Transactions on Computational Science XXXIV

by Marina L. Gavrilova (Editor), C.J. Kenneth Tan (Editor)

Overview

The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings, and solutions, and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods.

This, the 34th issue of the Transactions on Computational Science, contains seven in-depth papers focusing on research on data analytics using machine learning and pattern recognition, with applications in wireless networks, databases, and remotely sensed data.

Product Details

ISBN-13:	9783662599570
Publisher:	Springer Berlin Heidelberg
Publication date:	08/29/2019
Series:	Lecture Notes in Computer Science , #11820
Edition description:	1st ed. 2019
Pages:	137
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

Machine Learning in Hybrid Environment for Information Identification with Remotely Sensed Image Data.- Clustering-Based Aggregation of High-Utility Patterns from Unknown Multi-Database.- A Study of Three Different Approaches to Point Placement on a Line in an Inexact Model.- Cinolib: A Generic Programming Header Only C++ Library for Processing Polygonal and Polyhedral Meshes.- Trust Computation in VANET Cloud.- Received Power Exploration of Cooperative WSN Deployed in Adjustable Antenna Height Environment.- A Built-in Circuit for Self-Reconfiguring Mesh-Connected Processor Arrays with Spares on Diagonal.