Scene images with multiple light sources. by Chao Ma (207385)

The accuracy rate of each algorithm model training set and test set. by Jie Lu (139953)

Detectors, sources, and channels. by Ural Akincioglu (21772538)

Comparison of grayscale conversion algorithms. by Tianke Fang (22648035)

Comparison of noise reduction algorithms. by Tianke Fang (22648035)

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Workflow to extract and define myocardial motion and deformation patterns during early heart morphogenesis. by Morena Raiola (22439390)

Federated averaging algorithm pseudocode. by Hadi Mohammadian KhalafAnsar (22783784)

Baseline characteristics and outcomes. by Nowell M. Fine (13874645)

“…We applied a feature extraction method using deep feature synthesis from multiple sources of health data and compared performance of a gradient boosting algorithm (CatBoost) with logistic regression modelling. …”

The recognition performance of subject 5. by Yue Yuan (113839)

“…First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the recognition performance of different numbers of VMFs is evaluated. …”

The recognition performance of subject 1. by Yue Yuan (113839)

“…First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the recognition performance of different numbers of VMFs is evaluated. …”

Basic hand movements. by Yue Yuan (113839)

“…First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the recognition performance of different numbers of VMFs is evaluated. …”

S1 File - by Yue Yuan (113839)

“…First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the recognition performance of different numbers of VMFs is evaluated. …”

The recognition performance of subject 3. by Yue Yuan (113839)

“…First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the recognition performance of different numbers of VMFs is evaluated. …”

The recognition performance of subject 4. by Yue Yuan (113839)

“…First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the recognition performance of different numbers of VMFs is evaluated. …”

The recognition performance of subject 2. by Yue Yuan (113839)

“…First, VMD is used to decompose the sEMG signal into multiple variational mode functions (VMFs). To efficiently extract the intrinsic components of the sEMG, the recognition performance of different numbers of VMFs is evaluated. …”

Table 1_Magnetoencephalographic source localization and reconstruction via deep learning.pdf by Stefano Franceschini (488912)

“…To validate its efficacy, the Authors conducted simulations involving multiple active sources using a realistic forward model, and subsequently compared the results with those obtained using various state-of-the-art reconstruction algorithms. …”

Forest canopy height mapping considering the seasonal rhythm of different dominant tree species by Yali Zhang (206227)

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

Supplementary file 1_Multimodal framework for phishing attack detection and mitigation through behavior analysis using EM-BERT and SPCA-BASED EAI-SC-LSTM.docx by Mahmoud Murhej (21676118)

“…Thus, multiple data sources, including SMS, E-Mail, and URL links, are used in this paper to detect and mitigate phishing attacks.…”