Transactions on Computational Science XXXIX

Description

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. LNCS Transactions on Computational Science Scope Editorial LNCS Transactions on Computational Science – Editorial Board Contents Degradable Self-restructuring of Processor Arrays by Direct Spare Replacement 1 Introduction 2 DSRS and Repairability Condition 3 Degradable Restructuring Algorithm 4 Evaluation of Degradable Restructuring 5 Hardware Realization 6 Conclusion References Structural Composite Feature Triangulation for Visual Object Search 1 Introduction 2 Literature Review 3 Proposed Framework 4 Unique and Repeated Correspondences 5 Structural Composite Feature Triangulation 5.1 Orientation-Consistent Feature Pair Subsets 5.2 Mining of Composite Triangulations 6 Experimental Results 7 Conclusion References Study of Malaysian Cloud Industry and Conjoint Analysis of Healthcare and Education Cloud Service Utiliztion 1 Introduction 2 Related Works 3 Data Acquisition Methodology 3.1 Research Data – Secondary Sources 3.2 Data Normalization and Aggregation 3.3 Solution of Missing Data 4 Research Methodology 4.1 Research Hypothesis 5 Conclusion and Future Work References Generating Weakly Chordal Graphs from Arbitrary Graphs 1 Introduction 2 Preliminaries 3 Arbitrary Graph to Weakly Chordal Graph 3.1 Overview of the Method 3.2 Random Arbitrary Graph 3.3 Arbitrary Graph to Chordal Graph 3.4 Chordal Graph to Weakly Chordal Graph 3.5 Finding P3 and P4 Paths 3.6 Complexity 4 Experimental Results 5 Conclusion References A Novel Machine Learning Framework for Covid-19 Image Classification with Bio-heuristic Optimization 1 Introduction 2 Preliminaries 2.1 MobileNetV3 2.2 MoFAL Optimizer 3 Model Architecture 3.1 MobileNetV3 4 Results 4.1 Data Sources 4.2 Metrics 4.3 Analysis References An Unsupervised DNN Embedding System for Image Clustering 1 Introduction 2 Related Work 3 Method 3.1 AutoEmbedder Framework 3.2 Unsupervised Learning Strategy 3.3 Mathematical Analysis 4 Experimental Setup 4.1 Evaluation Metrics 4.2 Datasets 4.3 Environmental Setup 5 Result and Analysis 5.1 Comparison 5.2 Case Study 5.3 Sensitivity Analysis 6 Discussion 7 Conclusion References Author Index