Performance improvement for metro passenger flow forecast using spatio-temporal deep neural network

2021 ◽  
Author(s):  
Jaison Mulerikkal ◽  
Sajanraj Thandassery ◽  
Vinith Rejathalal ◽  
Deepa Merlin Dixon Kunnamkody
Keyword(s):  
Neural Network ◽  
Performance Improvement ◽  
Deep Neural Network ◽  
Passenger Flow ◽  
Spatio Temporal ◽  
Flow Forecast

scholarly journals Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network

2021 ◽  
Vol 13 (16) ◽  
pp. 3203
Author(s):  
Won-Kyung Baek ◽  
Hyung-Sup Jung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Performance Improvement ◽  
Oil Spill ◽  
Deep Neural Network ◽  
Support Vector ◽  
Sar Image ◽  
X Band

It is well known that the polarization characteristics in X-band synthetic aperture radar (SAR) image analysis can provide us with additional information for marine target classification and detection. Normally, dual-and single-polarized SAR images are acquired by SAR satellites, and then we must determine how accurate the marine mapping performance from dual-polarized (pol) images is versus the marine mapping performance from the single-pol images in a given machine learning model. The purpose of this study is to compare the performance of single- and dual-pol SAR image classification achieved by the support vector machine (SVM), random forest (RF), and deep neural network (DNN) models. The test image is a TerraSAR-X dual-pol image acquired from the 2007 Kerch Strait oil spill event. For this, 824,026 pixels and 1,648,051 pixels were extracted from the image for the training and test, respectively, and sea, ship, oil, and land objects were classified from the image by using the three machine learning methods. The mean f1-scores of the SVM, RF, and DNN models resulting from the single-pol image were approximately 0.822, 0.882, and 0.889, respectively, and those from the dual-pol image were about 0.852, 0.908, and 0.898, respectively. The performance improvement achieved by dual-pol was about 3.6%, 2.9%, and 1% in SVM, RF, and DNN, respectively. The DNN model had the best performance (0.889) in the single-pol test while the RF model was best (0.908) in the dual-pol test. The performance improvement was approximately 2.1% and not noticeable. If the condition that dual-pol images have two-times lower spatial resolution versus single-pol images in the azimuth direction is considered, a small improvement may not be valuable. Therefore, the results show that the performance improvement by X-band dual-pol image may be not remarkable when classifying the sea, ships, oil spills, and sea and land surfaces.

Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting

2020 ◽  
Vol 11 (2) ◽  
pp. 571-583 ◽  
Author(s):  
Mahdi Khodayar ◽  
Saeed Mohammadi ◽  
Mohammad E. Khodayar ◽  
Jianhui Wang ◽  
Guangyi Liu
Keyword(s):  
Neural Network ◽  
Solar Irradiance ◽  
Deep Neural Network ◽  
Spatio Temporal

scholarly journals Spatio-temporal feature based deep neural network for cell lineage analysis in microscopy images

2021 ◽  
Author(s):  
Siteng Chen ◽  
Andrew L. Paek ◽  
Kathleen A. Lasick ◽  
Suvithanandhini Loganathan ◽  
Janet Roveda ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Spatial Distance ◽  
Feature Based ◽  
Spatio Temporal ◽  
Microscopy Images ◽  
Temporal Feature ◽  
Lineage Analysis

Background: Time-lapse microscopy has been widely used in biomedical experiments because it can visualize the molecular activities of living cells in real time. However, biomedical researchers are still conducting cell lineage analysis manually. Developing automatic lineage tracing algorithms is a challenging task. In the past two decades, deep neural networks (DNNs) became have shown outstanding performance on computer vision tasks. They can learn complex visual features, capture long-range temporal dependencies, and have the potential to be used for automatic cell lineage analysis. Methods: In this study, we propose a multi-task spatio-temporal feature based deep neural network for cell lineages analysis (Cell-STN). The Cell-STN extracts spatio-temporal features from microscopy image sequences by leveraging our convolutional long short-term memory based core block. And the proposed Cell-STN utilized a task specific network to predict the cell location, the mitosis event, and the apoptosis event in a multi-task manner. Results: We evaluated the Cell-STN on three in-house datasets (MCF7, U2OS, and HCT116) and one public dataset (Fluo-N2DL-HeLa). For cell tracking, we used peak-wise precision, track-wise precision, end-peak precision, and spatial distance as metrics. The overall results showed the Cell-STN models outperform other state-of-the-art cell trackers. For mitosis and apoptosis tasks, we used accuracy, F1-score, temporal distance, and spatial distance as metrics. The Cell-STN models achieved the highest performance on all datasets. Conclusion: This study presented a novel DNNs approach for cell lineage analysis in microscopy images. The Cell-STN showed outstanding performance on the four datasets. Additionally, the Cell-STN required minimal training data and can be adapted to new biological event detection tasks by appending task-specific layers. This algorithm has the potential to be used in real-world biomedical research.

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