Objectives: 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.
As soon as coins or money was invented, there were people trying to make counterfeits. Counterfeit money is fake money that is produced without the permission of the state or government, usually to imitate the currency and deceive the intended recipient. In Bangladesh, this is a significant problem and the problem is becoming more and more phenomenon as the days are passing by. Today's modern bank notes have several security features that makes easier to identify fake notes. One of the security features is the use of UV ink. Bank notes deliberately put random flecks of color scattered all over the surface of the money -which acts as a extra layer of protection against counterfeiters. We propose an automatic authentication model for identifying counterfeit money based on these random flecks of color which is visible under UV light. To obtain a benchmark result, existing object detection pre-trained models were used, followed by MobileNet, Inception, ResNet50, ResNet101, and Inception-ResNet architectures. After that, using the Region Proposal Network (RPN) method with Convolutional Neural Network (CNN) based classification the optimal model was proposed. The proposed model had a 96.3 percent accuracy. It is critical to reduce the circulation of counterfeit money in a country's economy to stop inflation. This study will aid in the detection of counterfeit money and, hopefully, reduce its spread.
As soon as coins or money was invented, there were people trying to make counterfeits. Counterfeit money is fake money that is produced without the permission of the state or government, usually to imitate the currency and deceive the intended recipient. In Bangladesh, this is a significant problem and the problem is becoming more and more phenomenon as the days are passing by. Today’s modern bank notes have several security features that makes easier to identify fake notes. One of the security features is the use of UV ink. Bank notes deliberately put random flecks of color scattered all over the surface of the money - which acts as a extra layer of protection against counterfeiters. We propose an automatic authentication model for identifying counterfeit money based on these random flecks of color which is visible under UV light. To obtain a benchmark result, existing object detection pre-trained models were used, followed by MobileNet, Inception, ResNet50, ResNet101, and Inception-ResNet architectures. After that, using the Region Proposal Network (RPN) method with Convolutional Neural Network (CNN) based classification the optimal model was proposed. The proposed model had a 96.3 percent accuracy. It is critical to reduce the circulation of counterfeit money in a country’s economy to stop inflation. This study will aid in the detection of counterfeit money and, hopefully, reduce its spread.
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