Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of mach...Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.展开更多
Forecasting future outbreaks can help in minimizing their spread.Influenza is a disease primarily found in animals but transferred to humans through pigs.In 1918,influenza became a pandemic and spread rapidly all over...Forecasting future outbreaks can help in minimizing their spread.Influenza is a disease primarily found in animals but transferred to humans through pigs.In 1918,influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population.Afterwards,that influenza became a pandemic several times on a local and global levels.In 2009,influenza‘A’subtype H1N1 again took many human lives.The disease spread like in a pandemic quickly.This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network(MSDII-FFNN).This model helps us predict the outbreak,and determines which type of influenza becomes a pandemic,as well as which geographical area is infected.Data collection for the model is done by using IoT devices.This model is divided into 2 phases:The training phase and the validation phase,both being connected through the cloud.In the training phase,the model is trained using FFNN and is updated on the cloud.In the validation phase,whenever the input is submitted through the IoT devices,the system model is updated through the cloud and predicts the pandemic alert.In our dataset,the data is divided into an 85%training ratio and a 15%validation ratio.By applying the proposed model to our dataset,the predicted output precision is 90%.展开更多
基金This project was supported by grants from the Ministry of Education Humanities and Social Sciences Research Fund Project。
文摘Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.
基金Data and Artificial Intelligence Scientific Chair at UmmAlQura University.
文摘Forecasting future outbreaks can help in minimizing their spread.Influenza is a disease primarily found in animals but transferred to humans through pigs.In 1918,influenza became a pandemic and spread rapidly all over the world becoming the cause behind killing one-third of the human population and killing one-fourth of the pig population.Afterwards,that influenza became a pandemic several times on a local and global levels.In 2009,influenza‘A’subtype H1N1 again took many human lives.The disease spread like in a pandemic quickly.This paper proposes a forecasting modeling system for the influenza pandemic using a feed-forward propagation neural network(MSDII-FFNN).This model helps us predict the outbreak,and determines which type of influenza becomes a pandemic,as well as which geographical area is infected.Data collection for the model is done by using IoT devices.This model is divided into 2 phases:The training phase and the validation phase,both being connected through the cloud.In the training phase,the model is trained using FFNN and is updated on the cloud.In the validation phase,whenever the input is submitted through the IoT devices,the system model is updated through the cloud and predicts the pandemic alert.In our dataset,the data is divided into an 85%training ratio and a 15%validation ratio.By applying the proposed model to our dataset,the predicted output precision is 90%.
