AIMC Topic: Neural Networks, Computer

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Event-triggered impulsive control on quasi-synchronization of memristive neural networks with time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This paper discusses the quasi-synchronization of memristive neural networks (MNNs) with time-varying delays via event-triggered impulsive and state feedback control approaches. The choice of different initial conditions may lead to the unexpected pa...

Rainfall time series disaggregation in mountainous regions using hybrid wavelet-artificial intelligence methods.

Environmental research
In mountainous regions, rainfall can be extremely variable in space and time. The need to simulate rainfall time series at different scales on one hand and the lack of recording such parameters in small scales because of administrative and economic p...

Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device.

Computers in biology and medicine
BACKGROUND: Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wri...

Using neural attention networks to detect adverse medical events from electronic health records.

Journal of biomedical informatics
The detection of Adverse Medical Events (AMEs) plays an important role in disease management in ensuring efficient treatment delivery and quality improvement of health services. Recently, with the rapid development of hospital information systems, a ...

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning.

Scientific reports
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow fo...

Recurrent Neural Network for Predicting Transcription Factor Binding Sites.

Scientific reports
It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome th...

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification.

Sensors (Basel, Switzerland)
Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this p...

A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm.

Computational intelligence and neuroscience
The change of the number of sunspots has a great impact on the Earth's climate, agriculture, communications, natural disasters, and other aspects, so it is very important to predict the number of sunspots. Aiming at the chaotic characteristics of mon...

Dual vigilance fuzzy adaptive resonance theory.

Neural networks : the official journal of the International Neural Network Society
Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to their internal categorical representation. This study extends the capabilities of ART by incorporating multiple vigilance thresholds in a single network: stricter (...

Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks.

Environmental pollution (Barking, Essex : 1987)
Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum ...