ABSTRACT
Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. Similarly, in economic domain, it can be applied to analyse many unexpected events to harvest new knowledge like sudden crash of stock market, mismatch between country's per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest. These situations can arise due to several reasons, out of which the present COVID-19 pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them. Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently. Copyright © 2023 Inderscience Enterprises Ltd.
ABSTRACT
India is one of the largest nations with many geographical differences, which makes the learning process a difficult one in the present COVID-19 scenario. However, online mode offers big opportunity to reach out to students in remote locations though it has its own challenges too. This paper identifies a few such challenges and suggests mitigation strategies towards the same. In addition, an in-depth analysis is performed on a real COVID-19 student dataset to understand student overall behaviour in this pandemic situation as well as their experience in the online learning mode. Also, an automated framework of performing student feedback analysis is presented, which can be utilised to understand the quality of online classes by finding more useful insights from student responses. These measures will definitely support a vulnerable student population to overcome the uncertainties present in the period of extraordinary disruption.
ABSTRACT
India is one of the largest nations with many geographical differences, which makes the learning process a difficult one in the present COVID-19 scenario. However, online mode offers big opportunity to reach out to students in remote locations though it has its own challenges too. This paper identifies a few such challenges and suggests mitigation strategies towards the same. In addition, an in-depth analysis is performed on a real COVID-19 student dataset to understand student overall behaviour in this pandemic situation as well as their experience in the online learning mode. Also, an automated framework of performing student feedback analysis is presented, which can be utilised to understand the quality of online classes by finding more useful insights from student responses. These measures will definitely support a vulnerable student population to overcome the uncertainties present in the period of extraordinary disruption. Copyright © 2023 Inderscience Enterprises Ltd.
ABSTRACT
Outliers, or outlying observations, are values in data, which appear unusual. It is quite essential to analyze various unexpected events or anomalies in economic domain like sudden crash of stock market, mismatch between country’s per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest to find the insights for the benefit of humankind. These situations can arise due to several reasons, out of which pandemic is a major one. The present COVID-19 pandemic also disrupted the global economy largely as various countries faced various types of difficulties. This motivates the present researchers to identify a few such difficult areas in economic domain, arises due to the pandemic situation and identify the countries, which are affected most under each bucket. Two well-known machine-learning techniques DBSCAN (density based clustering approach) and Z-score (statistical technique) are utilized in this analysis. The results can be used as suggestive measures to the administrative bodies, which show the effectiveness of the study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.