Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

Ahmad, Ali; Zhu, Yanmin; Zakarya, Muhammad

doi:10.1016/j.neunet.2021.10.021

Cited by 208 publications

(55 citation statements)

References 34 publications

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“…Therefore, the position of coal mining subsidence area obtained by field investigation is imported into ArcGIS software in this study, and five buffer zones are constructed with a radius of 100 m to analyze the influence of coal mining activities on the landslide sensitivity (the details are illustrated in Figure 3(j) ). In the process of road construction, cutting slope may lead to slope failure, reduce the stability of the slope, and accelerate the occurrence of the landslide [ 25 ]. Therefore, the distance from road is one of the human activity factors that should be considered in the landslide sensitivity modeling [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

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A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

Tao

2022

Computational Intelligence and Neuroscience

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The purpose of this study is to compare the results of the frequency ratio (FR) model with the weight of evidence (WOE) and the logical regression (LR) methods when applied to the landslide susceptibility evaluation in coal mining subsidence areas. Key geological disaster prevention and control areas are taken as the research areas. Field investigation is carried out according to the recorded landslide disaster points in the past five years, and 86 landslide disaster points are determined from the remote sensing satellite images. Furthermore, 12 factors affecting the occurrence of landslide are selected as landslide sensitivity evaluation factors. Among them, slope degree, curvature, elevation, and slope aspect are derived using the digital elevation model (DEM) through 30 m × 30 m resolution. The DEM datasets are derived from the geospatial data cloud, lithology datasets are derived from the geological lithology maps, and land use type map is derived from the current situation of national land use. The distances between roads and coal mining subsidence areas are calculated according to field investigation and remote sensing image interpretation results. In addition, the evaluation model includes an annual rainfall distribution map. Finally, the accuracy of three models is compared by ROC curve analysis. The elevation results demonstrate that the frequency ratio-logic regression (FR-LR) model takes the maximum accurateness of 0.913, subsequent to the FR model and the frequency ratio-weight of evidence (FR-WOE) model, respectively. Thus, using LR method based on the FR model has guiding significance for predicting the landslide sensitivity in coal mining. This reduces probable risks and disasters that affect human health. Subsequently, this ensures higher safety from the healthcare perspective in the mining fields.

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Section: Methodsmentioning

confidence: 99%

“…In the process of road construction, cutting slope may lead to slope failure, reduce the stability of the slope, and accelerate the occurrence of the landslide [ 25 ]. Therefore, the distance from road is one of the human activity factors that should be considered in the landslide sensitivity modeling [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

Tao

2022

Computational Intelligence and Neuroscience

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“…the model produces good results on the seen data (training set) but performs poorly on the unseen data(test set). Conversely, Spatio-temporal correlation structures have been proven to improve the effectiveness of models [ 3 – 6 ]. Therefore, the dataset was augmented artificially to increase the model’s effective size, as image data augmentation not only prevents CNN from learning irrelevant patterns, overfitting, and memorizing exact details of training images but also supports temporal and spatial relationships.…”

Section: Methodsmentioning

confidence: 99%

Diagnosing and differentiating viral pneumonia and COVID-19 using X-ray images

Kör

Erbay

Yurttakal

2022

Multimed Tools Appl

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Coronavirus-caused diseases are common worldwide and might worsen both human health and the world economy. Most people may instantly encounter coronavirus in their life and may result in pneumonia. Nowadays, the world is fighting against the new coronavirus: COVID-19. The rate of increase is high, and the world got caught the disease unprepared. In most regions of the world, COVID-19 test is not possible due to the absence of the diagnostic kit, even if the kit exists, its false-negative (giving a negative result for a person infected with COVID-19) rate is high. Also, early detection of COVID-19 is crucial to keep its morbidity and mortality rates low. The symptoms of pneumonia are alike, and COVID-19 is no exception. The chest X-ray is the main reference in diagnosing pneumonia. Thus, the need for radiologists has been increased considerably not only to detect COVID-19 but also to identify other abnormalities it caused. Herein, a transfer learning-based multi-class convolutional neural network model was proposed for the automatic detection of pneumonia and also for differentiating non-COVID-19 pneumonia and COVID-19. The model that inputs chest X-ray images is capable of extracting radiographic patterns on chest X-ray images to turn into valuable information and monitor structural differences in the lungs caused by the diseases. The model was developed by two public datasets: Cohen dataset and Kermany dataset. The model achieves an average training accuracy of 0.9886, an average training recall of 0.9829, and an average training precision of 0.9837. Moreover, the average training false-positive and false-negative rates are 0.0085 and 0.0171, respectively. Conversely, the model’s test set metrics such as average accuracy, average recall, and average precision are 97.78%, 96.67%, and 96.67%, respectively. According to the simulation results, the proposed model is promising, can quickly and accurately classify chest images, and helps doctors as the second reader in their final decision.

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“…is is considered the information flow's forward assignment procedure. e sample signal is propagated forward layer by layer through the sigmoid function in the forward propagation process, and the state of each layer of neurons only influences the state of the subsequent layer of neurons [8].…”

Section: E Bp Algorithmmentioning

confidence: 99%

Evaluating the Vocal Music Teaching Using Backpropagation Neural Network

Cao

2022

Mobile Information Systems

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The vocal music teaching for evaluating performers is affected by multiple factors. Evaluators are greatly influenced by subjective factors in scoring outputs. The backpropagation (BP) neural network provides a novel technology that can theoretically simulate any nonlinear continuous function within a certain accuracy range. The backpropagation neural network is composed of adaptive feedforward learning network that is widely used in artificial intelligence (AI). In addition, the backpropagation neural network can simulate the nonlinear mapping composed of various factors. The novelty of the neural network is that it can model the nonlinear process without knowing the cause of the data, which can overcome the human subjective arbitrariness and make the evaluation outcomes. Furthermore, accurate and effective scoring systems can be designed using neural networks. In this paper, we establish a vocal music evaluation research system in order to objectivize each vocal music teaching evaluation index. To do so, we use the score vector as the input and obtain a reasonable and objective output score through the backpropagation neural network. Moreover, according to the characteristics of the backpropagation neural network, the factors of vocal music teaching evaluation are analyzed, and a backpropagation neural network model for vocal music teaching evaluation and evaluation is constructed. The experimental outcomes demonstrate that the trained backpropagation network can simulate a stable vocal music teaching evaluation research system. Furthermore, we observed that the backpropagation neural network can be well utilized for vocal music teaching evaluation research.

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Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction

Cited by 208 publications

References 34 publications

A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area

Diagnosing and differentiating viral pneumonia and COVID-19 using X-ray images

Evaluating the Vocal Music Teaching Using Backpropagation Neural Network

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