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The purpose of this research is to examine the relationship between the Dhaka Stock exchange index return and macroeconomic variables such as exchange rate, inflation, money supply etc. The long-term relationship between macroeconomic variables and stock market returns has been analyzed by using the Johnson Cointegration test, Augmented Dicky Fuller (ADF) and Phillip Perron (PP) tests. The results revealed the existence of cointegrating relationship between stock prices and the macroeconomic variables in the Dhaka stock exchange. The consumer price index, money supply, and exchange rates proved to be strongly associated with stock returns, while market capitalization was found to be negatively associated with stock returns. The findings suggest that in the long run, the Dhaka stock exchange is reactive to macroeconomic indicators.

Bangladesh is located in the alluvial floodplain of the Ganges–Brahmaputra–Meghna (GMB) river system and its numerous tributaries. In this country, the frequency, as well as severity, of riverbank erosion is high. To examine the impacts of riverbank erosion on rural food security, a baseline survey was conducted in Zajira, Bangladesh. The findings show that most people in the study area are affected by riverbank erosion. Moreover, agricultural production is gradually decreasing as a result of loss of farmland due to the catastrophe and its impacts. In some cases, people lose their entire homestead and are left with few income opportunities and low purchasing power. Consequently, they face the constant threat of a food crisis. The affected people follow some coping mechanisms to face these stressful situations, such as relying on less expensive or less preferred food items. While the local-government authorities have implemented some programmes to assist them, such as food aid and social-safety-net schemes, these are insufficient, due to the government's limited institutional resources and capacities. These findings suggest that for formulating effective rural-development plans, assessing the impact of riverbank erosion on the food security of people living in the affected areas is a prerequisite.

創造科学技術大学院, 甲第1236号

The amount of deaf and mute individuals on the earth is rising at an alarming rate. Bangladesh has about 2.6 million people who are unable to interact with the community using language. Hearing-impaired citizens in Bangladesh use Bangladeshi sign language (BSL) as a means of communication. In this article, we propose a new method for Bengali sign language recognition based on deep convolutional neural networks. Our framework employs convolutional neural networks (CNN) to learn from the images in our dataset and interpret hand signs from input images. Checking their collections of ten indications (we used ten sets of images with 31 distinct signs) for a total of 310 images. The proposed system takes snapshots from a video by using a webcam with applying a computer vision-based approach. After that, it compares those photos to a previously trained dataset generated with CNN and displays the Bengali numbers (০-৯). After estimating the model on our dataset, we obtained an overall accuracy of 99.8%. We want to strengthen things as far as we can to make silent contact with the majority of society as simple as probable.

The pandemic has taken the world by storm. Almost the entire world went into lockdown to save the people from the deadly COVID-19. Scientists around the around have come up with several vaccines for the virus. Among them, Pfizer, Moderna, and AstraZeneca have become quite famous. General people however have been expressing their feelings about the safety and effectiveness of the vaccines on social media like Twitter. In this study, such tweets are being extracted from Twitter using a Twitter API authentication token. The raw tweets are stored and processed using NLP. The processed data is then classified using a supervised KNN classification algorithm. The algorithm classifies the data into three classes, positive, negative, and neutral. These classes refer to the sentiment of the general people whose Tweets are extracted for analysis. From the analysis it is seen that Pfizer shows 47.29% positive, 37.5% negative, and 15.21% neutral, Moderna shows 46.16% positive, 40.71% negative, and 13.13% neutral, AstraZeneca shows 40.08% positive, 40.06% negative, and 13.86% neutral sentiment.