scholarly journals Forecasting COVID-19 Vaccination Trends in Indonesia using Machine Learning

2021 ◽  
Vol 3 ◽  
pp. 97-104
Author(s):  
Ahmad Fauzan Aqil ◽  
Hsi-Chieh Lee ◽  
Sofi Ismarilla Wardani
Keyword(s):  
Machine Learning ◽  
Arima Model ◽  
Economic Factors ◽  
Factors Affecting ◽  
Forecasting Method ◽  
Periodic Testing ◽  
The Government ◽  
Effective Use ◽  
Further Development ◽  
Machine Learning Models

The ongoing COVID-19 pandemic requires much research to deal with this problem. Medical treatment has resulted in vaccine findings that work as an immune system to block the COVID-19 reaction process. However, many of these developments are still undergoing improvement and periodic testing to found better results for humans. Therefore, forecasting trends of the COVID-19 vaccine in Indonesia is carried out to regularly predict vaccines' effectiveness by adjusting conditions. This forecasting uses the time-series forecasting method by prioritizing a machine learning process in predicting probably future forecasts. Based on the highest vaccine used, we propose ARIMA and Facebook Prophet as machine learning models to predict vaccine trends in each country. The Prophet model results achieved an RMSE score of 0.176, which these results contained vaccines distributed in Indonesia. Besides that, the ARIMA model achieved an RMSE score of 0.453 using the same dataset. The results obtained from this method can be considered a policy for the government to deal with the effective use of vaccines according to future needs. As a further development, this research can be reviewed by paying attention to external aspects such as social and economic factors affecting the COVID-19 vaccination. The results obtained are more comprehensive and representative than this research based on conditions that provide policies for handling COVID-19.

scholarly journals Modeling the British Pound Sterling to Nigerian Naira Exchange Rate During the Covid-19 Pandemic

2021 ◽  
Vol 2 (2) ◽  
pp. 25-35
Author(s):  
Chukwudi Paul Obite ◽  
Ugochinyere Ihuoma Nwosu ◽  
Prince Henry Osuagwu ◽  
Obioma Gertrude Onukwube
Keyword(s):  
Machine Learning ◽  
Exchange Rate ◽  
Random Forest ◽  
Model Comparison ◽  
Arima Model ◽  
Secondary Data ◽  
Ann Model ◽  
Learning Models ◽  
The Government ◽  
Machine Learning Models

The British Pound Sterling (GBP) to Nigerian Naira (NGN) exchange rate has been grossly affected by the Coronavirus 2019 (Covid-19) pandemic. It has become pertinent to identify robust models that will help to cope with the variability associated with the pandemic. Many original studies found the ARIMA method to be highly useful in modeling and forecasting exchange rates. However, not much work has been done on modeling the GBP and NGN exchange rate during the covid-19 pandemic using machine learning models. This study focuses on modeling the exchange rate between the GPB and NGN during the period of the Covid-19 pandemic by adopting the process of model comparison using the Artificial Neural Network (ANN), Autoregressive Integrated Moving Average (ARIMA), and Random Forest models to obtain an optimal model and forecasts from the model. Secondary data of the GBP to NGN exchange rate within the period of the Covid-19 pandemic from exchangerate.org.uk were used. The two machine learning models (ANN and random forest) performed better than the ARIMA model. The RF, though performed well in the training set, was outperformed in the test set by the ANN model. The ANN model was chosen to model and forecast the GBP and NGN exchange rate during the Covid-19 pandemic. The predicted fall in the GBP to NGN exchange rate to 570 by December 2021 and 575 by September 2022 using the ANN model will have a huge effect on the economy of the country as the country depends largely on imported goods. The Government and policymakers must put in place structural measures that will avoid the looming crisis.

