Impact analysis of COVID-19 news headlines on global economy

Malik, Ananya; Javeri, Yash; Shah, Mansi; Mangrulkar, Ramchandra

doi:10.1016/b978-0-12-824557-6.00001-7

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…Este modelo se ajusta a los datos de entrenamiento y aprende a identificar patrones que relacionan las características de los pacientes con la gravedad del Dengue. RandomForestClassifier, Según [21] se utiliza para intentar mejorar la precisión en comparación con la regresión lineal, ya que el bosque aleatorio puede aproximar mejor la relación entre los objetivos y las características.…”

Section: Modeladounclassified

Aplicación de machine learning y metodología CRISP-DM para la clasificación precisa de severidad en casos de dengue

Mejia Rodriguez,

Rincon Pinzon,

Palmera Quintero

et al. 2024

RCTA

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El proyecto se centra en clasificar con precisión la severidad de los casos de Dengue en Casanare, Colombia, utilizando Machine Learning (ML) y la metodología CRISP-DM. La variable objetivo es “clasificación final”, que categoriza los casos en dengue sin signos de alarma y con signos de alarma. Se probaron varios modelos y técnicas, destacando 'RandomForest' como el más efectivo debido a su alto rendimiento, alcanzando una precisión del 100%. La mejora en la clasificación permitirá una identificación temprana y precisa de la gravedad de los casos, lo que, a su vez, puede mejorar la atención médica y las estrategias de intervención. Se utilizo la base de datos “Casos de Dengue en Casanare por servicio hospitalario, relación tipo de persona, síntomas y estado hospitalario” para respaldar el análisis.

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Section: Modeladounclassified

Aplicación de machine learning y metodología CRISP-DM para la clasificación precisa de severidad en casos de dengue

Mejia Rodriguez,

Rincon Pinzon,

Palmera Quintero

et al. 2024

RCTA

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“…A selection of features in a more varied and diverse manner than the RF algorithm [49] RF -The algorithm is relatively more intricate than other ML algorithms [50,51] ADABOOST…”

Section: Models Mathematical Modelmentioning

confidence: 99%

A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset

Hu,

Luo,

Zhang

et al. 2023

Materials

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Fatigue life prediction of Inconel 718 fabricated by laser powder bed fusion was investigated using a miniature specimen tests method and machine learning algorithms. A small dataset-based machine learning framework integrating thirteen kinds of algorithms was constructed to predict the pore-influenced fatigue life. The method of selecting random seeds was employed to evaluate the performance of the algorithms, and then the ranking of various machine learning algorithms for predicting pore-influenced fatigue life on small datasets was obtained by verifying the prediction model twenty or thirty times. The results showed that among the thirteen popular machine learning algorithms investigated, the adaptive boosting algorithm from the boosting category exhibited the best fitting accuracy for fatigue life prediction of the additively manufactured Inconel 718 using the small dataset, followed by the decision tree algorithm in the nonlinear category. The investigation also found that DT, RF, GBDT, and XGBOOST algorithms could effectively predict the fatigue life of the additively manufactured Inconel 718 within the range of 1 × 105 cycles on a small dataset compared to others. These results not only demonstrate the capability of using small dataset-based machine learning techniques to predict fatigue life but also may guide the selection of algorithms that minimize performance evaluation costs when predicting fatigue life.

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“…As the topic of the COVID-19 pandemic touched extensive areas of economic sectors, so it is natural that there is plenty of research regarding this theme (Christophe et al, 2022;Hokama et al, 2022;Nurmohamed et al, 2022;Pinchera et al, 2022;Schmidt et al, 2022;Su et al, 2022;Wang et al, 2022). Lots of these researches are investigating the impact of COVID-19 on the economy (Atsız & Cifci, 2021;Hyman et al, 2021;Keogh-Brown et al, 2020;Malik et al, 2022;Pham et al, 2021b); others are making the prognoses (Atsız & Cifci, 2021;Hyman et al, 2021;Keogh-Brown et al, 2020;Malik et al, 2022;Pham et al, 2021b) or giving recommendations (Atsız & Cifci, 2021;Hyman et al, 2021;Keogh-Brown et al, 2020;Malik et al, 2022;Pham et al, 2021b), and only some of these scientific publications are dedicating the research about the impact of COVID -19 on a food and beverage service enterprises (Atsız & Cifci, 2021;Hyman et al, 2021;Keogh-Brown et al, 2020;Malik et al, 2022;Pham et al, 2021b) In order to conduct an analysis of the mass of published work, a selection of research and scholarly publications on the state of the situation in Europe, Canada, the United States, and Malaysia during the epidemic have been chosen. As the COVID -19 pandemic is a worldwide issue, the preventative plans and procedures of the various nations are more or less similar.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of the Impact of COVID-19 Pandemic on the Service Sector in Lithuania

Seržantė

Pakalka

2022

Economics and Culture

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Research purpose. This study aims to assess the impact of selected economic indicators (independent variables) on the turnover of food and beverage service companies in Lithuania. Design / Methodology / Approach. In order to achieve the intended goal of the study, an analysis of scientific articles was performed, with the primary aim of finding out the consequences of the COVID - 19 pandemic on the Lithuanian economy and selecting five independent variables that could affect the turnover of food and beverage companies. After analysing the literature, five independent variables were highlighted: the number of tourists accommodated, the statistics of Vilnius Airport flights, the number of employees hired, the unemployment rate and inflation. Findings. The correlation and regression analysis results show that the turnover of catering and beverage enterprises and the number of accommodated tourists are strongly correlated (r---gt--- 0.90). Meanwhile, the linear correlation between the number of Vilnius Airport flights and the number of hired employees with the research variable is average (0.60 ---lt---r ---lt---0.70). However, the linear correlation of macroeconomic factors such as unemployment and inflation with the turnover of food and beverage enterprises is statistically insignificant. Originality / Value / Practical implications. The originality of this article is that it studies the valuable topic of the COVID-19 crisis in service sectors, which substantially impacted Lithuania’s economy and adversely affected the economic performance of the region in general. The results of this research will contribute to future monitoring and crisis planning in particular sectors.

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Impact analysis of COVID-19 news headlines on global economy

Cited by 5 publications

References 9 publications

Aplicación de machine learning y metodología CRISP-DM para la clasificación precisa de severidad en casos de dengue

Aplicación de machine learning y metodología CRISP-DM para la clasificación precisa de severidad en casos de dengue

A Comparative Investigation of Machine Learning Algorithms for Pore-Influenced Fatigue Life Prediction of Additively Manufactured Inconel 718 Based on a Small Dataset

Assessment of the Impact of COVID-19 Pandemic on the Service Sector in Lithuania

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