CNN LSTM Network Architecture for Modeling Software Reliability

2019 ◽  
pp. 210-217
Author(s):  
Kamill Gusmanov
Keyword(s):  
Software Reliability ◽  
Network Architecture ◽  
Modeling Software ◽  
Lstm Network

Salp Swarm Optimizer for Modeling Software Reliability Prediction Problems

2021 ◽  
Author(s):  
Sofian Kassaymeh ◽  
Salwani Abdullah ◽  
Mohamad Al-Laham ◽  
Mohammed Alweshah ◽  
Mohammed Azmi Al-Betar ◽  
...  
Keyword(s):  
Software Reliability ◽  
Reliability Prediction ◽  
Software Reliability Prediction ◽  
Modeling Software ◽  
Prediction Problems

scholarly journals Multi-Headed Conv-LSTM Network for Heart Rate Estimation during Daily Living Activities

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5212
Author(s):  
Michał Wilkosz ◽  
Agnieszka Szczęsna
Keyword(s):  
Heart Rate ◽  
Network Architecture ◽  
Short Term Memory ◽  
Automated Analysis ◽  
Absolute Error ◽  
Rate Estimation ◽  
Non Invasive ◽  
Major Direction ◽  
Lstm Network ◽  
Heart Rate Estimation

Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during physical activity under free-living conditions. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. Because of their generalization capabilities, deep learning methods can be a major direction in the search for a heart rate estimation solution based on signals from wearable devices. A novel multi-headed convolutional neural network model enriched with long short-term memory cells (MH Conv-LSTM DeepPPG) was proposed for the estimation of heart rate based on signals measured by a wrist-worn wearable device, such as PPG and acceleration signals. For the PPG-DaLiA dataset, the proposed solution improves the performance of previously proposed methods. An experimental approach was used to develop the final network architecture. The average mean absolute error (MAE) of the final solution was 6.28 bpm and Pearson’s correlation coefficient between the estimated and true heart rate values was 0.85.

scholarly journals Predicting LQ45 financial sector indices using RNN-LSTM

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Seng Hansun ◽  
Julio Christian Young
Keyword(s):  
Financial Markets ◽  
Network Architecture ◽  
Stock Price ◽  
Short Term Memory ◽  
Short Term ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Short Period ◽  
Long Short Term Memory ◽  
Lstm Network

AbstractAs one of the most popular financial market instruments, the stock has formed one of the most massive and complex financial markets in the world. It could handle millions of transactions within a short period of time and highly unpredictable. In this study, we aim to implement a famous Deep Learning method, namely the long short-term memory (LSTM) networks, for the stock price prediction. We limit the stocks to those that are included in the LQ45 financial sectors indices, i.e., BBCA, BBNI, BBRI, BBTN, BMRI, and BTPS. Rather than using too deep network architecture, we propose using a simple three-layer LSTM network architecture to predict the stocks’ closing prices. We found that the prediction results fall in the reasonable forecasting category. Moreover, it is worth noting that two of the considered stocks, namely, BBCA and BMRI, have the lowest MAPE values at 19.1020 and 18.6135, which fall in the good forecasting results. Hence, the proposed LSTM model is most recommended to be used on those two stocks.

scholarly journals Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7508
Author(s):  
Zhen Ma ◽  
José J. J. Machado ◽  
João Manuel R. S. Tavares
Keyword(s):  
Anomaly Detection ◽  
Network Architecture ◽  
Vision Research ◽  
Large Scale ◽  
Three Dimensional ◽  
Neural Network Architecture ◽  
Temporal Features ◽  
Weakly Supervised ◽  
Lstm Network ◽  
High Computational Efficiency

Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness.

scholarly journals USING LSTM NETWORK FOR SOLVING THE MULTIDIMENTIONAL TIME SERIES FORECASTING PROBLEM

2021 ◽  
Vol 2 (68) ◽  
pp. 43-48
Author(s):  
M. Obrubov ◽  
S. Kirillova
Keyword(s):  
Time Series ◽  
Recurrent Neural Networks ◽  
Network Architecture ◽  
Time Series Prediction ◽  
Prediction Problem ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Multidimensional Time Series ◽  
Lstm Network

The article discusses using of the recurrent neural networks technology to the multidimensional time series prediction problem. There is an experimental determination of the neural network architecture and its main hyperparameters carried out to achieve the minimum error. The revealed network structure going to be used further to detect anomalies in multidimensional time series.

scholarly journals Automatic Recognition of Communication Signal Modulation Based on the Multiple-Parallel Complex Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhen Huang ◽  
Chengkang Li ◽  
Qiang Lv ◽  
Rijian Su ◽  
Kaibo Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Radio Signal ◽  
Signal Modulation ◽  
Neural Network Architecture ◽  
Communication Signals ◽  
Time Frequency ◽  
Modulation Pattern ◽  
Lstm Network

This paper implements a deep learning-based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple-parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all-digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time-frequency analysis methods, a time-frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time-frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.

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