Coronavirus disease 2019 brings a huge burden on the medical industry all over the world.In the background of artificial intelligence(AI)and Internet of Things(IoT)technologies,chest computed tomography(CT)and chest X...Coronavirus disease 2019 brings a huge burden on the medical industry all over the world.In the background of artificial intelligence(AI)and Internet of Things(IoT)technologies,chest computed tomography(CT)and chest Xray(CXR)scans are becoming more intelligent,and playing an increasingly vital role in the diagnosis and treatment of diseases.This paper will introduce the segmentation of methods and applications.CXR and CT diagnosis of COVID-19 based on deep learning,which can be widely used to fight against COVID-19.展开更多
Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel meth...Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.展开更多
基金supported by the Open Project of State Key Laboratory of Millimeter Wave,Southeast University,China,under Grant K202218.
文摘Coronavirus disease 2019 brings a huge burden on the medical industry all over the world.In the background of artificial intelligence(AI)and Internet of Things(IoT)technologies,chest computed tomography(CT)and chest Xray(CXR)scans are becoming more intelligent,and playing an increasingly vital role in the diagnosis and treatment of diseases.This paper will introduce the segmentation of methods and applications.CXR and CT diagnosis of COVID-19 based on deep learning,which can be widely used to fight against COVID-19.
文摘Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.
