Machine-learning models are key to modern biology, yet models trained on one dataset are
often not generalizable to other datasets from different cohorts or laboratories due to both …

Abstract Electrochemical Impedance Spectroscopy (EIS) serves as a valuable tool for
analyzing the health of Proton Exchange Membrane Fuel Cell (PEMFC). However, the …

We study regularized deep neural networks (DNNs) and introduce a convex analytic
framework to characterize the structure of the hidden layers. We show that a set of optimal …

A new line of research for feature selection based on neural networks has recently emerged.
Despite its superiority to classical methods, it requires many training iterations to converge …

Objective. To develop an efficient, embedded electroencephalogram (EEG) channel
selection approach for deep neural networks, allowing us to match the channel selection to …

Hyperspectral Image Classification (HSC) is a challenging task due to the high
dimensionality and complex nature of Hyperspectral (HS) data. Traditional Machine …

The applications of traditional statistical feature selection methods to high-dimension, low-
sample-size data often struggle and encounter challenging problems, such as overfitting …

Speech enhancement is widely used as a front-end to improve the speech quality in many
audio systems, while it is hard to extract the target speech in multi-talker conditions without …

Clustering is a fundamental learning task widely used as a first step in data analysis. For
example, biologists often use cluster assignments to analyze genome sequences, medical …

We present an approach to efficiently construct stepwise regression models in a very high
dimensional setting using a multidimensional index. The approach is based on an …