AIMC Topic: Neural Networks, Computer

Clear Filters Showing 21791 to 21800 of 31376 articles

Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit.

Molecules (Basel, Switzerland)
Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association informat...

Development of the Self Optimising Kohonen Index Network (SKiNET) for Raman Spectroscopy Based Detection of Anatomical Eye Tissue.

Scientific reports
Raman spectroscopy shows promise as a tool for timely diagnostics via in-vivo spectroscopy of the eye, for a number of ophthalmic diseases. By measuring the inelastic scattering of light, Raman spectroscopy is able to reveal detailed chemical charact...

Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

Artificial intelligence in medicine
OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health Organization, India has the highest number of pre-term births [1], with over 3.5 million babies born...

His-GAN: A histogram-based GAN model to improve data generation quality.

Neural networks : the official journal of the International Neural Network Society
Generative Adversarial Network (GAN) has become an active research field due to its capability to generate quality simulation data. However, two consistent distributions (generated data distribution and original data distribution) produced by GAN can...

Modeling azo dye removal by sono-fenton processes using response surface methodology and artificial neural network approaches.

Journal of environmental management
Textile industry wastewaters, which cause serious problems in the environment and human health, include synthetic dyes, complex organic pollutants, surfactants, and other toxic chemicals and therefore must be removed by advanced treatment methods. De...

Efficient training of interval Neural Networks for imprecise training data.

Neural networks : the official journal of the International Neural Network Society
This paper describes a robust and computationally feasible method to train and quantify the uncertainty of Neural Networks. Specifically, we propose a back propagation algorithm for Neural Networks with interval predictions. In order to maintain nume...

Use of artificial neural network for pretreatment verification of intensity modulation radiation therapy fields.

The British journal of radiology
OBJECTIVE: The accuracy of dose delivery for intensity modulated radiotherapy (IMRT) treatments should be determined by an accurate quality assurance procedure. In this work, we used artificial neural networks (ANNs) as an application for the pre-tre...

Readmission prediction using deep learning on electronic health records.

Journal of biomedical informatics
Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted inter...

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

Journal of medical systems
In medical image processing, Brain tumor segmentation plays an important role. Early detection of these tumors is highly required to give Treatment of patients. The patient's life chances are improved by the early detection of it. The process of diag...

Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans.

IEEE transactions on medical imaging
We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.g., pancreatic cyst), from abdominal CT scans. This is a challenging task in two aspects. First, some organs (e.g., the pancreas), are highl...