scholarly journals Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks

2021 ◽  
Author(s):  
P. Jiang ◽  
I. Bychkov ◽  
J. Liu ◽  
A. Hmelnov
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Support Vector ◽  
Temporal Attention ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Spatio Temporal

Forecasting of air pollutant concentration, which is influenced by air pollution accumulation, traffic flow and industrial emissions, has attracted extensive attention for decades. In this paper, we propose a spatio-temporal attention convolutional long short term memory neural networks (Attention-CNN-LSTM) for air pollutant concentration forecasting. Firstly, we analyze the Granger causalities between different stations and establish a hyperparametric Gaussian vector weight function to determine spatial autocorrelation variables, which is used as part of the input feature. Secondly, convolutional neural networks (CNN) is employed to extract the temporal dependence and spatial correlation of the input, while feature maps and channels are weighted by attention mechanism, so as to improve the effectiveness of the features. Finally, a depth long short term memory (LSTM) based time series predictor is established for learning the long-term and short-term dependence of pollutant concentration. In order to reduce the effect of diverse complex factors on LSTM, inherent features are extracted from historical air pollutant concentration data meteorological data and timestamp information are incorporated into the proposed model. Extensive experiments were performed using the Attention-CNNLSTM, autoregressive integrated moving average (ARIMA), support vector regression (SVR), traditional LSTM and CNN, respectively. The results demonstrated that the feasibility and practicability of Attention-CNN-LSTM on estimating CO and NO concentration.

scholarly journals MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 551-558 ◽  
Author(s):  
M. Rußwurm ◽  
M. Körner
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Land Cover ◽  
Short Term Memory ◽  
Land Cover Classification ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Classification Techniques ◽  
Long Short Term Memory

<i>Land cover classification (LCC)</i> is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how <i>long short-term memory</i> (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, <i>i.e.</i>, LSTM and <i>recurrent neural network</i> (RNN), with a classical non-temporal <i>convolutional neural network</i> (CNN) model and an additional <i>support vector machine</i> (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

scholarly journals Finger-Vein Verification Based on LSTM Recurrent Neural Networks

2019 ◽  
Vol 9 (8) ◽  
pp. 1687 ◽  
Author(s):  
Huafeng Qin ◽  
Peng Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Vein Pattern ◽  
Term Memory ◽  
Finger Vein ◽  
Spatial Dependencies ◽  
Long Short Term Memory

Finger-vein biometrics has been extensively investigated for personal verification. A challenge is that the finger-vein acquisition is affected by many factors, which results in many ambiguous regions in the finger-vein image. Generally, the separability between vein and background is poor in such regions. Despite recent advances in finger-vein pattern segmentation, current solutions still lack the robustness to extract finger-vein features from raw images because they do not take into account the complex spatial dependencies of vein pattern. This paper proposes a deep learning model to extract vein features by combining the Convolutional Neural Networks (CNN) model and Long Short-Term Memory (LSTM) model. Firstly, we automatically assign the label based on a combination of known state of the art handcrafted finger-vein image segmentation techniques, and generate various sequences for each labeled pixel along different directions. Secondly, several Stacked Convolutional Neural Networks and Long Short-Term Memory (SCNN-LSTM) models are independently trained on the resulting sequences. The outputs of various SCNN-LSTMs form a complementary and over-complete representation and are conjointly put into Probabilistic Support Vector Machine (P-SVM) to predict the probability of each pixel of being foreground (i.e., vein pixel) given several sequences centered on it. Thirdly, we propose a supervised encoding scheme to extract the binary vein texture. A threshold is automatically computed by taking into account the maximal separation between the inter-class distance and the intra-class distance. In our approach, the CNN learns robust features for vein texture pattern representation and LSTM stores the complex spatial dependencies of vein patterns. So, the pixels in any region of a test image can then be classified effectively. In addition, the supervised information is employed to encode the vein patterns, so the resulting encoding images contain more discriminating features. The experimental results on one public finger-vein database show that the proposed approach significantly improves the finger-vein verification accuracy.

scholarly journals Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xiaolu Wei ◽  
Binbin Lei ◽  
Hongbing Ouyang ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Temporal Pattern ◽  
Short Term Memory ◽  
Stock Index ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

This study attempts to predict stock index prices using multivariate time series analysis. The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue. The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve better prediction performance over traditional deep neural networks, such as recurrent neural network (RNN), convolutional neural network (CNN), and long and short-term time series network (LSTNet).

scholarly journals Human Action Recognition in Videos using Convolution Long Short-Term Memory Network with Spatio-Temporal Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 25-33
Author(s):  
Ashok Sarabu ◽  
Ajit Kumar Santra
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Human Action Recognition ◽  
Human Action ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Spatio Temporal

Two-stream convolutional networks plays an essential role as a powerful feature extractor in human action recognition in videos. Recent studies have shown the importance of two-stream Convolutional Neural Networks (CNN) to recognize human action recognition. Recurrent Neural Networks (RNN) has achieved the best performance in video activity recognition combining CNN. Encouraged by CNN's results with RNN, we present a two-stream network with two CNNs and Convolution Long-Short Term Memory (CLSTM). First, we extricate Spatio-temporal features using two CNNs using pre-trained ImageNet models. Second, the results of two CNNs from step one are combined and fed as input to the CLSTM to get the overall classification score. We also explored the various fusion function performance that combines two CNNs and the effects of feature mapping at different layers. And, conclude the best fusion function along with layer number. To avoid the problem of overfitting, we adopt the data augmentation techniques. Our proposed model demonstrates a substantial improvement compared to the current two-stream methods on the benchmark datasets with 70.4% on HMDB-51 and 95.4% on UCF-101 using the pre-trained ImageNet model. Doi: 10.28991/esj-2021-01254 Full Text: PDF

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