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Data · dataset · 2022

Intermediate data of the accuracy curves on possibility risk, consequence risk and final risk.

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The data were calculated by the GSS-RiskAseer, we compare our GSS-RiskAsser with the following popular deep-learning models: (1) CNN-based methods.

Description

We only apply CNN architecture to process visual information and predict gas supply system risk. The models we compare are ResNet50 and ResNet101.

(2) RNN-based methods. In these baselines, we only exploit score matrix by DNN, GRU or LSTM to predict risk value. (3) CNN-RNN fusion methods.

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We extract multi-modal features by CNN and RNN separately and concatenate them to predict risk.

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Where it is published

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Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
ScienceDB10.57760/sciencedb.0542612 d agoJSON v1
FieldAssertionExtractorEvidence
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