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.
Cuprins
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
Despre autor
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, Ph D, is a professor in the Department of Computer Science, Chandigarh University, Punjab, India. He has guided several ME and Ph D 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, Ph D, 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, Ph D, is Dean of Academic Planning and Research, Galgotias University, 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.
Abhishek Kumar, Ph D, is an associate professor in the Faculty of Engineering, Manipal University, Jaipur, Rajasthan, India and is currently a Post-Doctoral Fellow in Ingenium Research Group Lab, Universidad De Castilla- La Mancha, Ciudad Real, Spain. He has more than 170 publications in peer-reviewed national and international journals and conferences.
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