Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

“…The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. …”

Image2_A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.JPEG by Shuo Wang (143908)

Image7_A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.JPEG by Shuo Wang (143908)

Table1_A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.XLSX by Shuo Wang (143908)

Image3_A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.JPEG by Shuo Wang (143908)

Image6_A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.JPEG by Shuo Wang (143908)

Image1_A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.JPEG by Shuo Wang (143908)

Image4_A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.JPEG by Shuo Wang (143908)

Image5_A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data.JPEG by Shuo Wang (143908)

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…In this way, for each binary problem, the CSP algorithm produces features to determine if the specific body part is engaged in the task or not. …”

Models and Dataset by M RN (9866504)

“…The algorithm does not rely on predefined control parameters like crossover or mutation rates, which makes it lightweight and easy to implement for various feature selection and optimization tasks.…”

Data Sheet 1_A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus.docx by Tingting Li (10553)

“…</p>Conclusions<p>The developed XGBoost model demonstrated optimal predictive accuracy for the occurrence of DKD in patients with T2DM. …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…</i>, 2023</a>) (<a href="#sup1" target="_blank">Supplementary Data S2</a>). The final proposed betalain extraction procedure in the cape fig used 300 mg of frozen leaf samples ground with a mixer mill and homogenized with 0.5 mL of 50 % MeOH (aq.) and mixed for 20 min with a vortex. …”

DataSheet1_Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo.docx by Kaixian Yu (2836709)

“…A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. …”

Metadata supporting the published article: A Medicare-based Comparative Mortality Analysis of Active Surveillance in Older Women with DCIS by Igor Akushevich (163135)

“…The final sample contained 22,576 female patients diagnosed with DCIS.…”