Computational and mathematical methods in medicine
34413898
Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often...
Neural networks : the official journal of the International Neural Network Society
33933888
This article is devoted to the H estimation problem for stochastic semi-Markovian switching complex-valued neural networks subject to incomplete measurement outputs, where the time-varying delay also depends on another semi-Markov process. A sequence...
Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson's disease patients, the Parkinson's P...
Protein structure refinement is the last step in protein structure prediction pipelines. Physics-based refinement via molecular dynamics (MD) simulations has made significant progress during recent years. During CASP14, we tested a new refinement pro...
This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients ...
OBJECTIVE: To evaluate the cost-effectiveness of an artificial intelligence electrocardiogram (AI-ECG) algorithm under various clinical and cost scenarios when used for universal screening at age 65.
Human action recognition methods in videos based on deep convolutional neural networks usually use random cropping or its variants for data augmentation. However, this traditional data augmentation approach may generate many non-informative samples (...
IEEE transactions on neural networks and learning systems
33999825
Recently, differentiable neural architecture search (NAS) methods have made significant progress in reducing the computational costs of NASs. Existing methods search for the best architecture by choosing candidate operations with higher architecture ...
IEEE transactions on neural networks and learning systems
33929966
One of the pillar generative machine learning approaches in time series data study and analysis is the hidden Markov model (HMM). Early research focused on the speech recognition application of the model with later expansion into numerous fields, inc...
IEEE transactions on neural networks and learning systems
34106867
This article studies the problem of the stability for Markovian neural networks (MNNs) with time delay. The transition rate is considered to be generally hybrid, which treats those existing ones as its special cases. The introduced generally hybrid t...