scholarly journals Use of econometrics and machine learning models to predict the number of new cases per day of COVID-19

2020 ◽  
Author(s):  
Roberto Silva ◽  
Bruna Barreira ◽  
Fernando Xavier ◽  
Antonio Saraiva ◽  
Carlos Cugnasca
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Epidemiological Models ◽  
Learning Models ◽  
Expected Number ◽  
Short Term ◽  
Ensemble Models ◽  
Machine Learning Models

The COVID-19 pandemics will impact the demand for healthcare severely. It is essential to continually monitor and predict the expected number of new cases for each country. We explored the use of econometrics, machine learning, and ensemble models to predict the number of new cases per day for Brazil, China, Italy, and South Korea. These models can be used to make predictions in the short term, complementing the epidemiological models. Our main findings were: (i) there is no single best model for all countries; (ii) ensembles can, in some instances, improve the results of individual models; and (iii) the ML models had worse results due to the lack of data.

scholarly journals Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Models ◽  
Short Term ◽  
Infected People ◽  
Infected Case ◽  
First Case ◽  
Novel Coronavirus ◽  
Short Term Prediction ◽  
Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

scholarly journals Prediction of Head Movement in 360-Degree Videos Using Attention Model

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3678
Author(s):  
Dongwon Lee ◽  
Minji Choi ◽  
Joohyun Lee
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
The Other ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Attention Model ◽  
Long Short Term Memory ◽  
Machine Learning Models

In this paper, we propose a prediction algorithm, the combination of Long Short-Term Memory (LSTM) and attention model, based on machine learning models to predict the vision coordinates when watching 360-degree videos in a Virtual Reality (VR) or Augmented Reality (AR) system. Predicting the vision coordinates while video streaming is important when the network condition is degraded. However, the traditional prediction models such as Moving Average (MA) and Autoregression Moving Average (ARMA) are linear so they cannot consider the nonlinear relationship. Therefore, machine learning models based on deep learning are recently used for nonlinear predictions. We use the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network methods, originated in Recurrent Neural Networks (RNN), and predict the head position in the 360-degree videos. Therefore, we adopt the attention model to LSTM to make more accurate results. We also compare the performance of the proposed model with the other machine learning models such as Multi-Layer Perceptron (MLP) and RNN using the root mean squared error (RMSE) of predicted and real coordinates. We demonstrate that our model can predict the vision coordinates more accurately than the other models in various videos.

scholarly journals Ambient Monitoring of Gait and Machine Learning Models for Dynamic and Short-Term Falls Risk Assessment in People With Dementia

2021 ◽  
Author(s):  
Navid Korhani ◽  
Babak Taati ◽  
Andrea Iaboni ◽  
Andrea Sabo ◽  
Sina Mehdizadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Fall Risk ◽  
Learning Models ◽  
Short Term ◽  
Ambient Monitoring ◽  
Clinical Assessments ◽  
People With Dementia ◽  
Time Of Admission ◽  
Machine Learning Models

Data consists of baseline clinical assessments of gait, mobility, and fall risk at the time of admission of 54 adults with dementia. Furthermore, it includes the participants' daily medication intake in three medication categories, and frequent assessments of gait performed via a computer vision-based ambient monitoring system.

scholarly journals Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7834
Author(s):  
Christopher Hecht ◽  
Jan Figgener ◽  
Dirk Uwe Sauer
Keyword(s):  
Machine Learning ◽  
Binary Data ◽  
Training Data ◽  
Gradient Boosting ◽  
Traffic Density ◽  
Learning Models ◽  
Charging Infrastructure ◽  
Ensemble Models ◽  
Charging Station ◽  
Machine Learning Models

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

scholarly journals "Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

2021 ◽  
Author(s):  
Yongmin Cho ◽  
Rachael A Jonas-Closs ◽  
Lev Y Yampolsky ◽  
Marc W Kirschner ◽  
Leonid Peshkin
Keyword(s):  
Machine Learning ◽  
Chronological Age ◽  
Complex Behavior ◽  
Learning Models ◽  
Short Term ◽  
Health Span ◽  
Response Profile ◽  
Automated Phenotyping ◽  
Machine Learning Models

We present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi transparent freshwater organism, sensitive to drugs with complex behavior and physiology - the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioural features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using long term dose response profile of metformin and short term assay of such well-studied substances as caffeine and alcohol.

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