Deep Learning Techniques for Automation and Industrial Applications

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

• Publisher: Wiley-Scrivener
• Year: 2024
• Print ISBN: 9781394234240
• eBook ISBN: 9781394234264
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Chapter 1 Text Extraction from Images Using Tesseract
• Chapter 2 Chili Leaf Classification Using Deep Learning Techniques
• Chapter 3 Fruit Leaf Classification Using Transfer Learning Techniques
• Chapter 4 Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models
• Chapter 5 Sarcastic and Phony Contents Detection in Social Media Hindi Tweets
• Chapter 6 Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques
• Chapter 7 HOG and Haar Feature Extraction-Based Security System for Face Detection and Counting
• Chapter 8 A Comparative Analysis of Different CNN Models for Spatial Domain Steganalysis
• Chapter 9 Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques–A Review
• Chapter 10 Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications
• Chapter 11 Green AI: Carbon-Footprint Decoupling System
• Chapter 12 Review of State-of-Art Techniques for Political Polarization from Social Media Network
• Chapter 13 Collaborative Design and Case Analysis of Mobile Shopping Apps: A Deep Learning Approach
• Chapter 14 Exploring the Potential of Machine Learning and Deep Learning for COVID-19 Detection
• Index
• EULA