arXiv subjects

Chi-Hua Chen

Publications and source records attributed to Chi-Hua Chen.

4 recordsLinked to original sources

arXiv2023

Joint Modeling for Geometry and Functionality of Cerebral Cortical Surface Images

We propose a framework for jointly modeling the geometry and functionality in high dimensional functional surfaces. The proposed mixed effects model characterizes effects of subject-specific covariates and exogenous stimuli on functional surfaces while accounting for potential mutual-influence of their geometry and functionality. This is achieved through a computationally efficient estimation method that incorporates regularized estimation of the precision matrix of the random effects. We perform a thorough analysis of cerebral cortical surface structural MRI and task fMRI data from the Human Connectome Project and discover relationships between the geometric shapes of cortical surface and neuronal activation responding to task stimuli. Our findings highlight new modes of correspondence between cortical surface shape and functional activation relevant to emotion processing.

stat.AP↗

arXiv2021

Temporal-Spatial Feature Extraction Based on Convolutional Neural Networks for Travel Time Prediction

In recent years, some traffic information prediction methods have been proposed to provide the precise information of travel time, vehicle speed, and traffic flow for highways. However, big errors may be obtained by these methods for urban roads or the alternative roads of highways. Therefore, this study proposes a travel time prediction method based on convolutional neural networks to extract important factors for the improvement of traffic information prediction. In practical experimental environments, the travel time records of No. 5 Highway and the alternative roads of its were collected and used to evaluate the proposed method. The results showed that the mean absolute percentage error of the proposed method was about 5.69%. Therefore, the proposed method based on deep learning techniques can improve the accuracy of travel time prediction.

cs.LG↗

arXiv2020

ExpDNN: Explainable Deep Neural Network

In recent years, deep neural networks have been applied to obtain high performance of prediction, classification, and pattern recognition. However, the weights in these deep neural networks are difficult to be explained. Although a linear regression method can provide explainable results, the method is not suitable in the case of input interaction. Therefore, an explainable deep neural network (ExpDNN) with explainable layers is proposed to obtain explainable results in the case of input interaction. Three cases were given to evaluate the proposed ExpDNN, and the results showed that the absolute value of weight in an explainable layer can be used to explain the weight of corresponding input for feature extraction.

cs.LG↗

arXiv2018

An Exercise Fatigue Detection Model Based on Machine Learning Methods

This study proposes an exercise fatigue detection model based on real-time clinical data which includes time domain analysis, frequency domain analysis, detrended fluctuation analysis, approximate entropy, and sample entropy. Furthermore, this study proposed a feature extraction method which is combined with an analytical hierarchy process to analyze and extract critical features. Finally, machine learning algorithms were adopted to analyze the data of each feature for the detection of exercise fatigue. The practical experimental results showed that the proposed exercise fatigue detection model and feature extraction method could precisely detect the level of exercise fatigue, and the accuracy of exercise fatigue detection could be improved up to 98.65%.

stat.ML↗