scholarly journals SHORT TERM URBAN TRAFFIC FORECASTING USING DEEP LEARNING

2018 ◽  
Vol IV-4/W7 ◽  
pp. 3-10 ◽  
Author(s):  
G. Albertengo ◽  
W. Hassan
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Learning Algorithm ◽  
Urban Traffic ◽  
Accurate Information ◽  
Short Term ◽  
Current Time ◽  
Traffic Forecasting ◽  
Deep Learning Algorithm ◽  
Urban Traffic Control

<p><strong>Abstract.</strong> In today’s world, the number of vehicles is increasing rapidly in developing countries and China and remains stable in all other countries, while road infrastructure mostly remains unchanged, causing congestion problems in many cities. Urban Traffic Control systems can be helpful in counteracting congestion if they receive accurate information on traffic flow. So far, these data are collected by sensors on roads, such as Inductive Loops, which are rather expensive to install and maintain. A less expensive approach could be to use a limited number of sensors combined with Artificial Intelligence to forecast the intensity of traffic at any point in a city. In this paper, we propose a simple yet accurate short-term urban traffic forecasting solution applying supervised window-based regression analysis using Deep Learning algorithm. Experimental results show that is it possible to forecast the intensity of traffic with good accuracy just monitoring its intensity in the last few minutes. The most significant result, in our opinion, is that the machine can generate accurate predictions even with no knowledge of the current time, the day of the week or the type of the day (holiday, weekday, etc).</p>

scholarly journals Deep learning algorithm evaluation of hypertension classification in less photoplethysmography signals conditions

2021 ◽  
Vol 54 (3-4) ◽  
pp. 439-445
Author(s):  
Chih-Ta Yen ◽  
Sheng-Nan Chang ◽  
Cheng-Hong Liao
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Training Data ◽  
Short Term ◽  
Deep Learning Algorithm ◽  
Testing Dataset ◽  
Data Points ◽  
Optimal Classification ◽  
Long Short Term Memory

This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset consisted of 2100 data points. During the experiment, the proportion of training and testing datasets was 8:2. The model demonstrated an optimal classification accuracy of 76% when the testing dataset was used.

scholarly journals A Novel Deep Learning Algorithm for Groundwater Level Prediction based on Spatiotemporal Attention Mechanism

2020 ◽  
Author(s):  
Chong Chen ◽  
Han Zhou ◽  
Hui Zhang ◽  
Lulu Chen ◽  
Zhu Yan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Groundwater Level ◽  
Learning Algorithm ◽  
Attention Mechanism ◽  
Short Term ◽  
Groundwater Levels ◽  
Deep Learning Algorithm ◽  
Term Prediction ◽  
Groundwater Level Prediction

Abstract Groundwater resources play a vital role in production, human life and economic development. Effective prediction of groundwater levels would support better water resources management. Although machine learning algorithms have been studied and applied in many domains with good enough results, the researches in hydrologic domains are not adequate. This paper proposes a novel deep learning algorithm for groundwater level prediction based on spatiotemporal attention mechanism. Short-term (one month ahead) and long-term (twelve months ahead) prediction of groundwater level are conducted with observed groundwater levels collected from several boreholes in the middle reaches of the Heihe River Basin in northwestern China. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used to evaluate the performance of the proposed algorithm and several baseline models (i.e., SVR, Support Vector Regression; FNN, Feedforward Neural Networks; LSTM, Long Short-Term Memory neural network). The results show that the proposed model can effectively improve the prediction accuracy compared to the baseline models with MAE of 0.0754, RMSE of 0.0952 for short-term prediction and MAE of 0.0983, RMSE of 0.1215 for long-term prediction. This study provides a feasible and accurate approach for groundwater prediction which may facilitate decision making for water management.

scholarly journals A Management Model of Small- and Medium-Sized Enterprises Based on Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Junshu Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Neural Network Model ◽  
Learning Algorithm ◽  
Management Model ◽  
Short Term ◽  
Traditional Management ◽  
Deep Learning Algorithm ◽  
Management Method ◽  
Management Effect

The traditional management method of small- and medium-sized enterprises (SMEs) has the problem of poor turnover prediction effect. Therefore, this paper proposes a management model of SMEs based on the deep learning algorithm. Firstly, the proposed system analyzes the characteristics of SMEs, and based on the deep learning algorithm, the gating structure of the management of SMEs is designed. The internal structure diagram of the long- and short-term neural network is given, and the circulating neural network model of the management of SMEs is constructed. Finally, the experimental indexes of the management model of SMEs are designed, and the comparative experiments are carried out. The experimental results show that the proposed method is more accurate in predicting the turnover of SMEs and has better management effect on them.

scholarly journals Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

2018 ◽  
Author(s):  
Bing Yu ◽  
Haoteng Yin ◽  
Zhanxing Zhu
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Time Series Prediction ◽  
Urban Traffic ◽  
Traffic Forecasting ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Temporal Correlations ◽  
Temporal Graph ◽  
Spatio Temporal

Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

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