COVID-19 pandemic creates a worldwide problematic situation as well as Bangladesh which secures the top ten GDP growths in the last decade. Since this country has an agriculture dominated economy, this pandemic badly affects on the agricultural business sector. However, the significance of the COVID-19 pandemic may vary from different parts of Bangladesh. To visualize those effects on the consumers' points of view, this research has been conducted for measuring the different points of pandemic affects on 10 different agricultural products. A survey has been designed to collect the consumers' perception to buy agricultural food during this pandemic and a total of 200+ valid data was selected for analysis. Data has been randomly collected from all over the country having a prioritized location with a higher COVID-19 detection rate. Respondents shared their viewpoints on 10 different agricultural products type named as Coarse Rice, finer quality rice, beef & mutton, poultry & egg, local fish, exported fish, fruits & vegetables, cooking oil, spice crops, and imported foods. The data are statistically analyzed to answer three research questions regarding food availability, price hiking, and the government's initiatives to mitigate the impact of this pandemic. It has been found that almost every consumer reports comparatively higher pricing and a lack of agricultural products in the domestic market. All the data are negatively skewed for pricing in terms of any cities in Bangladesh, which means every consumer suffer from the price hiking during this pandemic. It also depicted that the food crisis was more dominated in the capital city rather than the remote local villages, which may happen due to the supply chain disruption of perishable products. However, the government already took some initiatives to mitigate the effect of this pandemic, but more thanthemajority of the respondents are not fully convinced of that. An interesting finding is that the crisis issue is not significantly dependent on any consumers' demographic data, which means every category of consumers already more or less affected by the pandemic.
The Covid-19 pandemic is heavily impacting not only the health sector but also every economical sector all over the world. Bangladesh is among the major agriculture production-based economies and is suffering greatly during this pandemic. This paper presents the current agricultural situation in Bangladesh based on a survey involving various pools of experts. It also visualizes the strategies used to assess the competency of initiatives aimed at minimizing the problems faced by farmers in Bangladesh arising from the COVID-19 pandemic. Data were collected from 117 expert agricultural personnel in Bangladesh (75.27% male and 24.73% female). Twenty-eight problems were identified that are faced by the farmers of Bangladesh arising from the COVID-19 pandemic. Among these, twenty two were severe while six were less so. It was found that problems relating to product wastage, low product-level pricing, the absence of traders, and transportation issues were identified as top ranking. Twenty-six initiatives were introduced and adopted by different organizations in Bangladesh to minimize agricultural problems arising from the COVID-19 pandemic. Among these, five were highly effective and the remainder moderately so. It was found that initiatives related to government directives and financial support were identified as the top ranked. Based on analysis of data, this paper concludes with several suggestions aimed at minimizing the problems faced by farmers in Bangladesh arising from the COVID-19 pandemic.
Bangladesh is one of the most promising developing countries in IT sector, where people from several disciplines and experiences are involved in this sector. However, no direct analysis in this sector is published yet, which covers the proper guideline for predicting future IT personnel. Hence this is not a simple solution, training data from real IT sector are needed and trained several classifiers for detecting perfect results. Machine learning algorithms can be used for predicting future potential IT personnel. In this paper, four different classifiers named as Naive Bayes, J48, Bagging and Random Forest in five different folds are experimented for that prediction. Results are pointed out that Random Forest performs better accuracy than other experimented classifier for future IT personnel prediction. It is mentioned that the standard accuracy measurement process named as Precision, Recall, F-Measure, ROC Area etc. are used for evaluating the results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.