AIMC Topic: Neural Networks, Computer

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A comprehensive graph neural network method for predicting triplet motifs in disease-drug-gene interactions.

Bioinformatics (Oxford, England)
MOTIVATION: The drug-disease, gene-disease, and drug-gene relationships, as high-frequency edge types, describe complex biological processes within the biomedical knowledge graph. The structural patterns formed by these three edges are the graph moti...

Deep Learning Approaches to Predict Geographic Atrophy Progression Using Three-Dimensional OCT Imaging.

Translational vision science & technology
PURPOSE: To evaluate the performance of various approaches of processing three-dimensional (3D) optical coherence tomography (OCT) images for deep learning models in predicting area and future growth rate of geographic atrophy (GA) lesions caused by ...

Real-Time sEMG Processing With Spiking Neural Networks on a Low-Power 5K-LUT FPGA.

IEEE transactions on biomedical circuits and systems
The accurate modeling of hand movement based on the analysis of surface electromyographic (sEMG) signals offers exciting opportunities for the development of complex prosthetic devices and human-machine interfaces, moving from discrete gesture recogn...

Role of short-term plasticity and slow temporal dynamics in enhancing time series prediction with a brain-inspired recurrent neural network.

Chaos (Woodbury, N.Y.)
Typical reservoir networks are based on random connectivity patterns that differ from brain circuits in two important ways. First, traditional reservoir networks lack synaptic plasticity among recurrent units, whereas cortical networks exhibit plasti...

Prediction of the Appropriate Temperature and Pressure for Polymer Dissolution Using Machine Learning Models.

Molecular informatics
The widespread use of polymer solutions in the chemical industry poses a significant challenge in determining optimal dissolution conditions. Traditionally, researchers have relied on experimental methods to estimate the processing parameters needed ...

A Neural Network Approach to Identify Left-Right Orientation of Anatomical Brain MRI.

Brain and behavior
PURPOSE: This study presents a novel application of deep learning to enhance the accuracy of left-right orientation identification in anatomical brain MRI scans. Left-right orientation misidentification in brain MRIs presents significant challenges d...

An Efficient Lightweight Multi Head Attention Gannet Convolutional Neural Network Based Mammograms Classification.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This research aims to use deep learning to create automated systems for better breast cancer detection and categorisation in mammogram images, helping medical professionals overcome challenges such as time consumption, feature extraction ...

Network Delay Forecast and Master-Slave Consistency Enhancement for Remote Surgical Robots.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The inevitable network delay can directly impact the process of remote surgeries and affect the master-slave motion consistency, and sudden changes in delay can compromise surgical safety.

Pre-trained convolutional neural network with transfer learning by artificial illustrated images classify power Doppler ultrasound images of rheumatoid arthritis joints.

The Journal of international medical research
OBJECTIVE: To study the classification performance of a pre-trained convolutional neural network (CNN) with transfer learning by artificial joint ultrasonography images in rheumatoid arthritis (RA).

Composition as Nonlinear Combination in Semantic Space: A Computational Characterization of Compound Processing.

Cognitive science
Most Chinese words are compounds formed through the combination of meaningful characters. Yet, due to compositional complexity, it is poorly understood how this combinatorial process affects the access to the whole-word meaning. In the present study,...