Editors: Vaishali Mehta, Dolly Sharma, Monika Mangla , Anita Gehlot

Challenges and Opportunities for Deep Learning Applications in Industry 4.0

eBook: US $69 Special Offer (PDF + Printed Copy): US $110
Printed Copy: US $76
Library License: US $276
ISBN: 978-981-5036-07-7 (Print)
ISBN: 978-981-5036-06-0 (Online)
Year of Publication: 2022
DOI: 10.2174/97898150360601220101


The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods.

The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications.

Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.

The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.


- Pp. i-iii (3)
Vaishali Mehta, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh, Sergio Marquez Sanchez
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List of Contributors

- Pp. iv-v (2)

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Challenges and Opportunities for Deep Learning Applications in Industry 4.0

- Pp. 1-24 (24)
Nipun R. Navadia*, Gurleen Kaur, Harshit Bhadwaj, Taranjeet Singh, Yashpal Singh, Indu Malik, Arpit Bhardwaj, Aditi Sakalle
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Application of IoT–A Survey

- Pp. 25-40 (16)
Richa Mishra*, Tushar
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Cloud Industry Application 4.0: Challenges and Benefits

- Pp. 41-66 (26)
Abhikriti Narwal, Sunita Dhingra
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Uses And Challenges of Deep Learning Models for Covid-19 Diagnosis and Prediction

- Pp. 67-84 (18)
Vaishali M. Wadhwa*, Monika Mangla, Rattandeep Aneja, Mukesh Chawla, Achyuth Sarkar
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Currency Trend Prediction using Machine Learning

- Pp. 85-108 (24)
Deepak Yadav, Dolly Sharma*
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A Bibliometric Analysis of Fault Prediction System using Machine Learning Techniques

- Pp. 109-130 (22)
Mudita Uppal*, Deepali Gupta, Vaishali Mehta
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COVID-19 Forecasting using Machine Learning Models

- Pp. 131-158 (28)
Vishal Dhull, Sumindar Kaur Saini*, Sarbjeet Singh, Akashdeep Sharma
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An Optimized System for Sentiment Analysis using Twitter Data

- Pp. 159-180 (22)
Stuti Mehla*, Sanjeev Rana
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Applications of AI in Agriculture

- Pp. 181-203 (23)
Taranjeet Singh*, Harshit Bhadwaj, Lalita Verma, Nipun R Navadia, Devendra Singh, Aditi Sakalle, Arpit Bhardwaj
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Subject Index

- Pp. 204-213 (10)

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