Real-time crash risk prediction on arterials based on LSTM-CNN

Pei, Li; Abdel-Aty, Mohamed; Yuan, Jinghui

doi:10.1016/j.aap.2019.105371

Cited by 237 publications

(87 citation statements)

References 39 publications

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“…The Optimization algorithm plays a crucial role during the training process to increase the performance of the LSTM network [65].To select the best optimizer in our proposed method; we compare the performance of root mean square (rmsprop), stochastic gradient descent (SGD), and adaptive moment estimation algorithm (Adam) optimizers. During the training process, we utilize 500 epochs to ensure that the training phase will be converged with min-batch size 16.…”

Section: B Our Proposed System Resultsmentioning

confidence: 99%

An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM

Shahin

Almotairi

2020

IEEE Access

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Echocardiography is an ultrasound-based imaging modality that helps the physician to visualize heart chambers and valves motion activity. Recently, deep learning plays an important role in several clinical computer-assisted diagnostic systems. There is a real need to employ deep learning methodologies to increase such systems. In this paper, we proposed a deep learning system to classify several echocardiography views and identify its physiological location. Firstly, the spatial CNN features are extracted from each frame in the echo-motion. Secondly, we proposed novel temporal features based on neutrosophic sets. The neutrosophic temporal motion features are extracted from echo-motion activity. To extract the deep CNN features, we activated a pre-trained deep ResNet model. Then, both spatial and neutrosophic temporal CNN features were fused based on features concatenation technique. Finally, the fused CNN features were fed into deep long short-term memory network to classify echo-cardio views and identify their location. During our experiments, we employed a public echocardiography dataset that consisted of 432 videos for eight cardio-views. We have investigated several pre-trained network activation performances. ResNet architecture activation achieved the best accuracy score among several pre-trained networks. The Proposed system based on fused spatial neutrosophic temporal deep features achieved 96.3% accuracy and 95.75% sensitivity. For the classification of cardio-views location, the proposed system achieved 99.1% accuracy. The proposed system achieved more accuracy than previous deep learning methods with a significant decrease in the training time cost. The experimental results showed promising results for our proposed approach.

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Section: B Our Proposed System Resultsmentioning

confidence: 99%

An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM

Shahin

Almotairi

2020

IEEE Access

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“…The two‐CNN model can automatically detect important features without any human supervision. It has been proven that the performance of LSTM could be improved by augmenting it with CNNs for the time series classification problem and image captioning and that the LSTM–CNN model achieved state‐of‐the‐art performance compared with other baseline methods 29–31 . Inspired by their achievements, we propose a hybrid neural network model to predict interpretable drug side‐effects.…”

Section: Architecture Of the Proposed Modelmentioning

confidence: 99%

Descriptive prediction of drug side‐effects using a hybrid deep learning model

Lee

Chen

2021

Int J Intell Syst

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In this study, we developed a hybrid deep learning (DL) model, which is one of the first interpretable hybrid DL models with Inception modules, to give a descriptive prediction of drug side‐effects. The model consists of a graph convolutional neural network (GCNN) with Inception modules to allow more efficient learning of drug molecular features and bidirectional long short‐term memory (BiLSTM) recurrent neural networks to associate drug structure with its associated side effects. The outputs from the two networks (GCNN and BiLSTM) are then concatenated and a fully connected network is used to predict the side effects of drugs. Our model achieves an AUC score of 0.846 irrespective of what classification threshold is chosen. It has a precision score of 0.925 and the Bilingual Evaluation Understudy (BLEU) scores obtained were 0.973, 0.938, 0.927, and 0.318 which show significant achievements despite the fact that a small drug data set is used for adverse drug reaction (ADR) prediction. Moreover, the model is capable of accurately structuring correct words to describe drug side‐effects and associates them with its drug name and molecular structure. The predicted drug structure and ADR relation will provide a reference for preclinical safety pharmacology studies and facilitate the identification of ADRs during early phases of drug development. It can also help detect unknown ADRs embedded in existing drugs, hence contributing significantly to the science of pharmacovigilance.

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“…Prevention is more effective than a cure. Lastly, the LSTM model can also be widely applied in many fields, such as vessel trajectory prediction [59], tidal level forecasting [60], financial market forecasting [61], and real-time crash risk prediction.…”

Section: Contribution Of This Papermentioning

confidence: 99%

Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan

Yang

Chen

et al. 2021

Mathematics

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The World Health Organization has urged countries to prioritize dementia in their public health policies. Dementia poses a tremendous socioeconomic burden, and the accurate prediction of the annual increase in prevalence is essential for establishing strategies to cope with its effects. The present study established a model based on the architecture of the long short-term memory (LSTM) neural network for predicting the number of dementia cases in Taiwan, which considers the effects of age and sex on the prevalence of dementia. The LSTM network is a variant of recurrent neural networks (RNNs), which possesses a special gate structure and avoids the problems in RNNs of gradient explosion, gradient vanishing, and long-term memory failure. A number of patients diagnosed as having dementia from 1997 to 2017 was collected in annual units from a data set extracted from the Health Insurance Database of the Ministry of Health and Welfare in Taiwan. To further verify the validity of the proposed model, the LSTM network was compared with three types of models: statistical models (exponential smoothing (ETS), autoregressive integrated moving average model (ARIMA), trigonometric seasonality, Box–Cox transformation, autoregressive moving average errors, and trend seasonal components model (TBATS)), hybrid models (support vector regression (SVR), particle swarm optimization–based support vector regression (PSOSVR)), and deep learning model (artificial neural networks (ANN)). The mean absolute percentage error (MAPE), root-mean-square error (RMSE), mean absolute error (MAE), and R-squared (R2) were used to evaluate the model performances. The results indicated that the LSTM network has higher prediction accuracy than the three types of models for forecasting the prevalence of dementia in Taiwan.

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Real-time crash risk prediction on arterials based on LSTM-CNN

Cited by 237 publications

References 39 publications

An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM

An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM

Descriptive prediction of drug side‐effects using a hybrid deep learning model

Forecasting of the Prevalence of Dementia Using the LSTM Neural Network in Taiwan

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