A novel approach to subgraph selection with multiple weights on arcs

Raayatpanah, Mohammad Ali; Khodayifar, Salman; Weise, Thomas; Pardalos, Pãnos M.

doi:10.1007/s10878-021-00823-0

Cited by 3 publications

(1 citation statement)

References 62 publications

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“…One purpose is to accurately predict enterprise performance using fluctuation data during the COVID-19 pandemic, while the other is to generate scientific performance optimization solutions for low-performance enterprises from the perspective of input/output resources. According to the SVM (Du et al 2021 ) and DEA (Raayatpanah et al 2021 ) theories, the proposed model is developed as follows, and the corresponding schematic is depicted in Fig. 1 .…”

Section: Proposed Intelligent Modelmentioning

confidence: 99%

Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model

et al. 2022

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The coronavirus disease (COVID-19) pandemic has caused significant changes in the external environment of enterprises, resulting in tremendous negative impacts. Accordingly, the irregular fluctuation of business data poses a critical challenge to traditional approaches. Therefore, to combat the effects of the COVID-19 pandemic, an effective model is required to proactively predict an enterprise’s performance and simultaneously generate scientific performance optimization solutions. Consequently, at the intersection of artificial intelligence algorithms, operations research, and management science, an intelligent DEA-SVM model, which has a theoretical contribution, is developed in this study. The capabilities of this model are verified through sufficient numerical experiments. On the one hand, this model outperforms traditional algorithms in prediction accuracy. On the other hand, effective performance optimization solutions for low-performance enterprises are obtained from the input–output perspective. Moreover, the application value of this model is reflected in its successful implementation in the healthcare industry. Thus, it is a user-friendly tool for realizing the stable operation of enterprises in the context of the COVID-19 pandemic.

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Section: Proposed Intelligent Modelmentioning

confidence: 99%

Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model

et al. 2022

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A novel approach to discover frequent weighted subgraphs using the average measure

Yun

et al. 2023

Appl Intell

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SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems

Hao,

Cai,

Fang

et al. 2024

ACM Trans. Intell. Syst. Technol.

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Domain knowledge is gradually renovating its attributes to exhibit distinct features in autonomy, propelled by the shift of modern transportation systems (TS) towards autonomous TS (ATS) comprising three progressive generations. Knowledge graph (KG) and its corresponding versions can help depict the evolving TS. Given that KG versions exhibit asymmetry primarily due to variations in evolved knowledge, it is imperative to harmonize the evolved knowledge embodied by the entity across disparate KG versions. Hence, this paper proposes a siamese-based graph convolutional network (GCN) model, namely SiG, to address unresolved issues of low accuracy, efficiency, and effectiveness in aligning asymmetric KGs. SiG can optimize entity alignment in ATS and support the analysis of future-stage ATS development. Such a goal is attained through: a) generating unified KGs to enhance data quality, b) defining graph split to facilitate entire-graph computation, c) enhancing GCN to extract intrinsic features, and d) designing siamese network to train asymmetric KGs. The evaluation results suggest that SiG surpasses other commonly employed models, resulting in average improvements of 23.90% and 37.89% in accuracy and efficiency, respectively. These findings have significant implications for TS evolution analysis and offer a novel perspective for research on complex systems limited by continuously updated knowledge.

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A novel approach to subgraph selection with multiple weights on arcs

Cited by 3 publications

References 62 publications

Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model

Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model

A novel approach to discover frequent weighted subgraphs using the average measure

SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems

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