An Experimental Study of Spammer Detection on Chinese Microblogs

2020 ◽  
Vol 30 (11n12) ◽  
pp. 1759-1777
Author(s):  
Jialing Liang ◽  
Peiquan Jin ◽  
Lin Mu ◽  
Jie Zhao
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
User Behavior ◽  
Real Data ◽  
User Profile ◽  
Data Set ◽  
Sina Weibo ◽  
Factors Affecting ◽  
The Government ◽  
Hot Event

With the development of Web 2.0, social media such as Twitter and Sina Weibo have become an essential platform for disseminating hot events. Simultaneously, due to the free policy of microblogging services, users can post user-generated content freely on microblogging platforms. Accordingly, more and more hot events on microblogging platforms have been labeled as spammers. Spammers will not only hurt the healthy development of social media but also introduce many economic and social problems. Therefore, the government and enterprises must distinguish whether a hot event on microblogging platforms is a spammer or is a naturally-developing event. In this paper, we focus on the hot event list on Sina Weibo and collect the relevant microblogs of each hot event to study the detecting methods of spammers. Notably, we develop an integral feature set consisting of user profile, user behavior, and user relationships to reflect various factors affecting the detection of spammers. Then, we employ typical machine learning methods to conduct extensive experiments on detecting spammers. We use a real data set crawled from the most prominent Chinese microblogging platform, Sina Weibo, and evaluate the performance of 10 machine learning models with five sampling methods. The results in terms of various metrics show that the Random Forest model and the over-sampling method achieve the best accuracy in detecting spammers and non-spammers.

scholarly journals Demographic and socio-economic factors affecting birth preparation and complication readiness (BPCR) practices in Nepal

2018 ◽  
Vol 18 (17) ◽  
pp. 23-32
Author(s):  
Sunil Kumar Acharya
Keyword(s):  
Economic Status ◽  
Economic Factors ◽  
Data Set ◽  
Complication Readiness ◽  
Factors Affecting ◽  
Logistics Regression ◽  
Analysis Technique ◽  
The Government ◽  
Status Of Women ◽  
Socio Economic Factors

BPCR practices by women in Nepal are still low. Still a relatively high percentage of women do not make BPCR to its fullest extent. Researches in developing countries show that various demographic, social and economic factors influence the BPCR practices by pregnant women. This paper examines the likelihood of BPCR practices based on women’s demographic, social and economic status in Nepal. NDHS 2011 data set has been utilized by applying bivariate logistics regression analysis technique to examine the effects of these variables on BPCR practices in Nepal. The analysis result shows high variations and gaps in BPCR practice based on demographic, social and economic status of women. Against this finding the study recommends implementation of appropriate policy and program measures by the government and other agencies to address the existing variations and gaps in BPCR practices among subgroups of women in Nepal. Further research studies focusing on the existing barriers on BPCR practice need to be conducted in Nepal especially among the women who are disadvantaged and marginalized.

scholarly journals MODELING AND FORECASTING OF INDIA’S DEFENSE EXPENDITURES USING BOX-JENKINS ARIMA MODEL

2021 ◽  
Vol 9 (2) ◽  
pp. 334-344
Author(s):  
Sapana Sharma ◽  
Sanju Karol
Keyword(s):  
Arima Model ◽  
Annual Data ◽  
Policy Makers ◽  
Rational Decision Making ◽  
Defense Expenditure ◽  
Forecasting Method ◽  
Modeling And Forecasting ◽  
One Step ◽  
The Government ◽  
The One

Many developed and developing countries are at the core of the security and peace agenda concerning rising defense expenditure and its enduring sustainability. The unremitting upsurge in defense expenditure pressurizes the government to rationally manage the resources so as to provide security and peace services in the most efficient, effective and equitable way. It is necessary to forecast the defense expenditure in India which leads the policy makers to execute reforms in order to detract burdens on these resources, as well as introduce appropriate plan strategies on the basis of rational decision making for the issues that may arise. The purpose of this study is to investigate the appropriate type of model based on the Box–Jenkins methodology to forecast defense expenditure in India. The present study applies the one-step ahead forecasting method for annual data over the period 1961 to 2020. The results show that ARIMA (1,1,1) model with static forecasting being the most appropriate to forecast the India’s defense expenditure.

scholarly journals Forecasting of Air Passengers using ARIMA Modeling

2019 ◽  
Vol 8 (11S) ◽  
pp. 1050-1054
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Autocorrelation Function ◽  
Arima Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Forecasting Method ◽  
Partial Autocorrelation Function ◽  
Time Series Method ◽  
Arima Modeling

