Framework of MsHop. by Jia Li (160557)

“…Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Results of ablation study. by Jia Li (160557)

“…Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Kappa consistency ranges. by Jia Li (160557)

“…Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Performance metrics under different noise levels. by Jia Li (160557)

“…Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

Results of ablation study. by Jia Li (160557)

“…Firstly, we combine active learning with self-supervised learning to decrease the reliance on labeled data. …”

DataSheet2_ADRB2 serves as a novel biomarker and attenuates alcoholic hepatitis via the SIRT1/PGC-1α/PPARα pathway: integration of WGCNA, machine learning and experimental validati... by Li Song (34475)

“…</p>Results<p>Using differential analysis and WGCNA of the data set, a total of 167 key genes that may be related to AH were obtained. …”

DataSheet1_ADRB2 serves as a novel biomarker and attenuates alcoholic hepatitis via the SIRT1/PGC-1α/PPARα pathway: integration of WGCNA, machine learning and experimental validati... by Li Song (34475)

“…</p>Results<p>Using differential analysis and WGCNA of the data set, a total of 167 key genes that may be related to AH were obtained. …”

Presentation 1_Differences in firing patterns along the dorsal-intermediate hippocampal axis in a fixed route during a change in emotional context.pptx by Ryan Troha (22597001)

Data Sheet 1_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 4_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 6_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 3_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 2_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 5_Identification of mitochondria-related feature genes for predicting type 2 diabetes mellitus using machine learning methods.csv by Xiuping Xuan (8686266)

Data Sheet 1_Serious gaming as an experiential learning tool: exploring the human–water perspectives in the case of Mt. Kenya water tower.docx by Charles Nduhiu Wamucii (20912792)

“…In such landscapes, resolving conflicting interests among individual viewpoints and moving toward collective human–water perspectives is paramount to achieving sustainable management of decreasing water resources. Serious games have been proposed as participatory tools for (social) learning in contested landscapes, however the impact of such approaches on learning is understudied. …”

Data Sheet 1_Diagnostic classification of mild cognitive impairment in Parkinson's disease using subject-level stratified machine-learning analysis.pdf by Jing Wang (6206297)

“…Additionally, more stringent site-level split validation yielded slightly decreased overall performance, with LR showing improved AUC-PR. …”

S1 Data - by Anja T. Zai (9225852)

“…However, we find no such decrease when social isolation is caused by either removal of a social companion or by transfer to a familiar environment. …”

Matrix of indicators and data sources for implementation of NoCs in primary care facilities in Ethiopia, 2024. by Gizachew Tadele Tiruneh (8407686)

Subjects:

Predicting the occurrence of root rot in tree stumps based on harvester data by Simon Berg (7376900)

Evaluation results. by Briya Tariq (19666901)

“…<div><p>Spectral Photon Counting Computed Tomography (SPCCT), a ground-breaking development in CT technology, has immense potential to address the persistent problem of metal artefacts in CT images. …”