Extraction and expression of architectural color. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Basic color value distribution map of the street. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

SegNet architecture. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Overview of workflow. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Descriptive statistics for the volunteers. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Jiefang North Road Street. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Colors with different number of clusters. by Xin Han (1329648)

“…We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. …”

Table_1_Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images.DOCX by Yeo Kyoung Won (15599561)

Table_1_Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.DOCX by Nguyen Thanh Nhu (16400859)

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

DataSheet_1_MRI-Radiomics Prediction for Cytokeratin 19-Positive Hepatocellular Carcinoma: A Multicenter Study.docx by Fan Yang (1413)

Data Sheet 1_Triglyceride-glucose index and mortality in congestive heart failure with diabetes: a machine learning predictive model.doc by Lin Yu (221619)

“…Subgroup analyses were conducted based on age, gender, chronic pulmonary disease, atrial fibrillation, hypertension, and mechanical ventilation to assess the robustness of our findings. Feature selection was performed using LASSO regression, and predictive modeling was carried out using machine learning algorithms.…”

Supplementary file 1_A study on a real-world data-based VTE risk prediction model for lymphoma patients.docx by Changli He (22424818)

“…Model development incorporated three imputation methods, three sampling strategies, three feature selection approaches, and nine machine learning algorithms. …”

Datasheet1_An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation.pdf by Mamoun T. Mardini (14672933)

“…</p>Results<p>We analyzed data from 4,178 patients listed as Status 2. Out of them, 12% had primary outcomes indicating Status 2 failure. …”

Data Sheet 1_Association between admission Braden Skin Score and delirium in surgical intensive care patients: an analysis of the MIMIC-IV database.docx by Meiling Shang (21086624)

“…The primary outcome was incidence of delirium. Feature importance of BSS was initially assessed using a machine learning algorithm, while restricted cubic spline (RCS) models and multivariable logistic analysis evaluated the relationship between BSS and delirium. …”

Data_Sheet_1_Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision.docx by Qunfeng Niu (13263975)