Air passengers prediction is said to be the centre of gravity of the growth. With people on the move constantly, there is bound to be some dissatisfaction amongst the customers which could be due to various reason, varying from overbooking of flights to ground operations. This dissatisfaction can be controlled till a limit, in ballpark figuring. In the past, this has been done using various machine learning techniques. For this prediction, in this project, ARIMA Modeling is used which is a time series forecasting method, based on machine learning. To test the stationarity of the data, which is done using Dickey Fuller test. If the data is stationary, it is fit into the ARIMA Model. If the data isn’t stationary, it is made stationary by differencing or by logarithmic transformation. The logarithmic method to make the data stationary. Once the data is stationary, using the Partial autocorrelation function and the autocorrelation function, values of p and q are found, which are required in the time series method. These values are then fit into the ARIMA Modeling and hence, the results are predicted. Upon the use and fitting of various models, the ARIMA(2,1,2) has been the best fit, having the least RMS and RMSE values.

scholarly journals Assessing socio-cultural factors affecting integration of Almajiris into formal education system in Katsina state, Nigeria

2021 ◽  
Vol 15 (2) ◽  
pp. 168-177
Author(s):  
Adekunle Thomas Olutola ◽  
Rafiu Ademola Olatoye ◽  
Garba Kofanaisa Adamu
Keyword(s):  
Education System ◽  
Formal Education ◽  
Survey Design ◽  
Cultural Factors ◽  
Economic Factors ◽  
Factors Affecting ◽  
The Government ◽  
Test Retest Reliability ◽  
Descriptive Survey ◽  
Socio Economic Factors

This study investigated the socio-cultural factors affecting integration into the formal education system in Katsina State, Nigeria. Descriptive survey design was adopted for this study. There were 653 Almajiris purposively selected from twelve Almajiris schools in Katsina State. A researcher-designed questionnaire titled: ‘‘Almajiris socio-cultural factors questionnaire’’ (ASCFQ) with test-retest reliability coefficient of 0.88 was used to collect data for the study. Analysis of data was carried out using frequencies and percentages. Findings revealed that most parents abandon their children in these informal Arabic schools. Most Almajiris (603, that is 92.3%) responded that their Mallams (Teachers) do not feed them. Therefore, the only means of survival is through begging and doing menial jobs. Based on these findings, it is recommended that all the identified socio-economic factors affecting the integration of Almajiris into the formal education system should be addressed by the government. This can be done through public enlightenment campaigns and provision of incentives.

scholarly journals Trends in the management of global economic development in the post-pandemic period

2021 ◽  
pp. 76-86
Author(s):  
Iaroslav Petrunenko ◽  
Valentyna Chychun ◽  
Nataliia Shuprudko ◽  
Yuliia Kalynichenko ◽  
Issa Ali
Keyword(s):  
Economic Development ◽  
Public Confidence ◽  
Important Place ◽  
Forecasting Method ◽  
Short Run ◽  
World Economic ◽  
The Government ◽  
The Impact ◽  
Further Development

This article is devoted to the study of trends in the management of global economic development in the post-pandemic period. The paper developed recommendations for further development of countries in the context of the recurrence of pandemics. With this in mind, the main trends in the development of countries during the pandemic were considered and the impact of quarantine on the economies of various countries was determined. To model the future actions of states, based on studies of the historical preconditions for the development of countries in the post-crisis period, the basic patterns were identified, allowing to predict different scenarios of world economic development. The article introduces a forecasting method of global economic development based on the quadrant of trust and affluence of the population, which allows predicting various options for post-pandemic development according to four possible scenarios. The first option is a rapid V-shaped growth, which is based on the fact that with a sufficient level of public confidence in the government, as soon as the quarantine restrictions expire, the economy will gain momentum. The second one is the long-term U-shaped growth, which is expected to take place in terms of insufficient public confidence in the government, with a population reluctant to invest in economic growth. The third one is the L-shaped development, which does not provide for economic recovery in the short run due to public distrust and the impossibility of business recovery. Finally, the fourth scenario is the worst one: it's the way of development, that occurs in case of impossibility of survival and complete distrust to the government; the population will be forced to organize protests and revolutions, thus making the economy operate even worse. According to the expectations of international regulators, V or U-shaped recovery of the world economy after the COVID-19 pandemic is expected nowadays. Most businessmen believe that post-pandemic development will be U-shaped. To improve the mechanisms of managing the development of world economies, the directions of development are proposed focused not on capitalist, but social goals. The state should occupy an important place in this process acting as a guarantor of efficient allocation of resources and providing social guarantees to the population during possible further cataclysms.

