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.
Author(s): Vaishali Mehta, Dolly Sharma, Monika Mangla, Anita Gehlot, Rajesh Singh, Sergio Márquez Sánchez
Publisher: Bentham Science Publishers
Year: 2022
Language: English
Pages: 228
City: Singapore
Cover
Title
Copyright
End User License Agreement
Contents
Preface
ORGANIZATION OF THE BOOK
List of Contributors
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
Nipun R. Navadia1,*, Gurleen Kaur1, Harshit Bhadwaj2, Taranjeet Singh2, Yashpal Singh2, Indu Malik3, Arpit Bhardwaj4 and Aditi Sakalle5
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
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
Application of IoT–A Survey
Richa Mishra1,* and Tushar1
INTRODUCTION
IoT in MANUFACTURING
LITERATURE SURVEY
ROLE OF IOT IN PANDEMIC COVID-19
Benefits of AROGYA SETU App
Advantages of IoT
INFORMATION
Tracking
Time
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
Cloud Industry Application 4.0: Challenges and Benefits
Abhikriti Narwal1 and Sunita Dhingra1
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
Uses And Challenges of Deep Learning Models for Covid-19 Diagnosis and Prediction
Vaishali M. Wadhwa1,*, Monika Mangla2, Rattandeep Aneja1, Mukesh Chawla1 and Achyuth Sarkar3
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
Currency Trend Prediction using Machine Learning
Deepak Yadav1 and Dolly Sharma1,*
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
A Bibliometric Analysis of Fault Prediction System using Machine Learning Techniques
Mudita Uppal1, Deepali Gupta1 and Vaishali Mehta2
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
ACKNOWLEDGEMENTS
REFERENCES
COVID-19 Forecasting using Machine Learning Models
Vishal Dhull1,#, Sumindar Kaur Saini1,*, #, Sarbjeet Singh1 and Akashdeep Sharma1
INTRODUCTION
Dataset Description
Literature Review
Methodology
Linear Regression (LR)
Polynomial Regression (PR)
Holt’s Linear Model Prediction
Holt’s Winter Model Prediction
Autoregressive (AR) Model
Moving-average Model (MA model)
ARIMA Model
SARIMA Model
SVM Model
Facebook's Prophet Model
RESULTS AND DISCUSSION
Experimental Setup
Performance Metrics
MAPE
PPMCC
RMSE
Performance Analysis
Linear Regression Prediction
Polynomial Regression Prediction
Support Vector Machine(SVM) Regression Prediction
Auto-regressive(AR) Model Prediction
Moving-average(MA) Model Prediction
Holt’s Linear Model Prediction
Holt’s Winter Model Prediction
ARIMA Model Prediction
SARIMA Model Prediction
Facebook's Prophet Model
DISCUSSION
CONCLUSION AND FUTURE SCOPE
CONSENT OF PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
An Optimized System for Sentiment Analysis using Twitter Data
Stuti Mehla1,* and Sanjeev Rana1
INTRODUCTION
LITERATURE REVIEW
SYSTEM MODEL
INPUT PHASE
REST APIS
PREPROCESSING PHASE
FEATURE EXTRACTION PHASE
OPTIMIZATION PHASE
CLASSIFICATION PHASE
WORKING
RESULTS
Precision
Recall
CONCLUSION
CONSENT OF PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Applications of AI in Agriculture
Taranjeet Singh1,*, Harshit Bhadwaj2, Lalita Verma2, Nipun R Navadia3, Devendra Singh1, Aditi Sakalle4 and Arpit Bhardwaj5
1. INTRODUCTION
2. USAGE OF ARTIFICIAL INTELLIGENCE IN AGRICULTURE
Pre-harvesting
Pesticides and Disease Detection
Harvesting
Post- Harvesting
Intello Labs
Microsoft India
AI-Based Machines in Agriculture
3. MOBILE APPS FOR PERFORMING AGRICULTURAL TASKS
Plantix
Prospera
The Sowing App
Smart Greenhouses
Deep Learning Overview
CONCLUDING REMARKS
CONSENT OF PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENTS
REFERENCES
Subject Index
