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Emotions recognition in human speech with deep learning models speech. Convolutional neural networks and recurrent neural networks with an LSTM memory cell were used
Detecting anomalies in network traffic using machine learning techniques, fully connected neural network and recurrent LSTM neural network were used as classification models
The power of deep learning to ligand-based novel drug discovery networks, recurrent neural networks, and several types of autoencoders. Several kinds of learning
Spectrum Hole Prediction in Cognitive Radio Systems by LSTM Neural Networks of a long short-term memory recurrent neural network models including classical, autoencoder, sparse
Control of a Technological Cycle of Production Process Based on a Neuro-Controller Model for
technological cycle of a production process is proposed.
A type of a neuro-controller based on recurrent neural
Deep learning for ICD coding: Looking for medical concepts in clinical documents in english and in French that was initially used for recurrent neural networks has been shown to provide powerful solution to tasks
KFU at CLEF eHealth 2017 Task 1: ICD-10 coding of English death certificates with recurrent neural networks we implemented recurrent neural networks to automatically assign ICD-10 codes to fragments of death
Hybrid genetic algorithm for the synthesis of dynamically controlled recurrent neural networksHybrid genetic algorithm for the synthesis of dynamically controlled recurrent neural networks
Comparing Recurrent Neural Networks and Symbolic regression methods Neural Networks (RNNs) and Symbolic Regression methods. Our study seeks to illuminate the effectiveness
Urban Traffic Flow Estimation System Based on Gated Recurrent Unit Deep Learning Methodology for Internet of Vehicles, and gated recurrent units (GRUs) layers has been structured to build the deep neural network in order
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