scholarly journals Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning

2022 ◽  
Author(s):  
Mehmet Balcilar ◽  
David Gabauer ◽  
Rangan Gupta ◽  
Christian Pierdzioch
Keyword(s):  
Machine Learning ◽  
United Kingdom ◽  
Output Growth ◽  
The United Kingdom

scholarly journals Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970

2020 ◽  
Vol 35 (2) ◽  
pp. 176-197 ◽  
Author(s):  
Gary Koop ◽  
Stuart McIntyre ◽  
James Mitchell ◽  
Aubrey Poon
Keyword(s):  
United Kingdom ◽  
Output Growth ◽  
The United Kingdom

scholarly journals Using Machine Learning for Analyzing Sentiment Orientations Toward Eight Countries

SAGE Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 215824402095126
Author(s):  
Shesen Guo ◽  
Ganzhou Zhang
Keyword(s):  
United States ◽  
Machine Learning ◽  
United Kingdom ◽  
Olympic Games ◽  
The United States ◽  
Economic Power ◽  
Machine Learning Technique ◽  
The United Kingdom ◽  
Learning Technique ◽  
The Relationship

By using machine learning technique, this article presents sentiment and concept analyses on 48,043 articles published in The Economist from 1991 through 2016. The Economist is one of the world’s most influential political and economic magazines. The article analyzes and compares the magazine’s sentiment orientations toward the Group of Seven’s ingroup member countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and its outgroup member country China. The sentiment analyses are performed on and compared between different periods of Clinton’s, Bush’s, and Obama’s administrations in the United States; Major’s, Blair’s, Brown’s, and Cameron’s cabinets in the United Kingdom; and Kohl’s, Schröder’s, and Merkel’s in Germany. The relationship between China hosting the Olympic Games or its growing economic power and the magazine’s sentiment orientations toward the country is examined. The concept analysis on the articles with extreme positivity or negativity shows that there is no difference between the ingroup and outgroup members in the topics covered in The Economist.

scholarly journals Analysis and Prediction of COVID-19 using SIR, SEIR, and Machine Learning Models: Australia, Italy, and UK Cases

2020 ◽  
Author(s):  
Iman Rahimi ◽  
Amir H Gandomi ◽  
Fang Chen
Keyword(s):  
Machine Learning ◽  
United Kingdom ◽  
Prediction Accuracy ◽  
Logistic Function ◽  
Learning Models ◽  
The United Kingdom ◽  
Increasing Trend ◽  
Classic Form ◽  
Novel Coronavirus ◽  
Machine Learning Models

Abstract The novel Coronavirus disease, known as COVID-19, is an outbreak that started in Wuhan, one of the Central Chinese cities. In this report, a short analysis focusing on Australia, Italy, and the United Kingdom has been conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia as compared with Italy and the United Kingdom, and the outbreak in different Australian cities. Mathematical approaches based on the susceptible, infected, and recovered case (SIR) and susceptible, exposed, infected, and recovered (SEIR) models were proposed to predict the epidemiology in the countries. Since the performance of the classic form of SIR and SEIR depends on parameter settings, some optimization algorithms, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), L-BFGS-B, and Nelder-Mead are proposed to optimize the parameters of SIR and SEIR models and improve its predictive capabilities. The results of optimized SIR and SEIR models are compared with the Prophet algorithm and logistic function as two known ML algorithms. The results show that different algorithms display different behaviours in different countries. However, the improved version of the SIR and SEIR models have a better performance compared with other mentioned algorithms described in this study. Moreover, the Prophet algorithm works better for Italy and the United Kingdom cases than for Australian cases and Logistic function compared with Prophet algorithm has a better performance in these cases. It seems that Prophet algorithm is suitable for data with increasing trend in pandemic situations. Optimization of the SIR and SEIR models parameters has yielded a significant improvement in the prediction accuracy of the models. Although there are several algorithms for prediction of this Pandemic, there is no certain algorithm that would be the best one for all cases.

A Study of the Movements of Productivity in Individual Industries in the United Kingdom 1968-79

1981 ◽  
Vol 97 ◽  
pp. 57-66 ◽  
Author(s):  
G.C. Wenban-Smith
Keyword(s):  
United Kingdom ◽  
Growth Rates ◽  
Manufacturing Industry ◽  
Output Growth ◽  
The United Kingdom

This article examines the movement of output per employee in one hundred and sixty industries between 1968 and 1979. It concludes that the slowdown in productivity and output growth rates observed for aggregate manufacturing industry since 1973 was fairly typical of the experience of most individual industries; and that no shift in the relation between productivity and output growth could be identified.

A comparison of the United Kingdom Clinical Aptitude Test (UK-CAT) with a traditional admission selection process

2009 ◽  
pp. 1-6 ◽  
Author(s):  
Nishan Fernando ◽  
Gordon Prescott ◽  
Jennifer Cleland ◽  
Kathryn Greaves ◽  
Hamish McKenzie
Keyword(s):  
United Kingdom ◽  
Selection Process ◽  
Aptitude Test ◽  
The United Kingdom

Research on Schizophrenia: A View From the United Kingdom

1990 ◽  
Vol 35 (8) ◽  
pp. 800-801
Author(s):  
Michael F. Pogue-Geile
Keyword(s):  
United Kingdom ◽  
The United Kingdom

In the United Kingdom, Employed Women Have Problems, Too

1992 ◽  
Vol 37 (10) ◽  
pp. 1076-1077
Author(s):  
Barbara A. Gutek
Keyword(s):  
United Kingdom ◽  
The United Kingdom ◽  
Employed Women
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