Research

The COVID-19 pandemic is among the most serious global threats, and it is still a significant concern. The people of Bangladesh are undergoing one of the world's largest vaccination drive. With the recent launch and introduction of the COVID-19 vaccines, many of us are curious about the general opinion or view of the vaccine. While the vaccine has ignited new hope in the battle against COVID-19, it has also sparked militant anti-vaccine campaigns, so the need to analyze public opinion on the COVID-19 vaccine has emerged. Methods: Traditional machine learning methods were used to obtain a benchmark result for the experiment. The recurrent neural network (RNN) algorithm was used next. Several different types of recurrent neural networks were used, including simple RNNs, Gated Recurrent Units (GRUs), and LSTMs. Finally, to achieve a more optimal result, small BERT models (Bidirectional Encoder Representations from Transformers) were used. Results: Upon study and testing on several models and methods, it can be seen that BERT model was the most accurate of the bunch, which was 84%. On the other hand, Naive Bayes was able to obtain an accuracy of 81%. Naive Bayes and BERT produced similar results in F1- Score, but the performance of Naive Bayes can improve as the dataset size grows. Conclusion: Knowing about public opinions on the COVID-19 vaccine is critical, and action must be taken to ensure that everybody understands the value of vaccination and that everybody receives the COVID-19 vaccine. Vaccination may help to develop immunity, which lowers the likelihood of contracting the disease and its consequences. 

Fresh water is a vital resource for the survival of our population. In countries like Bangladesh where clean water is scarce, overusing or wasting household water limits the availability of it for other communities to use for drinking, cleaning, cooking or growing and thus contributes to disease, illness, or agricultural scarcity/starvation. In relation to this the key concern is to develop an efficient, cost effective and real-time system that monitors leakage in the tank, water overflow and the turbidity of the water. Additionally, the system does not require any user interaction.

Some clinical trials of patients with covid-19 have shown that most of the patients affected by covid-19 experience lung infection after virus- contiguity. Chest x-rays and CT scans play an important role in the detection of lung-related illnesses. Therefore, for the diagnosis of covid-19, radiography and chest CT are considered as important imaging approaches. In this study, we have presented a method based on a densely interconnected convolutional neural network to identify covid19, pneumonia and normal patients from chest x-ray images. To create a new dataset and observe the performance of the proposed model, 6518 images have been assembled from two different datasets. The datasets contain the chest x-ray images of covid-19 affected patients, normal patients and pneumonia affected patients. The dataset that is used in this work contains 400 covid-19 affected x-ray images, 1212 normal x-ray images and 3212 pneumonia affected x-ray images. The proposed densely interrelated convolutional neural network model provides 98% testing accuracy for identifying chest radiography images without the application of any augmentation techniques.