Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid

Navares, Ricardo; Díaz, Julio; Linares, Cristina; Aznarte, José Luis

doi:10.1007/s00477-018-1519-z

Cited by 23 publications

(18 citation statements)

References 32 publications

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“…Rezaee et al (2020) introduced a mixture approach dependent on the Linguistic FMEA, Fuzzy Inference System and Fuzzy Data Envelopment Analysis model to ascertain a novel score for covering some RPN inadequacies and the prioritization of HSE dangers. Navares et al (2018) introduced an answer for the issue of anticipating every day medical clinic confirmations in Madrid because of circulatory and respiratory cases dependent on biometeorological markers. Cui and Singh (2017) created and applied the MRE hypothesis for month to month streamflow prediction withspectral power as a random variable.…”

Section: Related Workmentioning

confidence: 99%

A machine learning forecasting model for COVID-19 pandemic in India

Sujath

Chatterjee²,

Hassanien

2020

Stoch Environ Res Risk Assess

297

212

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Coronavirus disease (COVID-19) is an inflammation disease from a new virus. The disease causes respiratory ailment (like influenza) with manifestations, for example, cold, cough and fever, and in progressively serious cases, the problem in breathing. COVID-2019 has been perceived as a worldwide pandemic and a few examinations are being led utilizing different numerical models to anticipate the likely advancement of this pestilence. These numerical models dependent on different factors and investigations are dependent upon potential inclination. Here, we presented a model that could be useful to predict the spread of COVID-2019. We have performed linear regression, Multilayer perceptron and Vector autoregression method for desire on the COVID-19 Kaggle data to anticipate the epidemiological example of the ailment and pace of COVID-2019 cases in India. Anticipated the potential patterns of COVID-19 effects in India dependent on data gathered from Kaggle. With the common data about confirmed, death and recovered cases across India for over the time length helps in anticipating and estimating the not so distant future. For extra assessment or future perspective, case definition and data combination must be kept up persistently.

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Section: Related Workmentioning

confidence: 99%

A machine learning forecasting model for COVID-19 pandemic in India

Sujath

Chatterjee²,

Hassanien

2020

Stoch Environ Res Risk Assess

297

212

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“…Rezaee et al [18] use a hybrid method based on language Failure Modes and Effects Analysis (FMEA), Fuzzy inference system and fuzzy data analysis model for developing new assessment methods to resolve specific Risk Priority Number (RPN) errors and prioritize your health (H), safety (S), and environment (E) may be referred to as either HSE. Navares et al [19] recommended predicting the need for rehabilitation clinics in Madrid to treat cardiovascular and respiratory diseases based on bio meteorological markers. Cui et al [20] developed and tested the multiregional evolution (MRE) hypothesis using spectrum power as an indicator to predict monthly river flow.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers

Sultana¹,

Singha²,

Siddiqui³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…Sujatha and Chatterjee [1] proposed a model that could be useful to foresee the spread of COVID-2019 by using linear regression, Multilayer perceptron and Vector autoregression model on the COVID-19 kaggle data to envision the epidemiological example of the malady and pace of COVID-2019 cases in India. Navares et al [2] introduced an answer for the issue of anticipating every day medical clinic confirmations in Madrid because of circulatory and respiratory cases dependent on biometeorological markers. Cui and Singh created and applied the MRE hypothesis for month to month streamflow prediction with spectral power as a random variable.…”

Section: Recent Workmentioning

confidence: 99%

COVID-19 Face Mask Detection

Maurya¹

2021

Proceedings of ‏The 2nd International Conference on Advanced Research in Science, Engineering and Technology

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The present scenario of COVID-19 demands an efficient face mask detection application. The main goal of the project is to implement this system at entrances of colleges, airports, hospitals, and offices where chances of spread of COVID-19 through contagion are relatively higher. Reports indicate that wearing face masks while at work clearly reduces the risk of transmission. It is an object detection and classification problem with two different classes (Mask and Without Mask). A hybrid model using deep and classical machine learning for detecting face mask will be presented. A dataset is used to build this face mask detector using Python, OpenCV, and TensorFlow and Keras. While entering the place everyone should scan their face and then enter ensuring they have a mask with them. If anyone is found to be without a face mask, beep alert will be generated. As all the workplaces are opening. The number of cases of COVID-19 are still getting registered throughout the country. If everyone follows the safety measures, then it can come to an end. Hence to ensure that people wear masks while coming to work we hope this module will help in detecting it.

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Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid

Cited by 23 publications

References 32 publications

A machine learning forecasting model for COVID-19 pandemic in India

A machine learning forecasting model for COVID-19 pandemic in India

COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers

COVID-19 Face Mask Detection

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