论文标题
基于神经网络的国家明智的风险预测COVID-19
Neural network based country wise risk prediction of COVID-19
论文作者
论文摘要
新颖的冠状病毒（Covid-19）最近在全球爆发的爆发为研究界带来了新的挑战。人工智能（AI）驱动的方法可用于预测这种流行病的参数，风险和影响。这样的预测有助于控制和防止此类疾病的传播。应用AI的主要挑战是数据量很小和不确定的性质。在这里，我们提出了一个基于浅的长期记忆（LSTM）神经网络，以预测一个国家的风险类别。我们已经使用贝叶斯优化框架来优化和自动设计特定于国家 /地区的网络。结果表明，拟议的管道的表现优于180个国家数据的最先进方法，并且可以作为这种风险分类的有用工具。我们还尝试了趋势数据和天气数据的合并以进行预测。结果表明天气没有重要作用。该工具可用于预测这种流行病的长期爆发，以便我们可以更早采取预防步骤
The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlier
