COVID-19 Outbreak Prediction with Machine Learning

Algorithms, Journal Year: 2020, Volume and Issue: 13(10), P. 249 - 249

Published: Oct. 1, 2020

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among standard global pandemic prediction, simple epidemiological statistical have received more attention authorities, these popular in media. Due a high level of uncertainty lack essential data, shown low accuracy long-term prediction. Although literature includes several attempts address this issue, generalization robustness abilities existing need be improved. This paper presents comparative analysis machine learning soft computing predict as an alternative susceptible–infected–recovered (SIR) susceptible-exposed-infectious-removed (SEIR) models. wide range investigated, two showed promising results (i.e., multi-layered perceptron, MLP; adaptive network-based fuzzy inference system, ANFIS). Based on reported here, due highly complex nature variation its behavior across nations, study suggests effective tool model outbreak. provides initial benchmarking demonstrate potential future research. further that genuine novelty can realized integrating SEIR

Language: Английский

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COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

Mathematics, Journal Year: 2020, Volume and Issue: 8(6), P. 890 - 890

Published: June 2, 2020

Several epidemiological models are being used around the world to project number of infected individuals and mortality rates COVID-19 outbreak. Advancing accurate prediction is utmost importance take proper actions. Due lack essential data uncertainty, have been challenged regarding delivery higher accuracy for long-term prediction. As an alternative susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach predict COVID-19, we exemplify its potential using from Hungary. The methods adaptive network-based fuzzy inference system (ANFIS) multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) proposed time series rate. that by late May, outbreak total morality will drop substantially. validation performed 9 days with promising results, which confirms model accuracy. It expected maintains as long no significant interruption occurs. This paper provides initial benchmarking demonstrate future research.

Language: Английский

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Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Lecture notes in networks and systems, Journal Year: 2020, Volume and Issue: unknown, P. 215 - 227

Published: Jan. 1, 2020

Language: Английский

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Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review

Lecture notes in networks and systems, Journal Year: 2020, Volume and Issue: unknown, P. 52 - 62

Published: Jan. 1, 2020

Language: Английский

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Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research

Lecture notes in networks and systems, Journal Year: 2020, Volume and Issue: unknown, P. 19 - 32

Published: Jan. 1, 2020

Language: Английский

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COVID-19 Outbreak Prediction with Machine Learning

Published: Oct. 8, 2020

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among standard global pandemic prediction, simple epidemiological statistical have received more attention authorities, they popular in media. Due a high level of uncertainty lack essential data, shown low accuracy long-term prediction. Although literature includes several attempts address this issue, generalization robustness abilities existing needs be improved. This paper presents comparative analysis machine learning soft computing predict as an alternative SIR SEIR models. wide range investigated, two showed promising results (i.e., multi-layered perceptron, MLP, adaptive network-based fuzzy inference system, ANFIS). Based on reported here, due highly complex nature variation its behavior from nation-to-nation, study suggests effective tool model outbreak. provides initial benchmarking demonstrate potential future research. Paper further that real novelty can realized through integrating

Language: Английский

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List of Deep Learning Models

Lecture notes in networks and systems, Journal Year: 2020, Volume and Issue: unknown, P. 202 - 214

Published: Jan. 1, 2020

Language: Английский

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COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Research Square (Research Square), Journal Year: 2020, Volume and Issue: unknown

Published: May 6, 2020

Several epidemiological models are being used around the world to project number of infected individuals and mortality rates COVID-19 outbreak. Advancing accurate prediction is utmost importance take proper actions. Due a high level uncertainty or even lack essential data, standard have been challenged regarding delivery higher accuracy for long-term prediction. As an alternative susceptible-infected-resistant (SIR)-based models, this study proposes hybrid machine learning approach predict we exemplify its potential using data from Hungary. The methods adaptive network-based fuzzy inference system (ANFIS) multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) time series rate. that by late May, outbreak total morality will drop substantially. validation performed nine days with promising results, which confirms model accuracy. It expected maintains as long no significant interruption occurs. Based on results reported here, due complex nature variation in behavior nation-to-nation, suggests effective tool This paper provides initial benchmarking demonstrate future research.

Language: Английский

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Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Entropy, Journal Year: 2020, Volume and Issue: 22(11), P. 1239 - 1239

Published: Oct. 31, 2020

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction SMs’ direction may promise various benefits. Because fairly nonlinear nature historical data, accurate estimation SM a rather challenging issue. The aim this study to present novel machine learning (ML) model forecast movement Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) default Gaussian function as output function. financial time series data utilized research from 1996 2020, consisting nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Absolute Percentage (MAPE) correlation coefficient values compare accuracy performance developed models. Based on results, involvement function, improved models compared with significantly. MLP–PSO population size 125, followed MLP–GA 50, provided higher for testing, reporting RMSE 0.732583 0.733063, MAPE 28.16%, 29.09% 0.694 0.695, respectively. According hybrid ML method could successfully improve accuracy.

Language: Английский

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COVID-19 Outbreak Prediction with Machine Learning

Published: Oct. 6, 2020

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among standard global pandemic prediction, simple epidemiological statistical have received more attention authorities, they popular in media. Due a high level of uncertainty lack essential data, shown low accuracy long-term prediction. Although literature includes several attempts address this issue, generalization robustness abilities existing needs be improved. This paper presents comparative analysis machine learning soft computing predict as an alternative SIR SEIR models. wide range investigated, two showed promising results (i.e., multi-layered perceptron, MLP, adaptive network-based fuzzy inference system, ANFIS). Based on reported here, due highly complex nature variation its behavior from nation-to-nation, study suggests effective tool model outbreak. provides initial benchmarking demonstrate potential future research. Paper further that real novelty can realized through integrating

Language: Английский

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