VIS-NLP: Vaccination Inventory System for justified user using Natural Language Processing

Minh Phuc Vu, Satyam Mishra, Le Trung Thanh, Damilola Oni
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Abstract

In the healthcare industry, especially the Covid-19 pandemic in 2020, produced huge problems with isolate patient and patient heath. Thus, created large amount of data that has been generated every day for the patient heath, in this case is to justify the vaccination of users from social network Twitter. Processing such large volume of the data involves high computation overhead. Good health and well-being; to ensure healthy lives and promote well-being for all at all ages is United Nations 3rd Sustainable Development Goal and we want to align our study with it as well. It is crucial to create an application that is beneficial for humanity health. When we get large datasets from pandemics like Covid-19, for large scale datasets, we presented a solution to verify the user if they are vaccinated or not vaccinated by using Natural Language Processing methods to build an accuracy result, we tried to reduce the computation overhead by storing the data in distributed environment. After processing data, training the data, used pad_sequences, Keras, NLP to build the model. Through multiple epochs we have got an accuracy towards 90 to 91% (which is closer to state-of-the-art methods i.e., 95%). And since our accuracy is higher, we can further utilize it to increase for higher number of epochs. We hope scientists can further develop it and use it in real world applications so that more precious human being lives can be saved. By implementation of its successful results, it also aligns with one of the United Nations Sustainable Development Goals i.e., 3rd: Good Health and Well-Being.
使用自然语言处理的合理用户的疫苗库存系统
在医疗保健行业,特别是2020年的Covid-19大流行,在隔离患者和患者健康方面产生了巨大的问题。因此,创建了大量的数据,这些数据每天都在为患者的健康产生,在这种情况下,是为了证明来自社交网络Twitter的用户接种疫苗是合理的。处理如此大量的数据涉及很高的计算开销。良好的健康和福祉;确保健康生活和促进各年龄段所有人的福祉是联合国第三个可持续发展目标,我们也希望将我们的研究与该目标结合起来。创建一个对人类健康有益的应用程序至关重要。当我们获得来自Covid-19等流行病的大型数据集时,对于大规模数据集,我们提出了一种解决方案,通过使用自然语言处理方法来验证用户是否接种疫苗来构建准确性结果,我们试图通过将数据存储在分布式环境中来减少计算开销。在对数据进行处理后,对数据进行训练,使用pad_sequences、Keras、NLP建立模型。通过多个时代,我们得到了接近90%到91%的精度(这更接近于最先进的方法,即95%)。由于我们的精度更高,我们可以进一步利用它来增加更多的epoch。我们希望科学家们能进一步开发它,并将其应用于现实世界,以挽救更多宝贵的人类生命。通过落实其成功成果,它还符合联合国可持续发展目标之一,即第三项:良好健康和福祉。
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