Adversarial training. by Bing Zhang (129)

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Early detection results of rumors for each dataset. by Bing Zhang (129)

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Rumor detection results. by Bing Zhang (129)

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Performance of GAT, GCN and GraphSAGE architectures. by Bing Zhang (129)

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Performance of double comparison learning on three public datasets. by Bing Zhang (129)

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Performance of double contrast learning, self-supervised contrast learning and no contrast learning. by Bing Zhang (129)

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Performance of adversarial training on three public datasets. by Bing Zhang (129)

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Performance of the attention filter on three public datasets. by Bing Zhang (129)

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Attention filter structure diagram. by Bing Zhang (129)

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Statistics of the datasets. by Bing Zhang (129)

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Overview of rumor detection framework for adversarial dual contrast learning graph neural networks. by Bing Zhang (129)

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Data_Sheet_1_SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation.docx by Ekaterina Mikhaylets (17865407)

“…<p>The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. …”

The analytic pipeline for EGG analysis and biomarker identification. by Mahmoud Zeydabadinezhad (12289570)

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List of time- and frequency-domain features used for EGG analysis. by Mahmoud Zeydabadinezhad (12289570)

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This illustration compares the AUC for a Random Forest classifier trained using various feature sets. by Mahmoud Zeydabadinezhad (12289570)

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Fig 1 - by Mahmoud Zeydabadinezhad (12289570)

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Electrode placements. by Mahmoud Zeydabadinezhad (12289570)

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This illustration compares the AUC for a Random Forest classifier trained using various feature sets. by Mahmoud Zeydabadinezhad (12289570)

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PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …”