AIMC Topic: Neural Networks, Computer

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Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN.

Computational intelligence and neuroscience
Coronavirus took the world by surprise and caused a lot of trouble in all the important fields in life. The complexity of dealing with coronavirus lies in the fact that it is highly infectious and is a novel virus which is hard to detect with exact p...

A graph-based approach to multi-source heterogeneous information fusion in stock market.

PloS one
The stock market is an important part of the capital market, and the research on the price fluctuation of the stock market has always been a hot topic for scholars. As a dynamic and complex system, the stock market is affected by various factors. How...

Transformer Neural Networks for Protein Family and Interaction Prediction Tasks.

Journal of computational biology : a journal of computational molecular cell biology
The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have comp...

Semi-supervised classification of fundus images combined with CNN and GCN.

Journal of applied clinical medical physics
PURPOSE: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and diffe...

A general deep learning model for bird detection in high-resolution airborne imagery.

Ecological applications : a publication of the Ecological Society of America
Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural...

Location-aware convolutional neural networks for graph classification.

Neural networks : the official journal of the International Neural Network Society
Graph patterns play a critical role in various graph classification tasks, e.g., chemical patterns often determine the properties of molecular graphs. Researchers devote themselves to adapting Convolutional Neural Networks (CNNs) to graph classificat...

A privacy preservation framework for feedforward-designed convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
A feedforward-designed convolutional neural network (FF-CNN) is an interpretable neural network with low training complexity. Unlike a neural network trained using backpropagation (BP) algorithms and optimizers (e.g., stochastic gradient descent (SGD...

Brain-inspired chaotic backpropagation for MLP.

Neural networks : the official journal of the International Neural Network Society
Backpropagation (BP) algorithm is one of the most basic learning algorithms in deep learning. Although BP has been widely used, it still suffers from the problem of easily falling into the local minima due to its gradient dynamics. Inspired by the fa...

Dynamic branching in a neural network model for probabilistic prediction of sequences.

Journal of computational neuroscience
An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, ...

RootPainter: deep learning segmentation of biological images with corrective annotation.

The New phytologist
Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interf...