AIMC Topic: Neural Networks, Computer

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Emergent neural turing machine and its visual navigation.

Neural networks : the official journal of the International Neural Network Society
Traditional Turing Machines (TMs) are symbolic whose hand-crafted representations are static and limited. Developmental Network 1 (DN-1) uses emergent representation to perform Turing Computation. But DN-1 lacks hierarchy in its internal representati...

Versatility of fuzzy logic in chronic diseases: A review.

Medical hypotheses
The review aims at providing current state of evidence in the field of medicine with fuzzy logic for diagnosing diseases. Literature reveals that fuzzy logic has been used effectively in medicine. Different types of methodologies have been applied to...

Training bioinspired sensors to classify flows.

Bioinspiration & biomimetics
We consider the inverse problem of classifying flow patterns from local sensory measurements. This problem is inspired by the ability of various aquatic organisms to respond to ambient flow signals, and is relevant for translating these abilities to ...

RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks.

PLoS computational biology
Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA st...

Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI.

Journal of neural engineering
OBJECTIVE: Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In ...

Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography.

AJR. American journal of roentgenology
OBJECTIVE: The purpose of this study was to evaluate improvement of convolutional neural network detection of high-grade small-bowel obstruction on conventional radiographs with increased training set size.

Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models.

BMC genomics
BACKGROUND: An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenom...

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.

Artificial intelligence in medicine
This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditi...

Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model.

Medical & biological engineering & computing
The aim of medical image fusion is to improve the clinical diagnosis accuracy, so the fused image is generated by preserving salient features and details of the source images. This paper designs a novel fusion scheme for CT and MRI medical images bas...

A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning.

Medical physics
PURPOSE: To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity-modulated radiation therapy trea...