scholarly journals Neural Network Based Country Wise Risk Prediction of COVID-19

2020 ◽  
Author(s):  
Ratnabali Pal ◽  
Arif Ahmed Sekh ◽  
Samarjit Kar ◽  
Dilip K. Prasad
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Bayesian Optimization ◽  
Weather Data ◽  
Risk Category ◽  
The Novel ◽  
Optimization Framework ◽  
Long Duration ◽  
Trend Data ◽  
Country Specific

The recent worldwide outbreak of the novel corona-virus (COVID-19) opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow Long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimized and automatically design country-specific networks. We have combined the trend data and weather data together for the prediction. The results show that the proposed pipeline outperforms against state-of-the-art methods for 170 countries data and can be a useful tool for such risk categorization. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

scholarly journals Neural Network Based Country Wise Risk Prediction of COVID-19

2020 ◽  
Vol 10 (18) ◽  
pp. 6448
Author(s):  
Ratnabali Pal ◽  
Arif Ahmed Sekh ◽  
Samarjit Kar ◽  
Dilip K. Prasad
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Bayesian Optimization ◽  
Weather Data ◽  
Risk Category ◽  
The Novel ◽  
Long Duration ◽  
Trend Data ◽  
Novel Coronavirus ◽  
Country Specific

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier.

scholarly journals A Bayesian Optimization Framework for Neural Network Compression

2019 ◽  
Author(s):  
Xingchen Ma ◽  
Amal Rannen Triki ◽  
Maxim Berman ◽  
Christos Sagonas ◽  
Jacques Cali ◽  
...  
Keyword(s):  
Neural Network ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Network Compression

scholarly journals Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 718 ◽  
Author(s):  
Park ◽  
Kim ◽  
Lee ◽  
Kim ◽  
Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Prediction Model ◽  
Short Term Memory ◽  
Weather Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

scholarly journals Research on PM2.5 Spatiotemporal Forecasting Model Based on LSTM Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fang Zhao ◽  
Ziyi Liang ◽  
Qiyan Zhang ◽  
Dewen Seng ◽  
Xiyuan Chen
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Short Term Memory ◽  
Environmental Data ◽  
Weather Data ◽  
Short Term ◽  
Concentration Data ◽  
Data Set ◽  
Term Memory ◽  
Proposed Model

Accurate monitoring of air quality can no longer meet people’s needs. People hope to predict air quality in advance and make timely warnings and defenses to minimize the threat to life. This paper proposed a new air quality spatiotemporal prediction model to predict future air quality and is based on a large number of environmental data and a long short-term memory (LSTM) neural network. In order to capture the spatial and temporal characteristics of the pollutant concentration data, the data of the five sites with the highest correlation of time-series concentration of PM2.5 (particles with aerodynamic diameter ≤2.5 mm) at the experimental site were first extracted, and the weather data and other pollutant data at the same time were merged in the next step, extracting advanced spatiotemporal features through long- and short-term memory neural networks. The model presented in this paper was compared with other baseline models on the hourly PM2.5 concentration data set collected at 35 air quality monitoring sites in Beijing from January 1, 2016, to December 31, 2017. The experimental results show that the performance of the proposed model is better than other baseline models.

scholarly journals Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network

2020 ◽  
Vol 10 (8) ◽  
pp. 2962
Author(s):  
Lijuan Liu ◽  
Rung-Ching Chen ◽  
Shunzhi Zhu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Weather Conditions ◽  
Urban Transport ◽  
Weather Data ◽  
Weather Variables ◽  
Short Term ◽  
Passenger Flow ◽  
Input Variables ◽  
Main Station

Metro systems play a key role in meeting urban transport demands in large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored the issue of how to use historical weather data to make passenger flow forecasting more accurate. To this end, an hourly metro passenger flow forecasting model using a deep long short-term memory neural network (LSTM_NN) was developed. The optimized traditional input variables, including the different temporal data and historical passenger flow data, were combined with weather variables for data modeling. A comprehensive analysis of the weather impacts on short-term metro passenger flow forecasting is discussed in this paper. The experimental results confirm that weather variables have a significant effect on passenger flow forecasting. It is interesting to find out that the previous variables of one-hour temperature and wind speed are the two most important weather variables to obtain more accurate forecasting results on rainy days at Taipei Main Station, which is a primary interchange station in Taipei. Compared to the four widely used algorithms, the deep LSTM_NN is an extremely powerful method, which has the capability of making more accurate forecasts when suitable weather variables are included.

