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The thesis is carried out in complete compliance with research ethics norms, and the codes and practices set by BRAC University. In our thesis, we use the data from primary sources. We collect data from different participants, and we use our own dataset for our thesis. We are ensuring we use references and in-text citations properly. We the four co-authors take full responsibility for the thesis code violations. For solving problems, we read different websites, YouTube tutorials, and Questionnaire Free tools. We also took help from our university faculty members. Finally, we declare that we give credit to every people from whom we took help. We did not make any fraud able means for completing the thesis. Our work is in compliance with the ethics standard set by BRAC University.

Deep learning has been very successful in the field of research which includes predictions. In this paper, one such prediction is discussed which can help to implement safe vaccination. Vaccination is very important in order to fight viral diseases such as covid-19. However, people at times have to go through unwanted side effects of the vaccinations which might often cause serious illness. Therefore, modern techniques are to be utilised for safe implementations of vaccines. In this research, Gated Recurrent Unit, GRU, which is a form of Recurrent Neural Network is used to predict whether a particular vaccine will have any side effect on a particular patient. The extracted predictions might be used before deciding whether a vaccine should be injected to a particular person or not.

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