Computational intelligence and neuroscience
Jan 23, 2023
A nonlinear error correction model (ECM) is developed to fit nonlinear relationships between the nonstationary time series in a cointegration relationship. Different from the previous parametric methods, this paper constructs a hybrid neural network ...
How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. O...
Machine learning method has become a popular, convenient and efficient computing tool applied to many industries at present. Multi-hole pressure probe is an important technique widely used in flow vector measurement. It is a new attempt to integrate ...
Clinical and experimental rheumatology
Jan 23, 2023
Although medical research has been performed predominantly on men both in preclinical and clinical studies, continuous efforts have been made to overcome this gender bias. Examining retrospectively 21 data sets containing sex as one of the descriptiv...
International journal of molecular sciences
Jan 22, 2023
Thermophilic proteins have important value in the fields of biopharmaceuticals and enzyme engineering. Most existing thermophilic protein prediction models are based on traditional machine learning algorithms and do not fully utilize protein sequence...
A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural n...
Neural networks : the official journal of the International Neural Network Society
Jan 21, 2023
This paper is devoted to the study of the robust exponential stability (RES) of discrete-time uncertain impulsive stochastic neural networks (DTUISNNs) with delayed impulses. Using Lyapunov function methods and Razumikhin techniques, a number of suff...
Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform conventional machin...
Neural networks : the official journal of the International Neural Network Society
Jan 20, 2023
In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the ...
Acquisition of a standard section is a prerequisite for ultrasound diagnosis. For a long time, there has been a lack of clear definitions of standard liver views because of physician experience. The accurate automated scanning of standard liver secti...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.