کتاب Cloud Analytics for Industry 4.0

کتاب Cloud Analytics for Industry 4.0 (تحلیل ابری برای صنعت 4) حاصل نوشته‌ی چندین متخصص حوزه‌ی رایانش ابری در زمینه‌ی محاسبات ابری و کاربردهای آن است. این کتاب در چندین بخش متنوع به شرح کاربرد رایانش ابری در زمینه‌های مختلف خواهد پرداخت.

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مقدمه‌ای بر کتاب Cloud Analytics for Industry 4.0:

مجموعه کتاب «کاربردهای محاسبات هوشمند» بستری را برای محققان، دانشگاهیان و متخصصان فراهم می‌کند تا در مورد تحقیقات اخیر علم داده‌های نظری و کاربردی و فناوری‌های محاسباتی با توجه ویژه به کاربردهای احتمالی چنین فناوری‌هایی در صنعت، به ویژه در رشته مهندسی مکانیک و صنایع تبادل نظر کنند.

این مجموعه به عنوان یک منبع ارزشمند برای دانشجویان فارغ التحصیل، کارشناسی ارشد، دکترا، محققان، دانشگاهیان و متخصصان صنعت عمل می‌کند.

سرفصل‌های کتاب Cloud Analytics for Industry 4.0:

• List of authors
• G. Sucharitha, I. Venkata Sai Suhruth, Bitla Vishweshwar, Sirisha Potluri The impact of green manufacturing in Industry 4.0 for future ecosystems
• 1 Introduction
• 2 Review
• 2.1 Literature
• 2.2 Background and framework
• 3 Manufacturing ecosystem
• 3.1 Sustainability
• 3.2 Production system
• 3.3 Flexible manufacturing system
• 3.4 Supply chain management
• 3.5 How to build a better manufacturing ecosystem?
• 4 Analysis
• 5 Conclusion
• G. Sucharitha, Ujjwal Shivacharya, Sri Vishal Lanka, Sirisha Potluri Custom manufacturing using Industry 4.0: cost-effective industry revolution model
• 1 Introduction
• 2 Manufacturing and challenges
• 3 Industry 4.0 and its benefits
• 4 Identify opportunities
• 4.1 Optimize logistics and supply chains
• 4.2 Internet of Things and the cloud
• 4.3 Robots
• 4.4 Autonomous equipment and vehicles
• 5 Custom manufacturing
• 6 Conclusion
• T. Padmavathy, C. Muralidharan, Y. Mohamed Sirajudeen Cloud-based industrial IoT infrastructure to facilitate efficient data analytics
• 1 Introduction to Industry 4.0
• 1.1 Opportunities IoT, Cloud, and Big Data
• 1.2 Data analytics in Industry 4.0
• 1.3 Evolution of cyber systems and Industrial IoT (IIoT)
• 1.4 Tangible benefits of using Big Data, IoT, and the Cloud
• 2 Data analytics and methods
• 2.1 Descriptive data analytics
• 2.2 Predictive data analytics
• 2.3 Prescriptive data analytics
• 3 Cloud computing and IoT
• 4 IoT architecture and technologies
• 4.1 Integration of Cloud and IoT: challenges
• 4.2 Edge computing and IoT
• 4.3 Cloud providers for IoT deployments
• 5 Fog computing and IoT
• 5.1 Fog analytics
• 5.2 Fog computing drivers – IoT gateways
• 6 IoT big data analytics versus Industry 4.0
• 6.1 Big data in automotive industry
• 6.2 Big data in security
• 6.3 Big data in services industry
• 6.4 Big data analytics market
• 7 Case studies
• 7.1 Cyber-Physical Systems (CPS) and Big Data analytics
• 7.2 Drone-assisted safe home
• 7.3 Big Data analytics – future directions
• G. Anil Kumar, R. Praveen Sam, A. Ravi Kumar, V. Subbaramaiah An impact of robust Industry 4.0 strategy on supply chain management
• 1 Introduction
• 1.1 Role of industry in supply chain management
• 2 Literature survey
• 2.1 Supply chain management
• 2.2 Industry 4.0’s key drivers and barriers
• 3 Proposed methodology
• 4 Proposed SD model
• 5 Results and discussion
• 6 Conclusion and future scope
• P. Dileep, Madadi Vijayakamal, Mantri Gayatri Machine learning based smart cloud factories
• 1 Introduction
• 1.1 Smart factory definition
• 1.2 Features
• 2 Literature survey
• 3 Architecture for smart factory
• 4 Key technologies
• A. Smart device layer
• B. Intuitive movement path planning
• C. Software-defined network game theory
• D. Network layer
• E. Intelligent management of resources using resource virtualization
• F. Active preventive maintenance using machine learning
• 5 Challenges, issues, and solutions for smart factory implement
• A. Highly intelligent devices
• B. Cloud security
• 6 Conclusion
• Fahmina Taranum, K. Niraja, Maniza Hijab Sustainable and flexible digital models for the manufacturing of ecosystem
• 1 Introduction
• 1.1 Digitizing world
• 1.2 Ecosystem
• 1.3 Role of industry
• 1.4 Sustainability
• 1.5 Flexible digital models
• 1.6 Transforming world
• 2 Conclusion
• Ruqqaiya Begum, Koti Tejasvi Industry 4.0: efficient supply chain management using energy-aware cloud infrastructure model
• 1 Introduction
• 2 Literature survey
• 2.1 Industry 4.0 concept and perspectives
• 2.2 Cyber-Physical Systems (CPS) based Industry 4.0
• 2.3 Industry 4.0 and supply chains and sustainability
• 3 Methodology
• 3.1 Data collection and results
• 3.2 Discussion and future scope
• K. Tejasvi, Ruqqaiya Begum Analytical models for planning and control of autonomous mobile robots for logistic management
• 1 Introduction
• 1.1 Requirements for autonomous systems in the real world
• 2 Literature survey
• 2.1 A review of AMR deployment studies and solution strategies
• 2.2 An AMR framework for planning and monitoring
• 2.3 Methods for planning and controlling AMRs
• 2.4 Methods for decentralized scheduling and task allocation
• 3 Dispatching
• Problem
• Methods
• 3.1 Path planning
• 3.2 Robustness and resilience
• 4 Conclusion and future scope
• Ayesha Mariyam, Sk. Althaf Hussain Basha, S. Viswanadha Raju Industry 4.0: augmented reality in smart manufacturing industry environment to facilitate faster
• and easier work procedures
• 1 Introduction
• 2 Literature survey
• 2.1 Operations in Industry 4.0
• 2.2 Augmented reality
• 3 Development of smart products in industry
• 4 Methodology
• 5 Implementation of software and hardware for augmented reality in smart industry
• 5.1 Hardware requirements
• 5.2 Software requirements
• 6 Results and discussion
• 7 Areas of applications of AR
• 8 Devices used to work with AR
• 9 Implemented tracking methods
• 10 Challenges and issues
• 11 Conclusion and future scope
• 11.1 Future research areas of AR applications in I4.0
• G. Malini Devi Prediction of knee osteoarthritis progression using machine learning techniques
• 1 Introduction
• 1.1 Problem definition
• 1.2 Existing system
• 1.3 Proposed system
• 1.4 Objectives
• 2 Literature survey
• 3 Methodology
• 3.1 The architecture of knee osteoarthritis prediction
• 3.2 Modules
• 4 Results and discussions
• 4.1 Comparison of precision, recall, F1-score, and support using SVM algorithm
• 5 Conclusion
• Index
• De Gruyter Series Smart Computing Applications

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