AUC scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

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Recall scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

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F1-scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

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Impact of class imbalance. by GaoXiang Zhao (21499525)

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Hyperparameters of different datasets. by GaoXiang Zhao (21499525)

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Results of different models. by GaoXiang Zhao (21499525)

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Schematic diagram of proposed model averaging method. by GaoXiang Zhao (21499525)

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Results of different models. by GaoXiang Zhao (21499525)

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Impact of class imbalance. by GaoXiang Zhao (21499525)

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Schematic diagram of proposed model averaging method. by GaoXiang Zhao (21499525)

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Experimental results after data augmentation using ADASYN. by GaoXiang Zhao (21499525)

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Distributed and explainable GHSOM for anomaly detection in sensor networks by Paolo Mignone (9760832)

The brief description on the WTCCC dataset. by Liyan Sun (760586)

“…In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 8 DNME models. by Liyan Sun (760586)

“…In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 8 DME models. by Liyan Sun (760586)

“…In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

The penetrance tables for the 6 DNME3 models. by Liyan Sun (760586)

“…In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”

Underlying Data for '<b>Detection of False Position Attacks in VANETs through Bagging Ensemble Learning</b>' by Bekan Mekonen (21544637)

Issues with automatic and team-based QC. by Michael E. Kim (21956727)

A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors by Shunhan Yao (20296506)

“…To tackle this challenge, we established the Bone Tumor X-ray Radiograph dataset (termed BTXRD) in collaboration with multiple medical institutes and hospitals. The BTXRD dataset comprises 3,746 bone images (1,879 normal and 1,867 tumor), with clinical information and global labels available for each image, and distinct mask and annotated bounding box for each tumor instance. …”

Data Sheet 4_Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.pdf by Zheng Ye (15102)

“…Background<p>Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. …”