International Virtual Conference on Industry 4.0 (eBook, PDF)

Select Proceedings of IVCI4.0 2020

• Format: PDF

Produktbeschreibung

This book presents the proceedings of the International Virtual Conference on Industry 4.0 (IVCI4.0 2020). This conference brings together specialists from the academia and industry sectors to promote the exchange of knowledge, ideas, and information on the latest developments and applied technologies in the field of Industry 4.0. The book discusses a wide range of topics such as the design of smart and intelligent products, developments in recent technologies, rapid prototyping and reverse engineering, multistage manufacturing processes, manufacturing automation in the Industry 4.0 model, cloud-based products, and cyber-physical and reconfigurable systems, etc. The volume supports the transfer of vital knowledge to the next generation of academics and practitioners.

Produktdetails

• Verlag: Springer Singapore
• Erscheinungstermin: 3. August 2021
• Englisch
• ISBN-13: 9789811612442
• Artikelnr.: 62372600

Autorenporträt

R. Jagadeesh Kannan is Professor and Dean of the School of Computer Science & Engineering at Vellore Institute of Technology, India. He completed his Ph.D. degree in Handwritten Character Recognition using Hybrid Techniques from Anna University, Chennai, India. He got his M.E. degree in Computer Science & Engineering from National Engineering College, Tamil Nadu, and B.E. in Instrumentation & Control Engineering from Madurai Kamaraj University, Tamil Nadu, India. Prof. Kannan has over 18 years of teaching and industrial experience in reputed organizations. Prof. Kannan has got several publications in conference proceedings and journals of National and International repute. His research interests are neural networks, fuzzy logic, neuro-fuzzy systems, soft computing tools, pattern recognition, natural language processing, image processing, networking, printed, handwritten & cursive character recognition, and artificial intelligence. Dr. Kannan is an active member of several Indian and International societies such as IEEE, ISTE, IACSIT, SDIWC, IFRSA, and IAENG.
S. Geetha is Professor and Associate Dean of the School of Computer Science & Engineering at Vellore Institute of Technology, Chennai Campus, India. Dr. Geetha received her Master's (Computer Science and Engineering) and doctoral (Information and Communication Engineering) degrees from Anna University in 2004 and 2011, respectively. Her areas of interest are computer vision, information and network security, and machine learning. Dr. Geetha has co-authored 06 books, 06 book chapters, and over 100 journal and conference papers.
Sravanthi Sashikumar is Deputy Head of Department of Engineering at Manchester Metropolitan University, United Kingdom. She received a Bachelor’s degree in mechanical engineering from the University of Madras, India, in 2002 and a Master’s degree in Engineering Control Systems and Instrumentation from the University of Huddersfield, in 2005. She completed her Ph.D. from the University of Bolton in 2012, and her research interest areas are structural analysis vehicle crashworthiness, passive safety of road vehicles, vehicle occupant safety, and impact biomechanics. Dr. Sravanthi has got several publications to her credit.
Carl Diver is the academic lead on Industry 4.0 and a reader in Industrial Digitalization at Manchester Metropolitan University. Carl joined Manchester Metropolitan University in 2018 to lead the University’s Industry 4.0 activities. Before academia, Carl worked in the manufacturing sector for over 20 years, firstly for a large multi-national and then establishing his consultancy. At Delphi Automotive Systems, Carl’s focus was on fuel injection equipment from both an R&D and manufacturing angle. More recently, Carl led Industry 4.0 activity, research, and teaching for the manufacturing group in the School of Mechanical Aerospaceand Civil Engineering at the University of Manchester. He helped establish the academic conference at the Industry 4.0 summit in Manchester, and his Ph.D. students won international competitions such as the Siemens Open Space challenge at Hannover Messe in 2018. Carl has been speaking about Industry 4.0 at conferences and events in the UK, Europe, the Middle East, and Asia. He is an associate co-editor of the Virtual and Physical Prototyping Journal and continues helping businesses with more advanced, efficient, and controlled processes.

Inhaltsangabe

Pre-diagnosis, Prediction and Report Generation of a Disease.- An Audio Aided Face and Text Recognition System for Visually Impaired.- Improving Prediction Accuracy using Machine Learning Classification Techniques for Alzheimer’s Disease in Healthcare Services.- Evolutionary Computing based Feature Selection for Cardiovascular Disease: A Review.- Readiness and Maturity Assessment Model to Measure the Industry 4.0 Ecosystem.- An Insight on Context-aware Mobile Application Execution in Mobile Cloud IoT (MCIoT).- Breast Cancer Detection in Histology Images using Convolutional Neural Network.- A Novel Approach on Auto Scaling for Resource Scheduling using AWS.- A Blockchain-based COVID19 Protection Framework.- A State-Of-Art of Machine Learning Algorithms Applied Over Language Identification and Speech Recognition Models.

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Pre-diagnosis, Prediction and Report Generation of a Disease.- An Audio Aided Face and Text Recognition System for Visually Impaired.- Improving Prediction Accuracy using Machine Learning Classification Techniques for Alzheimer's Disease in Healthcare Services.- Evolutionary Computing based Feature Selection for Cardiovascular Disease: A Review.- Readiness and Maturity Assessment Model to Measure the Industry 4.0 Ecosystem.- An Insight on Context-aware Mobile Application Execution in Mobile Cloud IoT (MCIoT).- Breast Cancer Detection in Histology Images using Convolutional Neural Network.- A Novel Approach on Auto Scaling for Resource Scheduling using AWS.- A Blockchain-based COVID19 Protection Framework.- A State-Of-Art of Machine Learning Algorithms Applied Over Language Identification and Speech Recognition Models.