Challenges and Opportunities for Deep Learning Applications in Industry 4.0

Welcome
Table of Content
Title
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    General:
PREFACE
    ORGANIZATION OF THE BOOK
List of Contributors
Challenges and Opportunities for Deep Learning Applications in Industry 4.0
    Abstract
    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
    Abstract
    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
    Abstract
    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
    ABSTRACT:
    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
    Abstract
    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
    Abstract
    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
    Abstract
    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
    Abstract
    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
    Abstract
    INTRODUCTION
    USAGE OF ARTIFICIAL INTELLIGENCE IN AGRICULTURE
        Pre-harvesting
        Pesticides and Disease Detection
        Harvesting
        Post- Harvesting
        Intello Labs
        Microsoft India
        AI-Based Machines in Agriculture
    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