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.
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.
Table of Contents
Chapter 1 Introduction- History Of Ml In Manufacturing
- Challenges In The Realm Of Manufacturing
- Introduction To Technologies
- Introduction To Artificial Intelligence And Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
- Introduction Of Supervised Ml Algorithm In The Realm Of Manufacturing
- Application Of Ml Techniques In Manufacturing
- Areas Of Application To Supervised Machine Learning In Manufacturing And Its Development
- Management Of Method/Machine Level Uncertainties And Adjustments
- Tool Condition Manufacturing
- Process Modelling
- Adaptive Control
- Intelligent Approaches In System-Level Control Of Difficulty, Modification, And Disruption 15 Holonic Manufacturing Systems (Hmss)
- Approaches To Improve The Efficiency Of The Output System Dependent On Agents
- Advantages And Challenges In The Use Of Machine Learning In The Development Of Manufacturing
- Advantages
- Challenges
- Concluding Remarks
- Consent Of Publication
- Conflict Of Interest
- Acknowledgements
- References
- Introduction
- Literature Survey
- Role Of Iot In Pandemic Covid-19
- Benefits Of Arogya Setu App
- Advantages Of Iot
- Information
- Tracking
- Time
- Iot In Manufacturing
- Money
- Better Quality Of Life
- Energy
- Disadvantages Of Iot
- Privacy And Security
- Too Much Reliance On The Technology
- Distraction From The Real World
- Unemployment And Lack Of Craftsmanship
- Conclusion
- Consent Of Publication
- Conflict Of Interest
- Acknowledgements
- References
- Introduction
- Fundamental Concepts
- Industry 4.0 (I4.0)
- Nine Pillars Of Industry 4.0
- Advanced Robotics
- Additive Manufacturing
- Augmented Reality
- Simulation
- Horizontal/Vertical Integration
- Industrial Internet And Internet Of Things
- Cloud
- Cyber Security And Cyber-Physical Systems
- Big Data Analytics
- The Cloud And Industry4.0
- Pay As You Use
- Agility And Flexibility
- Zero Deployment Time
- Cost Reduction
- Shorter Innovation Cycles
- Increase In The Speed And Rate Of Innovation
- Total Cost Of Ownership Optimization
- Rapid Provisioning Of Resources
- Increased Control Over Costs And Savings
- Dynamic Use Of Resources
- Sustainability And Privacy
- Optimization In It Functionality
- Skills
- Applications
- Cloud Manufacturing (Cm)
- Digital Shadow Of Production
- Healthcare
- Benefits Of Cloud In Industry 4.0
- Challenges And Issues
- Intelligent Negotiation Mechanism And Decision Making
- Industrial Wireless Network (Iwn.) Protocols With High Speed
- Manufacturing Specific Big Data And Analytics
- System Analysis And Modelling
- Cyber Security
- Flexible And Modularized Physical Artifacts
- Investment Issues
- Conclusion
- Consent Of Publication
- Conflict Of Interest
- Acknowledgements
- References
- Introduction
- Working Of Deep Neural Network
- Vulnerabilities In Deep Learning Algorithms
- The Security Of Deep Learning Systems
- Security Attacks On Deep Learning Models
- Influence
- Deep Learning For Covid 19 Diagnosis And Prediction
- Challenges Involved
- Conclusion
- Consent Of Publication
- Conflict Of Interest
- Acknowledgements
- References
- Introduction
- Price Of Bitcoin
- Background Information
- Focus On Bitcoin
- The Price Of Bitcoin
- Decentralized System
- Blockchain Technology
- Comparing Traditional Currency And Crypto-Currency
- Future Of Bitcoin
- Goals And Objectives Of Proposed Work
- Literature Review
- Future Scope Of Technology
- Machine Learning
- Improved Customer Services
- Risk Management
- Fraud Prevention
- Network Security
- Scope Of This Work
- Investment Predictions
- Implementation
- Research Methodology
- Application Back-End
- Containerization
- Agile Development
- Testing
- Technologies Used
- Python 3
- The Flask Microframework
- Redis
- Forex-Python
- Mongodb
- Vue.Js
- Chart.Js
- Tensorflow
- System Design
- Currency Data
- Machine Learning
- Final Architecture
- Result
- Usability Testing
- Conclusion
- Evaluation Of Objectives
- Deliver Cryptocurrency Prices To The User
- Provide An Educated Guess As To Future Changes In Prices
- Work Closely With The Given Learning Outcomes For This Work
- Future Work
- Wider Variety Of Cryptocurrencies
- Natural Language Processing
- Long Term Predictions
- Docker
- Consent Of Publication
- Conflict Of Interest
- Acknowledgements
- References
- Introduction
- Review Of Literature
- Data And Methodology
- Bibliometric Analysis
- A. Annual Trend Of Publications
- B. Top Authors, Organizations And Funding Agencies Working In Sfp
- C. Percentage Of Publishers
- D. Country Distribution Analysis
- E. Keywords Analysis
- F. Publication Sources
- Discussion
- Conclusion & Future Work
- Consent Of Publication
- Conflict Of Interest
Author
- Vaishali Mehta
- Dolly Sharma
- Monika Mangla
