Results 1 to 10 of about 447,603 (295)
Fractal Nature Bridge between Neural Networks and Graph Theory Approach within Material Structure Characterization [PDF]
Fractal and Fractional, 2022 Many recently published research papers examine the representation of nanostructures and biomimetic materials, especially using mathematical methods. For this purpose, it is important that the mathematical method is simple and powerful.
Branislav M. Randjelovic +6 more
doaj +7 more sources
International Journal of Bridge Engineering, Management and ResearchTraditional bridge maintenance approaches often lack the capability to capture interactions among structural elements or assess the vulnerability of freight networks to disruptions such as bridge closures. To overcome these limitations, this study introduces a data-driven bridge maintenance framework that integrates artificial intelligence, geographic ...
Subrata Saha +3 more
semanticscholar +4 more sources
GT-DTW: Bridging Graph Theory and Dynamic Time Warping for Complex Time Series Analysis [PDF]
, 2023 <p>Classification of time series data plays a critical role across various domains, enabling pattern recognition and trend prediction. Traditional methods like Dynamic Time Warping (DTW) have been widely used to measure similarity between time series, but there are challenges related to computational complexity and sensitivity to noise.
Sachit Mahajan
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Model-Based Predictive Control with Graph Theory Approach Applied to Multilevel Back-to-Back Cascaded H-Bridge Converters [PDF]
Electronics, 2022 The multilevel back-to-back cascaded H-bridge converter (CHB-B2B) presents a significantly reduced components per level in comparison to other classical back-to-back multilevel topologies. However, this advantage cannot be fulfilled because of the several internal short circuits presented in the CHB-B2B when a conventional PWM modulation is applied. To
Gabriel Gaburro Bacheti +4 more
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Proceedings of the Web Conference 2021, 2021 The von Neumann graph entropy (VNGE) is a measure of graph complexity based on the Laplacian spectrum. It has recently found applications in various learning tasks driven by networked data. However, it is computationally demanding and hard to interpret using simple structural patterns.
Xuecheng Liu, Luoyi Fu, Xinbing Wang
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Bridging Theory and Practice in Link Representation with Graph Neural Networks [PDF]
Graph Neural Networks (GNNs) are widely used to compute representations of node pairs for downstream tasks such as link prediction. Yet, theoretical understanding of their expressive power has focused almost entirely on graph-level representations. In this work, we shift the focus to links and provide the first comprehensive study of GNN expressiveness
Veronica Lachi +5 more
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Boletim da Sociedade Paranaense de MatemáticaAs modern systems become increasingly complex, there is a growing need for advanced mathe- matical frameworks capable of modeling uncertainty, vagueness, and indeterminacy. This research presents a novel contribution by integrating neutrosophic set theory with graph theory and topol- ogy, providing a more flexible model for handling complex networked ...
Gazwan Haider Abdulhusein +2 more
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International Journal of Latest Technology in Engineering Management & Applied ScienceAbstract: Graph theory has evolved from its origins in Euler’s 1736 solution to the Königsberg bridge problem into a foundational discipline with far-reaching applications in computer science, biology, social networks, and artificial intelligence.
S R Ashwini
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Bioinformatics, 2018 Abstract Motivation Computational studies of molecular evolution are usually performed from a multiple alignment of homologous sequences, on which sequences resulting from a common ancestor are aligned so that equivalent residues are placed in the same position.
Neli Fonseca +3 more
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