Deep Learning And Edge Computing Solutions For High Performance Computing
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Deep Learning and Edge Computing Solutions for High Performance Computing
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.
High Performance Computing in Biomimetics
Author: Kamarul Arifin Ahmad
language: en
Publisher: Springer Nature
Release Date: 2024-03-20
This book gives a complete overview of current developments in the implementation of high performance computing (HPC) in various biomimetic technologies. The book presents various topics that are subdivided into the following parts: A) biomimetic models and mechanics; B) locomotion and computational methods; C) distributed computing and its evolution; D) distributed and parallel computing architecture; E) high performance computing and biomimetics; F) big data, management, and visualization; and G) future of high performance computing in biomimetics. This book presents diverse computational technologies to model and replicate biologically inspired design for the purpose of solving complex human problems. The content of this book is presented in a simple and lucid style which can also be used by professionals, non-professionals, scientists, and students who are interested in the research area of high performance computing applications in the development of biomimetics technologies.
Advances in Scalable and Intelligent Geospatial Analytics
Geospatial data acquisition and analysis techniques have experienced tremendous growth in the last few years, providing an opportunity to solve previously unsolved environmental- and natural resource-related problems. However, a variety of challenges are encountered in processing the highly voluminous geospatial data in a scalable and efficient manner. Technological advancements in high-performance computing, computer vision, and big data analytics are enabling the processing of big geospatial data in an efficient and timely manner. Many geospatial communities have already adopted these techniques in multidisciplinary geospatial applications around the world. This book is a single source that offers a comprehensive overview of the state of the art and future developments in this domain. FEATURES Demonstrates the recent advances in geospatial analytics tools, technologies, and algorithms Provides insight and direction to the geospatial community regarding the future trends in scalable and intelligent geospatial analytics Exhibits recent geospatial applications and demonstrates innovative ways to use big geospatial data to address various domain-specific, real-world problems Recognizes the analytical and computational challenges posed and opportunities provided by the increased volume, velocity, and veracity of geospatial data This book is beneficial to graduate and postgraduate students, academicians, research scholars, working professionals, industry experts, and government research agencies working in the geospatial domain, where GIS and remote sensing are used for a variety of purposes. Readers will gain insights into the emerging trends on scalable geospatial data analytics.