Challenges and Opportunities for Deep Learning Applications in Industry 4.0

Description

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. 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