AIMC Topic: Recurrent Neural Networks

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Taming the chaos gently: a predictive alignment learning rule in recurrent neural networks.

Nature communications
Recurrent neural circuits often face inherent complexities in learning and generating their desired outputs, especially when they initially exhibit chaotic spontaneous activity. While the celebrated FORCE learning rule can train chaotic recurrent net...

Analyzing crises in global financial indices using Recurrent Neural Network based Autoencoder.

PloS one
In this study, we present a novel approach to analyzing financial crises of the global stock market by leveraging a modified Autoencoder model based on Recurrent Neural Network (RNN-AE). We analyze time series data from 24 global stock markets betwee...

An advanced fire detection system for assisting visually challenged people using recurrent neural network and sea-horse optimizer algorithm.

Scientific reports
The developing elderly population undergoes a high level of eyesight and mental impairment, which frequently results in a defeat of independence. That kind of person should do vital daily tasks like heating and cooking, with methods and devices inten...

The analysis of dynamic evaluation of online shopping satisfaction based on the recurrent neural network model.

Scientific reports
This work aims to accurately understand user satisfaction in online shopping, reflecting user preferences and promoting the development of online shopping. This work explores a behavioral prediction method for online shopping users using a Recurrent ...

A FPGA based recurrent neural networks-based impedance spectroscopy system for detection of YAKE in tuna.

Scientific reports
This paper evaluates the use of impedance spectroscopy combined with artificial intelligence. Both technologies have been widely used in food classification and it is proposed a way to improve classifications using recurrent neural networks that trea...

Modeling Normal and Abnormal Circuit Development with Recurrent Neural Networks.

Cold Spring Harbor perspectives in biology
Neural development must construct neural circuits that can perform the computations necessary for survival. However, many theoretical models of development do not explicitly address the computational goals of the resulting networks, or computations t...

Confidence interval forecasting model of small watershed flood based on compound recurrent neural networks and Bayesian.

PloS one
Flood forecasting exhibits rapid fluctuations, water level forecasting shows great uncertainty and inaccuracy in small watersheds, and the reliability and accuracy performance of traditional probability forecasting is often unbalanced. This study com...

Heuristically enhanced multi-head attention based recurrent neural network for denial of wallet attacks detection on serverless computing environment.

Scientific reports
Denial of Wallet (DoW) attacks are a cyber threat designed to utilize and deplete an organization's financial resources by generating excessive prices or charges in their cloud computing (CC) and serverless computing platforms. These threats are prim...

Universality of reservoir systems with recurrent neural networks.

Neural networks : the official journal of the International Neural Network Society
Approximation capability of reservoir systems whose reservoir is a recurrent neural network (RNN) is discussed. We show what we call uniform strong universality of RNN reservoir systems for a certain class of dynamical systems. This means that, given...

Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks.

Nature neuroscience
The temporal order of a sequence of events has been thought to be reflected in the ordered firing of neurons at different phases of theta oscillations. Here we assess this by measuring single neuron activity (1,420 neurons) and local field potentials...