AIMC Topic: Neural Networks, Computer

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Prediction and evaluation of the severity of acute respiratory distress syndrome following severe acute pancreatitis using an artificial neural network algorithm model.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: To predict the risk and severity of acute respiratory distress syndrome (ARDS) following severe acute pancreatitis (SAP) by artificial neural networks (ANNs) model.

iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components.

Journal of theoretical biology
The 2'-O-methylation transferase is involved in the process of 2'-O-methylation. In catalytic processes, the 2-hydroxy group of the ribose moiety of a nucleotide accept a methyl group. This methylation process is a post-transcriptional modification, ...

Estimation of somatic cell count levels of hard cheeses using physicochemical composition and artificial neural networks.

Journal of dairy science
This study addresses the prediction of the somatic cell counts of the milk used in the production of sheep cheese using artificial neural networks. To achieve this objective, the neural network was designed using 33 parameters of the physicochemical ...

A Reweighted Scheme to Improve the Representation of the Neural Autoregressive Distribution Estimator.

Computational intelligence and neuroscience
The neural autoregressive distribution estimator(NADE) is a competitive model for the task of density estimation in the field of machine learning. While NADE mainly focuses on the problem of estimating density, the ability for dealing with other task...

Fast and scalable neural embedding models for biomedical sentence classification.

BMC bioinformatics
BACKGROUND: Biomedical literature is expanding rapidly, and tools that help locate information of interest are needed. To this end, a multitude of different approaches for classifying sentences in biomedical publications according to their coarse sem...

Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.

IEEE transactions on pattern analysis and machine intelligence
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few de...

Long short-term memory - Fully connected (LSTM-FC) neural network for PM concentration prediction.

Chemosphere
People have been suffering from air pollution for a decade in China, especially from PM (particulate matter with a diameter of less than 2.5 μm). Accurate prediction of air quality has great practical significance. In this paper, we propose a data-dr...

Classification and regression of spatio-temporal signals using NeuCube and its realization on SpiNNaker neuromorphic hardware.

Journal of neural engineering
OBJECTIVE: The objective of this work is to use the capability of spiking neural networks to capture the spatio-temporal information encoded in time-series signals and decode them without the use of hand-crafted features and vector-based learning and...

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

Journal of magnetic resonance imaging : JMRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety ...