AIMC Topic: Neural Networks, Computer

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Controlling Complexity of Cerebral Cortex Simulations-I: CxSystem, a Flexible Cortical Simulation Framework.

Neural computation
Simulation of the cerebral cortex requires a combination of extensive domain-specific knowledge and efficient software. However, when the complexity of the biological system is combined with that of the software, the likelihood of coding errors incre...

Exponential consensus of discrete-time non-linear multi-agent systems via relative state-dependent impulsive protocols.

Neural networks : the official journal of the International Neural Network Society
In this paper, we discuss the exponential consensus problem of discrete-time multi-agent systems with non-linear dynamics via relative state-dependent impulsive protocols. Impulsive protocols of which the impulsive instants are dependent on the weigh...

Domain Wall Motion-Based Dual-Threshold Activation Unit for Low-Power Classification of Non-Linearly Separable Functions.

IEEE transactions on biomedical circuits and systems
Recently, a great deal of scientific endeavour has been devoted to developing spin-based neuromorphic platforms owing to the ultra-low-power benefits offered by spin devices and the inherent correspondence between spintronic phenomena and the desired...

Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators.

Neural computation
This study focuses on predicting stock closing prices by using recurrent neural networks (RNNs). A long short-term memory (LSTM) model, a type of RNN coupled with stock basic trading data and technical indicators, is introduced as a novel method to p...

Automated retinopathy of prematurity screening using deep neural networks.

EBioMedicine
BACKGROUND: Retinopathy of prematurity (ROP) is the leading cause of childhood blindness worldwide. Automated ROP detection system is urgent and it appears to be a safe, reliable, and cost-effective complement to human experts.

Identification of a Novel Clinical Phenotype of Severe Malaria using a Network-Based Clustering Approach.

Scientific reports
The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 400,000 deaths are reported every year, mainly in African children. The diversity of clinical presentations associated w...

A deep convolutional neural network approach for astrocyte detection.

Scientific reports
Astrocytes are involved in various brain pathologies including trauma, stroke, neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, or chronic pain. Determining cell density in a complex tissue environment in microscopy images an...

Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks.

Computational intelligence and neuroscience
In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanc...

The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women.

Public health
OBJECTIVE: Artificial neural networks (ANNs) and classification and regression trees (CARTs) have been previously used for the prediction of cancer in several fields. In our study, we aim to investigate the diagnostic accuracy of three different meth...

Personalized Saliency and Its Prediction.

IEEE transactions on pattern analysis and machine intelligence
Nearly all existing visual saliency models by far have focused on predicting a universal saliency map across all observers. Yet psychology studies suggest that visual attention of different observers can vary significantly under specific circumstance...