AIMC Topic: Neural Networks, Computer

Clear Filters Showing 20161 to 20170 of 31376 articles

Physics-informed neural networks for solving nonlinear diffusivity and Biot's equations.

PloS one
This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting. S...

Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms.

Clinical neurology and neurosurgery
OBJECTIVES: A significant proportion of patients with acute minor stroke have unfavorable functional outcome due to early neurological deterioration (END). The purpose of this study was to evaluate the applicability of machine learning algorithms to ...

Network Dynamics Governed by Lyapunov Functions: From Memory to Classification.

Trends in neurosciences
In 1982, John Hopfield published a neural network model for memory retrieval, a model that became a cornerstone in theoretical neuroscience. In a recent paper, Krotov and Hopfield built on these early studies and showed how a network that incorporate...

Deep Dive into Machine Learning Models for Protein Engineering.

Journal of chemical information and modeling
Protein redesign and engineering has become an important task in pharmaceutical research and development. Recent advances in technology have enabled efficient protein redesign by mimicking natural evolutionary mutation, selection, and amplification s...

DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images.

International journal of computer assisted radiology and surgery
PURPOSE: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fl...

Facial Expression Recognition Based on Weighted-Cluster Loss and Deep Transfer Learning Using a Highly Imbalanced Dataset.

Sensors (Basel, Switzerland)
Facial expression recognition (FER) is a challenging problem in the fields of pattern recognition and computer vision. The recent success of convolutional neural networks (CNNs) in object detection and object segmentation tasks has shown promise in b...

A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

Computational and mathematical methods in medicine
We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific targe...

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.

Computational and mathematical methods in medicine
Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper,...

Prediction of miRNA targets by learning from interaction sequences.

PloS one
MicroRNAs (miRNAs) are involved in a diverse variety of biological processes through regulating the expression of target genes in the post-transcriptional level. So, it is of great importance to discover the targets of miRNAs in biological research. ...