Buch, Englisch, Band 39, 801 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1223 g
Reihe: Lecture Notes on Data Engineering and Communications Technologies
ICSCN 2019
Buch, Englisch, Band 39, 801 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1223 g
Reihe: Lecture Notes on Data Engineering and Communications Technologies
ISBN: 978-3-030-34514-3
Verlag: Springer International Publishing
This book presents state-of-the-art theories and technologies and discusses developments in the two major fields: engineering and sustainable computing. In this modern era of information and communication technologies [ICT], there is a growing need for new sustainable and energy-efficient communication and networking technologies. The book highlights significant current and potential international research relating to theoretical and practical methods toward developing sustainable communication and networking technologies.
In particular, it focuses on emerging technologies such as wireless communications, mobile networks, Internet of things [IoT], sustainability, and edge network models. The contributions cover a number of key research issues in software-defined networks, blockchain technologies, big data, edge/fog computing, computer vision, sentiment analysis, cryptography, energy-efficient systems, and cognitive platforms.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
An Android Based Assessment Tool for the Visually Impaired.- Optimization of Sensing Time in Cognitive Radio Networks based on Localization Algorithm.- Automated Robot Communication System Using Swarm Intelligence.- Applying Lagrange Model to Fill Data During Big Data Streaming.- Design of a Home Guide System for Dementia Using Integrated Sensor.- Speech Separation Using Deep Learning.- Divisible Load Scheduling from Single Source in Distributed Heterogeneous Environments.- Classification of Walkers Based on Back Angle Measurements Using Wireless Sensor Node.- Cassava Disease Prediction Using Data Mining.- A Literature Survey on Various Classifications of Data Mining For Predicting Heart Disease.