ERGO: a new Robust Design Optimization Technique combining Multi-Objective Bayesian Optimization with Analytical Uncertainty Quantification

2021 ◽  
pp. 1-22
Author(s):  
Jolan Wauters
Keyword(s):  
Design Optimization ◽  
Uncertainty Quantification ◽  
Robust Design ◽  
Bayesian Optimization ◽  
Robust Design Optimization ◽  
The Novel ◽  
Analytical Uncertainty ◽  
Multi Objective ◽  
Optimization Framework ◽  
Quantities Of Interest

Abstract In this work, robust design optimization (RDO) is treated, motivated by the increasing desire to account for variability the design phase. The problem is formulated in a multi-objective setting with the objective of simultaneously minimizing the mean of the objective and its variance due to variability of design variables and/or parameters. This allows the designer to choose its robustness level without the need to repeat the optimization as typically encountered when formulated as a single objective. To account for the computational cost that is often encountered in RDO problems, the problem is fitted in a Bayesian optimization framework. The use of surrogate modeling techniques to efficiently solve problems under uncertainty has effectively found its way in the optimization community leading to surrogate-assisted optimization-under-uncertainty schemes. The surrogates are often considered cheap-to-sample black-boxes and are sampled to obtain the desired quantities of interest. However, since the analytical formulation of the surrogates is known, an analytical treatment of the problem is available. To obtain the quantities of interest without sampling an analytical uncertainty propagation through the surrogate is presented. The multi-objective Bayesian optimization framework and the analytical uncertainty quantification are linked together through the formulation of the robust expected improvement (REI), obtaining the novel efficient robust global optimization (ERGO) scheme. The method is tested on a series of test cases to examine its behavior for varying difficulties and validated on an aerodynamic test function which proves the effectiveness of the novel scheme.

scholarly journals Short-Term Solar Irradiance Forecasting Based on a Hybrid Deep Learning Methodology

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 32 ◽  
Author(s):  
Ke Yan ◽  
Hengle Shen ◽  
Lei Wang ◽  
Huiming Zhou ◽  
Meiling Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Solar Irradiance ◽  
Power Plants ◽  
Short Term Memory ◽  
Learning Model ◽  
Weather Data ◽  
Short Term ◽  
Original Dataset ◽  
Deep Learning Model

Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU).

scholarly journals Image Classification using a Hybrid LSTM-CNN Deep Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 1342-1348 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Short Term Memory ◽  
Wide Spectrum ◽  
New Paradigm ◽  
Long Duration ◽  
Proposed Model ◽  
Classification Tasks

This work elaborates on the integration of the rudimentary Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM), resulting in a new paradigm in the well-explored field of image classification. LSTM is one kind of Recurrent Neural Network (RNN) which has the potential to memorize long-term dependencies. It was observed that LSTMs are able to complement the feature extraction ability of CNN when used in a layered order. LSTMs have the capacity to selectively remember patterns for a long duration of time and CNNs are able to extract the important features out of it. This LSTM-CNN layered structure, when used for image classification, has an edge over conventional CNN classifier. The model which has been proposed is based on the sets of Artificial Neural Network like Recurrent and Convolutional neural network; hence this model is robust and suitable to a wide spectrum of classification tasks. To validate these results, we have tested our model on two standard datasets. The results have been compared with other classifiers to establish the significance of our proposed model.

scholarly journals Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4733
Author(s):  
Andi A. H. Lateko ◽  
Hong-Tzer Yang ◽  
Chao-Ming Huang ◽  
Happy Aprillia ◽  
Che-Yuan Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Weather Data ◽  
Support Vector ◽  
Short Term ◽  
Learner Model ◽  
Average Maximum ◽  
Power Forecasting

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Sign in / Sign up

Export Citation Format

Share Document