AIMC Topic: Neural Networks, Computer

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Red blood cell phenotyping from 3D confocal images using artificial neural networks.

PLoS computational biology
The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fin...

Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks.

PLoS computational biology
Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes...

A comprehensive swarming intelligent method for optimizing deep learning-based object detection by unmanned ground vehicles.

PloS one
Unmanned ground vehicles (UGVs) are an important research application of artificial intelligence. In particular, the deep learning-based object detection method is widely used in UGV-based environmental perception. Good experimental results are achie...

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks.

PloS one
Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become...

Mapping soil salinity using a combined spectral and topographical indices with artificial neural network.

PloS one
Monitoring the status of natural and ecological resources is necessary for conservation and protection. Soil is one of the most important environmental resources in agricultural lands and natural resources. In this research study, we used Landsat 8 a...

A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens.

Cancer cytopathology
BACKGROUND: Although deep learning algorithms for clinical cytology have recently been developed, their application to practical assistance systems has not been achieved. In addition, whether deep learning systems (DLSs) can perform diagnoses that ca...

A theory of capacity and sparse neural encoding.

Neural networks : the official journal of the International Neural Network Society
Motivated by biological considerations, we study sparse neural maps from an input layer to a target layer with sparse activity, and specifically the problem of storing K input-target associations (x,y), or memories, when the target vectors y are spar...

Experimental stability analysis of neural networks in classification problems with confidence sets for persistence diagrams.

Neural networks : the official journal of the International Neural Network Society
We investigate classification performance of neural networks (NNs) based on topological insight in an attempt to guarantee stability of their inference. NNs which can accurately classify a dataset map it into a hidden space while disentangling intert...

Efficient learning with augmented spikes: A case study with image classification.

Neural networks : the official journal of the International Neural Network Society
Efficient learning of spikes plays a valuable role in training spiking neural networks (SNNs) to have desired responses to input stimuli. However, current learning rules are limited to a binary form of spikes. The seemingly ubiquitous phenomenon of b...

AQI time series prediction based on a hybrid data decomposition and echo state networks.

Environmental science and pollution research international
A hybrid AQI time series prediction model is proposed based on EWT-SE-VMD secondary decomposition, ICA (imperialist competitive algorithm) feature selection, and ESN (echo state network) neural network. Firstly, EWT (empirical wavelet transform) and ...