computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. nowadays, despite the numerous time series forecasting models propos...

Background:Today, with the coronavirus pandemic, the governments and international institutions are rapidly taking various approaches to infection control. In the meantime, the care of healthcare workers who are responsible for the health and treatment of patients is very important. The COVID-19 epidemic in China has achieved a great victory, and it is important to summarize the successful anti...

abstract forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. this paper studies load consumption modeling in hamedan city province distribution network by applying esn neural network. weather forecasting data such as minimum day temperature, average day temp...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

In light of the global events resulting from spread Corona pandemic and viral mutations, there is a need to examine epidemic data in terms numbers infected deaths, different geographical locations, dynamics disease dissemination virus. Kingdom Saudi Arabia (KSA), since virus on March 2, 2020, number confirmed cases has increased 599044 until January 13, 2022, which 262 are critical cases, while...

Background Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available and have the potential to provide valuable information to estimate and...

Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these wide...

Dengue/dengue haemorrhagic fever is the leading cause of death from the declared infectious diseases in children in southern Vietnam. The dengue epidemic in 1998 was an extensive one, with the morbidity rate of 455 cases/100 000 pop. and mortality rate of 1.27/100 000 pop. DEN-3 was the predominant serotype. However, in 1999, there was a lower morbidity (80.7 cases/100 000) and a lower mortalit...

The 2014 Ebola epidemic in West Africa is the largest ever recorded, and understanding the interrelated dynamics of surveillance and intervention is a key concern, both for this and future epidemics. Moreover, as transmissibility and mortality are believed to increase as symptoms progress, intervention strategies may depend on individual’s stage of infection. To examine these issues, we develop...

Since the outbreak of Coronavirus Disease 2019 (COVID-19), spread epidemic has been a major international public health issue. Hence, various forecasting models have used to predict infectious disease. In general, problems often involve prediction accuracy decreasing as horizon increases. Thus, extend without performance or prediction, this study developed Dual Long Short-Term Memory (LSTM) wit...