Deep Learning and Edge Computing Solutions for High Performance Computing

ISBN-13: 9783030602673 / Angielski / Miękka / 2022 / 292 str.

Opis

This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology.

Spis treści

1) Deep Learning and Edge Computing Solution for High Performance Computing

2) Artificial Intelligence in Healthcare Databases

3) A Study of Dengue Disease Data by GIS in Kolkata City: An Approach to Healthcare Informatics

4) Edge Computing: Next Generation Computing

5) Edge Computing in Healthcare Systems

6) Deep Stack Neural Networks Based Learning Model for Fault Detection and Classification in Sensor Data

7) Fuzzy Adaptive Intelligent Controller for AC Servo Motor

8) Deep Learning in Healthcare

9) Understanding Deep Learning: Case Study based Approach

10) Deep Learning and its Applications - A real world perspective

11) Applying Blockchain in Agriculture: A Study on Blockchain Technology, Benefits, and Challenges

12) Diverse Applications of Deep Learning Techniques - A Review

13) Healthcare Informatics to analyze patient health records, for enabling better clinical decision making and improved healthcare outcomes

14) Malaria Parasite Enumeration and Classification using Convolutional Neural Networking

15) High Performance Computing: A Deep Learning Perspective

O autorze

Dr. A. Suresh, B.E., M.Tech., Ph.D works as the Associate Professor, Department of the Computer Science and Engineering in SRM Institute of Science & Technology, Kattankulathur, Chenagalpattu District, Tamil Nadu, India. He has been nearly two decades of experience in teaching and his areas of specializations are Data Mining, Artificial Intelligence, Image Processing, Multimedia and System Software. He has published five patents and 95 papers in International journals. He has book authored “Industrial IoT Application Architectures and use cases” published in CRC press and edited book entitled “Deep Neural Networks for Multimodal Imaging and Biomedical Application” published in IGI Global. He has currently editing three books namely “Deep learning and Edge Computing solutions for High Performance Computing” in EAI/Springer Innovations in Communications and Computing,  “Sensor Data Management and Analysis: The Role of Deep Learning” and “Bioinformatics and Medical Applications: Big Data using Deep Learning Algorithms” in Scrivener-Wiley publisher. He has published 15 chapters in the book title An Intelligent Grid Network Based on Cloud Computing Infrastructures in IGI Global Publisher and Internet of Things for Industry 4.0 in EAI/Springer Innovations in Communication and Computing. He has published more than 40 papers in National and International Conferences. He has served as editor / reviewer for Springer, Elsevier, Wiley, IGI Global, IoS Press, Inderscience journals etc... He is a member of IEEE(Senior Member), ISTE, MCSI, IACSIT, IAENG, MCSTA and Global Member of Internet Society (ISOC). He has organized several National Workshop, Conferences and Technical Events. He is regularly invited to deliver lectures in various programmes for imparting skills in research methodology to students and research scholars. He has published four books in Indian publishers, in the name of Hospital Management, Data Structures & Algorithms, Computer Programming, Problem Solving and Python Programming and Programming in “C”. He has hosted two special sessions for IEEE sponsored conference in Osaka, Japan and Thailand.

Sara Paiva is an Associate Professor at the Polytechnic Institute of Viana do Castelo, a PhD in Informatics Engineering from University of Vigo in 2011 and a Postdoctoral Researcher at the University of Oviedo since January 2018, under advanced driving assistants and urban mobility. She is the coordinator of a recently created research center in the Polytechnic Institute of Viana do Castelo. Her main line of research is mobility solutions with a focus on accessibility/social inclusion and also outdoor positioning enhancement. She has supervised several final projects and thesis of Bachelor and Master in her main line of work. She is Editor in Chief of EAI Endorsed Transaction on Smart Cities, Associate Editor of Springer Wireless Networks, editor of multiple Springer and IGI books, special issues, has authored and co-authored several scientific publications in journals and conferences, is a frequent reviewer of international journals and international conferences.  She is leader and collaborator of P2020 R&D projects and also member of H2020 R&D projects.

Blurb

This book provides an insight into ways of inculcating the need for applying mobile edge data analytics in bioinformatics and medicine. The book is a comprehensive reference that provides an overview of the current state of medical treatments and systems and offers emerging solutions for a more personalized approach to the healthcare field. Topics include deep learning methods for applications in object detection and identification, object tracking, human action recognition, and cross-modal and multimodal data analysis. High performance computing systems for applications in healthcare are also discussed. The contributors also include information on microarray data analysis, sequence analysis, genomics based analytics, disease network analysis, and techniques for big data Analytics and health information technology.

• Identifies deep learning techniques in mobile edge data analytics and computing environments suitable for applications in healthcare;
• Introduces big data analytics to the sources available and possible challenges and techniques associated with bioinformatics and the healthcare domain;
• Features advancements in the computing field to effectively handle and make inferences from voluminous and heterogeneous healthcare data.