scholarly journals Forecasting COVID 19 Confirmed Cases Using Machine Learning: the Case of America

2020 ◽  
Author(s):  
Mario Jojoa ◽  
Begoña Garcia-Zapirain
Keyword(s):  
United States ◽  
Machine Learning ◽  
Open Data ◽  
Absolute Error ◽  
The United States ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Tools ◽  
The European Union ◽  
Testing Stage

This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.

scholarly journals Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2328 ◽  
Author(s):  
Md Shafiullah ◽  
M. Abido ◽  
Taher Abdel-Fattah
Keyword(s):  
Machine Learning ◽  
Fault Location ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Approach ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Performance Indices ◽  
Machine Learning Approach ◽  
Distribution Grids

Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

scholarly journals A Machine Learning Analysis of COVID-19 Mental Health Data

2022 ◽  
Author(s):  
Mostafa Rezapour ◽  
Lucas Hansen
Keyword(s):  
Mental Health ◽  
United States ◽  
Machine Learning ◽  
Survey Data ◽  
Social Research ◽  
Multinomial Logistic Regression ◽  
The United States ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Individual

Abstract In late December 2019, the novel coronavirus (Sars-Cov-2) and the resulting disease COVID-19 were first identified in Wuhan China. The disease slipped through containment measures, with the first known case in the United States being identified on January 20th, 2020. In this paper, we utilize survey data from the Inter-university Consortium for Political and Social Research and apply several statistical and machine learning models and techniques such as Decision Trees, Multinomial Logistic Regression, Naive Bayes, k-Nearest Neighbors, Support Vector Machines, Neural Networks, Random Forests, Gradient Tree Boosting, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, and Chi-Squared Test to analyze the impacts the COVID-19 pandemic has had on the mental health of frontline workers in the United States. Through the interpretation of the many models applied to the mental health survey data, we have concluded that the most important factor in predicting the mental health decline of a frontline worker is the healthcare role the individual is in (Nurse, Emergency Room Staff, Surgeon, etc.), followed by the amount of sleep the individual has had in the last week, the amount of COVID-19 related news an individual has consumed on average in a day, the age of the worker, and the usage of alcohol and cannabis.

scholarly journals Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: a multisource Internet data approach

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dinda Thalia Andariesta ◽  
Meditya Wasesa
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Tourism Demand ◽  
Learning Models ◽  
Content Type ◽  
International Tourist ◽  
Machine Learning Models

PurposeThis research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.Design/methodology/approachTo develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).FindingsPrediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.Originality/valueFirst, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

scholarly journals Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

2021 ◽  
Author(s):  
Muhammad Adnan ◽  
Rana Muhammad Adnan ◽  
Shiyin Liu ◽  
Muhammad Saifullah ◽  
Yasir Latif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Relative Humidity ◽  
Global Climate ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Future Research ◽  
Learning Tools ◽  
Mars Model ◽  
Testing Stage ◽  
Adaptive Regression

Accurate and reliable prediction of relative humidity is of great importance in all fields concerning global climate change. The current study has employed Multivariate Adaptive Regression Spline (MARS) and M5 Tree (M5T) models to predict the relative humidity in the Hunza River basin, Pakistan. Both the models provided the best prediction for the input scenario S6 (RHt-1, RHt-2, RHt-3, Tt-1, Tt-2, Tt-3). The statistical analysis displayed that the MARS model provided a better prediction of relative humidity as compared to M5T at all meteorological stations, especially, at Ziarat followed by Khunjerab and Naltar. The values of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were (5.98%, 5.43%, and 0.808) for Khunjerab; (6.58%, 5.08%, and 0.806) for Naltar; and (5.86%, 4.97%, 0.815) for Ziarat during the testing of MARS model whereas, the values were (6.14%, 5.56%, and 0.772) for Khunjerab; (6.19%, 5.58% and 0.762) for Naltar and (6.08%, 5.46%, 0.783) for Ziarat during the testing of M5T model. Both the models performed slightly better in training as compared to the testing stage. The current study encourages future research to be conducted at high altitude basins for the prediction of other meteorological variables using machine learning tools.

