TY -的A2 -辛格,Dilbag盟——Alanazi Saad Awadh盟——Kamruzzaman m . m . AU - Alruwaili Madallah AU - Alshammari,纳赛尔AU - Alqahtani,萨尔曼·阿里盟——Karime阿里PY - 2020 DA - 2020/11/02 TI -测量和防止COVID-19先生使用机器学习模型和智能医疗SP - 8857346六世- 2020 AB - COVID-19是一个紧迫的全球性挑战,因为它会传染的本性,经常变化的特征,缺乏疫苗或有效的药物。测量模型的持续传播,防止COVID-19迫切需要提供智能医疗保健服务。这需要使用先进的智能计算,如人工智能,机器学习,深入学习,认知计算、云计算、雾计算和计算边缘。提出了一种模型预测COVID-19使用先生和机器学习的智能医疗和福利的公民籍。知道易感,感染,和恢复情况每天对数学建模能够识别至关重要的行为影响大流行。它预测情况为即将到来的700天。提出系统预测COVID-19是否会在人群中传播或消亡。数学分析和仿真结果给出了这里来预测疫情的进展及其可能对三种类型的场景:“没有行动,”“封锁”,和“新药物。“干预措施的效果像封锁和新药物相比,“没有行动”的场景。锁定的情况下延迟峰值点减少感染和影响区域平等的受感染的曲线。另一方面,新药物产生重大影响感染曲线通过减少受感染的人对时间的数量。 Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully. SN - 2040-2295 UR - https://doi.org/10.1155/2020/8857346 DO - 10.1155/2020/8857346 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -