Dual attention-based sequential auto-encoder for Covid-19 outbreak forecasting: A case study in Vietnam

Pham, Phu; Pedrycz, Witold; Vo, Bay

doi:10.1016/j.eswa.2022.117514

Cited by 13 publications

(5 citation statements)

References 38 publications

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“…[ [16][17][18][19][20][21] used LSTM, GRU, and CNN-based models for predicting cases in Malaysia and various other countries. The authors employed daily confirmed cases as input features and evaluated the models' performance using MAE and RMSE.…”

Section: Model Selection and Evaluationmentioning

confidence: 99%

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting

et al. 2023

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The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models’ performance discloses the most appropriate architecture for Malaysia’s specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus’s spread and minimize its effects on Malaysia’s population.

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Section: Model Selection and Evaluationmentioning

confidence: 99%

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting

et al. 2023

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“…To extend the field and address these challenges, future research can explore the efficiency of several LSTM variants and compare them with statistical models such as the ARIMA (AutoRegressive Integrated Moving Average) model [27,28]. Other techniques, such as linear regression [29,30] and autoencoders [31,32], should also be analyzed and compared to determine their effectiveness in aviation applications. Additionally, the impact of external factors, such as weather patterns, economic indicators, and geopolitical events, on aviation demand and performance should be considered in future research.…”

Section: Discussionmentioning

confidence: 99%

FlightForecast: A Comparative Analysis of Stack LSTM and Vanilla LSTM Models for Flight Prediction

Qamar,

Asif,

Falak Naz

et al. 2024

VFAST trans. softw. eng.

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The Coronavirus was first reported in China in the city of Wuhan in December 2019, after a couple of months, it was widespread around the world. The whole world was in a state of lockdown. This hazardous disease affects the normal daily life of every individual and the tourism industry, especially the airline business was at a greater loss. Considering the airline business, this study contains data on commercial flights from 2019 to 2020. The conducted research analyzed the rise and fall of different flights in the lockdown period. The research is based on the variants of Long Short-Term Memory (LSTM) such as standard Recurrent Neural Network (RNN) and stack LSTM. The comparative research shows that the prediction of the stack LSTM model is better than the standard RNN keeping view of taking a considerable amount of time to train.

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“…The authors applied a Gaussian walk and Lévy flight to improve the exploration and exploitation to avoid trapping in local optima. An improvement of a long short-term memory network was proposed in [14], where the authors show the behavior of the proposed method with a chaotic time series of COVID-19. They demonstrated the effectiveness of their proposed method for COVID-19 cases in Vietnam.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

Melín

Sánchez

Castro

et al. 2022

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In this work, information on COVID-19 confirmed cases is utilized as a dataset to perform time series predictions. We propose the design of ensemble neural networks (ENNs) and type-3 fuzzy inference systems (FISs) for predicting COVID-19 data. The answers for each ENN module are combined using weights provided by the type-3 FIS, in which the ENN is also designed using the firefly algorithm (FA) optimization technique. The proposed method, called ENNT3FL-FA, is applied to the COVID-19 data for confirmed cases from 12 countries. The COVID-19 data have shown to be a complex time series due to unstable behavior in certain periods of time. For example, it is unknown when a new wave will exist and how it will affect each country due to the increase in cases due to many factors. The proposed method seeks mainly to find the number of modules of the ENN and the best possible parameters, such as lower scale and lower lag of the type-3 FIS. Each module of the ENN produces an individual prediction. Each prediction error is an input for the type-3 FIS; moreover, outputs provide a weight for each prediction, and then the final prediction can be calculated. The type-3 fuzzy weighted average (FWA) integration method is compared with the type-2 FWA to verify its ability to predict future confirmed cases by using two data periods. The achieved results show how the proposed method allows better results for the real prediction of 20 future days for most of the countries used in this study, especially when the number of data points increases. In countries such as Germany, India, Italy, Mexico, Poland, Spain, the United Kingdom, and the United States of America, on average, the proposed ENNT3FL-FA achieves a better performance for the prediction of future days for both data points. The proposed method proves to be more stable with complex time series to predict future information such as the one utilized in this study. Intelligence techniques and their combination in the proposed method are recommended for time series with many data points.

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Dual attention-based sequential auto-encoder for Covid-19 outbreak forecasting: A case study in Vietnam

Cited by 13 publications

References 38 publications

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting

Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting

FlightForecast: A Comparative Analysis of Stack LSTM and Vanilla LSTM Models for Flight Prediction

Design of Type-3 Fuzzy Systems and Ensemble Neural Networks for COVID-19 Time Series Prediction Using a Firefly Algorithm

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