scholarly journals Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

2018 ◽  
Vol 10 (10) ◽  
pp. 3498 ◽  
Author(s):  
Juan Rincon-Patino ◽  
Emmanuel Lasso ◽  
Juan Corrales
Keyword(s):  
United States ◽  
Machine Learning ◽  
Prediction Model ◽  
Multivariate Regression ◽  
The United States ◽  
Persea Americana ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Support Vector ◽  
Multivariate Regression Prediction

Persea americana, commonly known as avocado, is becoming increasingly important in global agriculture. There are dozens of avocado varieties, but more than 85% of the avocados harvested and sold in the world are of the Hass one. Furthermore, information on the market of agricultural products is valuable for decision-making; this has made researchers try to determine the behavior of the avocado market, based on data that might affect it one way or another. In this paper, a machine learning approach for estimating the number of units sold monthly and the total sales of Hass avocados in several cities in the United States, using weather data and historical sales records, is presented. For that purpose, four algorithms were evaluated: Linear Regression, Multilayer Perceptron, Support Vector Machine for Regression and Multivariate Regression Prediction Model. The last two showed the best accuracy, with a correlation coefficient of 0.995 and 0.996, and a Relative Absolute Error of 7.971 and 7.812, respectively. Using the Multivariate Regression Prediction Model, an application that allows avocado producers and sellers to plan sales through the estimation of the profits in dollars and the number of avocados that could be sold in the United States was created.

Russian Federation and the United States of America: geostrategic directions to manage the Ukrainian crisis

2019 ◽  
pp. 123
Author(s):  
Attarid Awadh Abdulhameed
Keyword(s):  
United States ◽  
European Union ◽  
National Security ◽  
Russian Federation ◽  
United States Of America ◽  
The United States ◽  
The European Union ◽  
The Usa ◽  
Ukrainian Crisis ◽  
Zero Sum

Ukrainia Remains of huge importance to Russian Strategy because of its Strategic importance. For being a privileged Postion in new Eurasia, without its existence there would be no logical resons for eastward Expansion by European Powers.  As well as in Connection with the progress of Ukrainian is no less important for the USA (VSD, NDI, CIA, or pentagon) and the European Union with all organs, and this is announced by John Kerry. There has always ben Russian Fear and Fear of any move by NATO or USA in the area that it poses a threat to  Russians national Security and its independent role and in funence  on its forces especially the Navy Forces. There for, the Crisis manyement was not Zero sum game, there are gains and offset losses, but Russia does not accept this and want a Zero Sun game because the USA. And European exteance is a Foot hold in Regin Which Russian sees as a threat to its national security and want to monopolize control in the strategic Qirim.

COMPARATIVE ANALYSIS OF THE CURRENT FRENCH FOREIGN POLICY ACTIVITY

2019 ◽  
Vol 22 (2) ◽  
pp. 74-79
Author(s):  
Nargiza Sodikova ◽  
◽  
◽  
Keyword(s):  
United States ◽  
European Union ◽  
Foreign Policy ◽  
International Security ◽  
The United States ◽  
Foreign Affairs ◽  
Modern Time ◽  
The European Union ◽  
National Interests ◽  
French Foreign Policy

Important aspects of French foreign policy and national interests in the modern time,France's position in international security and the specifics of foreign affairs with the United States and the European Union are revealed in this article

Ocena antykryzysowej polityki gospodarczej Stanów Zjednoczonych i wybranych państw członkowskich Unii Europejskiej w latach 2005–2014

2016 ◽  
pp. 26-46
Author(s):  
Marcin Jan Flotyński
Keyword(s):  
United States ◽  
European Union ◽  
Financial Markets ◽  
Global Economy ◽  
Global Financial Crisis ◽  
The United States ◽  
The European Union ◽  
High Volatility ◽  
The Global Financial Crisis ◽  
Domestic Products

The global financial crisis in 2007–2009 began a period of high volatility on the financial markets. Specifically, it caused an increased amplitude of fluctuations of the level of gross domestic products, the level of investment and consumption and exchange rates in particular countries. To address the adverse market circumstances, governments and central banks took actions in order to bolster the weakening global economy. The aim of this article is to present the anti-crisis actions in the United States and selected member states of the European Union, including Poland, and an assessment of their efficiency. The analysis conducted indicates that generally the actions taken in the United States in response to the crisis were faster and more adequate to the existing circumstances than in the European Union.

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