khunteta, Anupam Baliyan, Abhishek Kumar