Transactions on Computational Science XXXIX - Gavrilova, Marina L.; Tan, C. J. Kenneth (Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2022)
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 39th issue of the Transactions on Computational Science, is devoted to research on geometric modeling, visual object detection, cloud service utilization, pattern recognition, processing arrays, and classification using bio-heuristic optimization.
Degradable Self-Restructuring of Processor Arrays by Direct Spare Replacement -- Structural Composite Feature Triangulation for Visual Object Search -- Study of Malaysian Cloud Industry and Conjoint Analysis of Healthcare and Education Cloud Service Utilization -- Algorithms for Generating Strongly Chordal Graphs -- A Novel Machine Learning Framework for COVID-19 Image Classification with Bio-heuristic Optimization -- An Unsupervised DNN Embedding System for Image Clustering.
Keywords: Mathematics-Data processing; Numerical analysis; Algorithms; Pattern recognition systems; Machine learning; Computers, Special purpose; Computational Science and Engineering; Numerical Analysis; Design and Analysis of Algorithms; Automated Pattern Recognition; Machine Learning; Special Purpose and Application-Based Systems