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 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:
Preface xiii 1 Text Extraction from Images Using Tesseract 1 Santosh Kumar, Nilesh Kumar Sharma, Mridul Sharma and Nikita Agrawal 2 Chili Leaf Classification Using Deep Learning Techniques 19 Chenchupalli Chathurya, Diksha Sachdeva and Mamta Arora 3 Fruit Leaf Classification Using Transfer Learning Techniques 31 Taha Siddiqui, Surbhit Chopra and Mamta Arora 4 Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models 45 Abhishek Maurya, Akashdeep and Rohit Kumar 5 Sarcastic and Phony Contents Detection in Social Media Hindi Tweets 69 Surbhi Sharma and Nisheeth Joshi 6 Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques 85 Pushpa Koranga, Ravindra Singh Koranga, Sumitra Singar and Sandeep Gupta 7 HOG and Haar Feature Extraction-Based Security System for Face Detection and Counting 99 Prachi Soni and Viplav Soni 8 A Comparative Analysis of Different CNN Models for Spatial Domain Steganalysis 109 Ankita Gupta, Rita Chhikara and Prabha Sharma 9 Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques--A Review 129 Dineshkumar Singh and Vishnu Sharma 10 Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications 151 Inam Ul Haq, Gursimran Kaur and Adil Husain Rather 11 Green AI: Carbon-Footprint Decoupling System 179 Bindiya Jain and Shikha Sharma 12 Review of State-of-Art Techniques for Political Polarization from Social Media Network 199 Akshita Bhatnagar and B.K. Sharma 13 Collaborative Design and Case Analysis of Mobile Shopping Apps: A Deep Learning Approach 223 Santosh Kumar, Vipul Jain, Abhishek Bairwa and Pradeep Saharan 14 Exploring the Potential of Machine Learning and Deep Learning for COVID-19 Detection 235 Saimul Bashir, Faisal Firdous and Syed Zoofa Rufai References 253 Index 257