Breast cancer survival prognosis using the graph convolutional network with Choquet fuzzy integral
Breast cancer is the most prevalent kind of cancer among women and there is a need for a
reliable algorithm to predict its prognosis. Previous studies focused on using gene …
reliable algorithm to predict its prognosis. Previous studies focused on using gene …
Feature selection of gene expression data for cancer classification using double RBF-kernels
Background Using knowledge-based interpretation to analyze omics data can not only
obtain essential information regarding various biological processes, but also reflect the …
obtain essential information regarding various biological processes, but also reflect the …
An intelligent hybrid model for air pollutant concentrations forecasting: Case of Beijing in China
The forecasting of air pollutant concentrations is of great significance to protect the
environment and guarantee the health of people. In the study, a novel hybrid model, namely …
environment and guarantee the health of people. In the study, a novel hybrid model, namely …
Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods
Rapid determination of moisture content plays an important role in guiding the recycling,
treatment and disposal of solid waste, as the moisture content of solid waste directly affects …
treatment and disposal of solid waste, as the moisture content of solid waste directly affects …
A comparison of machine learning approaches for identifying high-poverty counties: Robust features of DMSP/OLS night-time light imagery
G Li, Z Cai, X Liu, J Liu, S Su - International journal of remote …, 2019 - Taylor & Francis
The goal of the present study is to demonstrate that high-poverty counties and robust
classification features can be identified by machine learning approaches using only …
classification features can be identified by machine learning approaches using only …
Evolving kernels for support vector machine classification
KM Sullivan, S Luke - Proceedings of the 9th annual conference on …, 2007 - dl.acm.org
While support vector machines (SVMs) have shown great promise in supervised
classification problems, researchers have had to rely on expert domain knowledge when …
classification problems, researchers have had to rely on expert domain knowledge when …
Learning-based near-optimal motion planning for intelligent vehicles with uncertain dynamics
Motion planning has been an important research topic in achieving safe and flexible
maneuvers for intelligent vehicles. However, it remains challenging to realize efficient and …
maneuvers for intelligent vehicles. However, it remains challenging to realize efficient and …
Personality segmentation of users through mining their mobile usage patterns
R Razavi - International Journal of Human-Computer Studies, 2020 - Elsevier
Users' interactions with their mobile devices leave behind unique digital footprints that can
reveal important information about their characteristics, including their personality. By …
reveal important information about their characteristics, including their personality. By …
Multi-scale mahalanobis kernel-based support vector machine for classification of high-resolution remote sensing images
G Sun, X Rong, A Zhang, H Huang, J Rong… - Cognitive …, 2021 - Springer
Support vector machine (SVM) is a powerful cognitive and learning algorithm in the domain
of pattern recognition and image classification. However, the generalization ability of SVM is …
of pattern recognition and image classification. However, the generalization ability of SVM is …