Feature Selection With Discernibility and Independence Criteria.

CIL-Net: Densely Connected Context Information Learning Network for Boosting Thyroid Nodule Segmentation Using Ultrasound Images.

SFKNN-DPC: Standard deviation weighted distance based density peak clustering algorithm.

ANN-DPC: Density peak clustering by finding the adaptive nearest neighbors.

The nnU-Net based method for automatic segmenting fetal brain tissues.

Unsupervised spectral feature selection algorithms for high dimensional data.

Fetal brain tissue annotation and segmentation challenge results.

DSSDPP: Data Selection and Sampling Based Domain Programming Predictor for Cross-Project Defect Prediction.

DP-k-modes: A self-tuning k-modes clustering algorithm.

KSRFB-net: detecting and identifying butterflies in ecological images based on human visual mechanism.

Head and neck tumor segmentation in PET/CT: The HECKTOR challenge.

PSP-PJMI: An innovative feature representation algorithm for identifying DNA N4-methylcytosine sites.

Fetal Brain Tissue Annotation and Segmentation Challenge Results.

Investigations of butterfly species identification from images in natural environments.

Colon cancer data analysis by chameleon algorithm.

A novel method detecting the key clinic factors of portal vein system thrombosis of splenectomy & cardia devascularization patients for cirrhosis & portal hypertension.

The Automatic Identification of Butterfly Species.

Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors.

Extending twin support vector machine classifier for multi-category classification problems.

Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases.

An Efficient Global K-means Clustering Algorithm.

Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases.