Deep Learning Techniques for Automation and Industrial Applications (eBook)

Dive into this 15-chapter book on 'Deep Learning Techniques' and how its solutions allow computers to learn from experience and understand hierarchy concepts. It provides approaches to deep learning in areas of detection, prediction, and future framework development. This book presents a concise introduction to recent advances in the field of AI, discussing reinforcement learning and applying deep learning techniques to various domains.

Topics discussed in this book:

• The penetration of social media in society and how women living in India are deprived of mobile services. The study generates research on how gender characterizes individuals
• Discusses agriculture's role and the detection of diseases in plants
• Basic deep learning techniques  and how dataset images are compared among model performances
• Talks about image processing fields like surveillance, detection, and recognition. Digital properties have developed dehazing techniques to enhance images and obtain information
• Explains the modification of networking services and how it affects the way of cyberspace communication
• Extensive research on how deep learning is used and challenged by academics, students, and researchers in pointing out accurate metrics and how to increase green AI by reducing its environmental impact

Pramod Singh Rathore is an assistant professor at Manipal University Jaipur, India. He has 45 publications in peer-reviewed National and International Journals. He is a part of the editorial and advisory committee of the Global Journal group. He is a member of various National and International professional societies in the engineering & research field.

 

Sachin Ajuja 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. Starting as a test automation carrier in 2004, he has worked as a leader for Fortune 500 Organizations advising on global compliance, cloud migration, and machine learning.

 

Ajay Khunteta 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 102 papers in international and national journals, guided 44 M.Tech projects, and completed research projects from AICTE New Delhi and DST Rajasthan.

 

Anupam Baliyan 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 Mtech projects and thesis.

 

Abhishek Kumar is an associate professor at the Department of Computer Science, SMIEEE, Chandigarh University, Punjab, India. His research areas focus on artificial intelligence, image processing, computer vision, data mining, and machine learning. He is a session chair and keynote speaker for international conferences and the reviewer for IEEE and Inderscience Journal. He has edited three book series, Quantum Computing with Degruter Germany, Intelligent Energy System with Elsevier, and Sustainable Energy with Nova, USA. In 2018, Kumar earned the CV Raman National Award in the young researcher and faculty category from the URP Group.

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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.