51,692 research outputs found
A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer
Identifying university students' weaknesses results in better learning and
can function as an early warning system to enable students to improve. However,
the satisfaction level of existing systems is not promising. New and dynamic
hybrid systems are needed to imitate this mechanism. A hybrid system (a
modified Recurrent Neural Network with an adapted Grey Wolf Optimizer) is used
to forecast students' outcomes. This proposed system would improve instruction
by the faculty and enhance the students' learning experiences. The results show
that a modified recurrent neural network with an adapted Grey Wolf Optimizer
has the best accuracy when compared with other models.Comment: 34 pages, published in PLoS ON
Learning text representation using recurrent convolutional neural network with highway layers
Recently, the rapid development of word embedding and neural networks has
brought new inspiration to various NLP and IR tasks. In this paper, we describe
a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN)
with highway layers. The highway network module is incorporated in the middle
takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module
in the first stage and provides the Convolutional Neural Network (CNN) module
in the last stage with the input. The experiment shows that our model
outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment
analysis task. Besides, the analysis of how sequence length influences the RCNN
with highway layers shows that our model could learn good representation for
the long text.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieva
Full Resolution Image Compression with Recurrent Neural Networks
This paper presents a set of full-resolution lossy image compression methods
based on neural networks. Each of the architectures we describe can provide
variable compression rates during deployment without requiring retraining of
the network: each network need only be trained once. All of our architectures
consist of a recurrent neural network (RNN)-based encoder and decoder, a
binarizer, and a neural network for entropy coding. We compare RNN types (LSTM,
associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study
"one-shot" versus additive reconstruction architectures and introduce a new
scaled-additive framework. We compare to previous work, showing improvements of
4.3%-8.8% AUC (area under the rate-distortion curve), depending on the
perceptual metric used. As far as we know, this is the first neural network
architecture that is able to outperform JPEG at image compression across most
bitrates on the rate-distortion curve on the Kodak dataset images, with and
without the aid of entropy coding.Comment: Updated with content for CVPR and removed supplemental material to an
external link for size limitation
Fleet Prognosis with Physics-informed Recurrent Neural Networks
Services and warranties of large fleets of engineering assets is a very
profitable business. The success of companies in that area is often related to
predictive maintenance driven by advanced analytics. Therefore, accurate
modeling, as a way to understand how the complex interactions between operating
conditions and component capability define useful life, is key for services
profitability. Unfortunately, building prognosis models for large fleets is a
daunting task as factors such as duty cycle variation, harsh environments,
inadequate maintenance, and problems with mass production can lead to large
discrepancies between designed and observed useful lives. This paper introduces
a novel physics-informed neural network approach to prognosis by extending
recurrent neural networks to cumulative damage models. We propose a new
recurrent neural network cell designed to merge physics-informed and
data-driven layers. With that, engineers and scientists have the chance to use
physics-informed layers to model parts that are well understood (e.g., fatigue
crack growth) and use data-driven layers to model parts that are poorly
characterized (e.g., internal loads). A simple numerical experiment is used to
present the main features of the proposed physics-informed recurrent neural
network for damage accumulation. The test problem consist of predicting fatigue
crack length for a synthetic fleet of airplanes subject to different mission
mixes. The model is trained using full observation inputs (far-field loads) and
very limited observation of outputs (crack length at inspection for only a
portion of the fleet). The results demonstrate that our proposed hybrid
physics-informed recurrent neural network is able to accurately model fatigue
crack growth even when the observed distribution of crack length does not match
with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer
perceptron, the stress intensity and Paris law layers, the cumulative damage
cell, as well as python driver scripts) used in this manuscript are publicly
available on GitHub at https://github.com/PML-UCF/pinn. The data and code are
released under the MIT Licens
Takens-inspired neuromorphic processor: a downsizing tool for random recurrent neural networks via feature extraction
We describe a new technique which minimizes the amount of neurons in the
hidden layer of a random recurrent neural network (rRNN) for time series
prediction. Merging Takens-based attractor reconstruction methods with machine
learning, we identify a mechanism for feature extraction that can be leveraged
to lower the network size. We obtain criteria specific to the particular
prediction task and derive the scaling law of the prediction error. The
consequences of our theory are demonstrated by designing a Takens-inspired
hybrid processor, which extends a rRNN with a priori designed delay external
memory. Our hybrid architecture is therefore designed including both, real and
virtual nodes. Via this symbiosis, we show performance of the hybrid processor
by stabilizing an arrhythmic neural model. Thanks to our obtained design rules,
we can reduce the stabilizing neural network's size by a factor of 15 with
respect to a standard system.Comment: 12 pages, 8 figure
Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients
Time Series Prediction with Recurrent Neural Networks using a Hybrid PSO-EA Algorithm
To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the recurrent neural network for the time series prediction. The experimental results show that our approach gives good performance in predicting the missing values from the time series
Movement and gesture recognition using deep learning and wearable-sensor technology
Pattern recognition of time-series signals for movement and gesture analysis plays an important role in many fields as diverse as healthcare, astronomy, industry and entertainment. As a new technique in recent years, Deep Learning (DL) has made
tremendous progress in computer vision and Natural Language Processing (NLP), but largely unexplored on its performance for
movement and gesture recognition from noisy multi-channel sensor signals. To tackle this problem, this study was undertaken to classify diverse movements and gestures using four developed DL
models: a 1-D Convolutional neural network (1-D CNN), a Recurrent neural network model with Long Short Term Memory
(LSTM), a basic hybrid model containing one convolutional layer and one recurrent layer (C-RNN), and an advanced hybrid model containing three convolutional layers and three recurrent layers
(3+3 C-RNN). The models will be applied on three different databases (DB) where the performances of models were compared.
DB1 is the HCL dataset which includes 6 human daily activities of 30 subjects based on accelerometer and gyroscope signals. DB2 and DB3 are both based on the surface electromyography (sEMG) signal for 17 diverse movements. The evaluation and discussion for the improvements and limitations of the models were made
according to the result
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