scholarly journals Demographic and Socio-economic Factors Affecting on Health Facility Delivery in Nepal

2017 ◽  
Vol 25 (1-2) ◽  
pp. 24-35
Author(s):  
Sunil Kumar Acharya
Keyword(s):  
Health Facility ◽  
Economic Status ◽  
Facility Delivery ◽  
Economic Factors ◽  
Health Facility Delivery ◽  
Data Set ◽  
Factors Affecting ◽  
New Era ◽  
Analysis Technique ◽  
The Government

In Nepal, health facility delivery has improved since the 1990s, but child health delivery facility is still low. In 2001 about 9 percent of the births was delivered at a health facility (MoH, New ERA, and ORC Macro, 2002) which increased to 35 percent in 2011 (MOHP, New ERA, and ICF International Inc, 2012) and further increased to 57 percent in 2016 (MoH, New ERA and ICF, various 2017). This is a rapid increase in health facility delivery since 1996 but still nearly one-half of delivery take place at home. Research in developing countries shows that demographic, social and economic factors influence the utilization of health facility for delivery of births. This paper examines the likelihood of health facility delivery in relation to women’s demographic, social and economic status in Nepal. The 2011 Nepal Demographic and Health Survey (NDHS) data set has been utilized by applying bivariate logistics regression analysis technique to examine the effects of these variables in health facility delivery in Nepal. The analysis findings show large variations and gaps on delivery care based on demographic, social and economic status of women. Against this finding, the study concludes that there is a need for the implementation of appropriate policy and program measures by the government and other agencies to address the existing variations and gaps in utilization of health facility for delivery of births among different sub-groups of women in Nepal. Further research studies focusing on the existing barriers in health facility delivery need to be conducted in Nepal especially among women who are disadvantaged and marginalized.

scholarly journals PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (2) ◽  
pp. 194-207
Author(s):  
Riski Arum Pitaloka ◽  
Sugito Sugito ◽  
Rita Rahmawati
Keyword(s):  
Time Series ◽  
Trade Balance ◽  
Arima Model ◽  
Ensemble Averaging ◽  
High Increase ◽  
Forecasting Accuracy ◽  
Central Java ◽  
Forecasting Method ◽  
The Ensemble Method ◽  
The Government

Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging

scholarly journals Demographic and Socio-economic Factors Affecting Antenatal Care Services Utilization in Nepal

2018 ◽  
Vol 24 (1-2) ◽  
pp. 71-87
Author(s):  
Sunil Kumar Acharya
Keyword(s):  
Antenatal Care ◽  
Pregnant Women ◽  
Economic Status ◽  
Economic Factors ◽  
Data Set ◽  
Factors Affecting ◽  
Services Utilization ◽  
Care Services ◽  
Seeking Behavior ◽  
The Government

In Nepal, Antenatal care (ANC) services seeking behavior has improved since the 1990s. More pregnant women are seeking the services now than ever. Still a relatively high percentage does not seek even a onetime ANC care and the percentage of women who seek 4 or more ANC care from a skilled service provider is still low. Research in developing countries shows that various demographic, social and economic factors influence the utilization of ANC services and the number of times the care is sought by pregnant women. This paper examines the likelihood of utilization of ANC services 4 or more times in relation to women’s demographic, social and economic status in Nepal. The 2011 Nepal Demographic and Health Survey (NDHS) data set has been utilized by applying bivariate logistics regression analysis technique to examine the effects of these variables in 4 or more ANC use in Nepal. The analysis findings show large variations and gaps in the utilization of ANC care based on demographic, social and economic status of women. Against this finding, the study recommends implementation of appropriate policy and program measures by the government and other agencies to address the existing variations and gaps in ANC services utilization among different sub-groups of women in Nepal. Further research studies focusing on the existing barriers in ANC services utilization need to be conducted in Nepal especially among the women who are disadvantaged and marginalized.  The Journal of Development and Administrative Studies (JODAS)Vol. 24 (1-2), pp. 71-87

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