着力于增强办公软件的社会化分享能力,采用品牌架构、易用性、一致性、反馈机制和用户归属感等设计方法,整合办公软件与社交媒体的功能,致力于提升办公软件的用户体验,以云技术为技术支持,设计出一款基于Apache Open Office(AOO)的社会...着力于增强办公软件的社会化分享能力,采用品牌架构、易用性、一致性、反馈机制和用户归属感等设计方法,整合办公软件与社交媒体的功能,致力于提升办公软件的用户体验,以云技术为技术支持,设计出一款基于Apache Open Office(AOO)的社会化分享工具,实现了AOO与IBM Connections的集成,提升了AOO的社会化分享能力,开创了办公软件与IM以及社交媒体结合的先例。展开更多
Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design...Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design(CAD)system that presents a new method for DED classification called(IAOO-PSO),which is a powerful Feature Selection technique(FS)that integrates with Opposition-Based Learning(OBL)and Particle Swarm Optimization(PSO).We improve the speed of convergence with the PSO algorithmand the exploration with the IAOO algorithm.The IAOO is demonstrated to possess superior global optimization capabilities,as validated on the IEEE Congress on Evolutionary Computation 2022(CEC’22)benchmark suite and compared with seven Metaheuristic(MH)algorithms.Additionally,an IAOO-PSO model based on Support Vector Machines(SVMs)classifier is proposed for FS and classification,where the IAOO-PSO is used to identify the most relevant features.This model was applied to the DED dataset comprising 20,000 cases and 26 features,achieving a high classification accuracy of 99.8%,which significantly outperforms other optimization algorithms.The experimental results demonstrate the reliability,success,and efficiency of the IAOO-PSO technique for both FS and classification in the detection of DED.展开更多
文摘着力于增强办公软件的社会化分享能力,采用品牌架构、易用性、一致性、反馈机制和用户归属感等设计方法,整合办公软件与社交媒体的功能,致力于提升办公软件的用户体验,以云技术为技术支持,设计出一款基于Apache Open Office(AOO)的社会化分享工具,实现了AOO与IBM Connections的集成,提升了AOO的社会化分享能力,开创了办公软件与IM以及社交媒体结合的先例。
文摘Thediagnosis of Dry EyeDisease(DED),however,usually depends on clinical information and complex,high-dimensional datasets.To improve the performance of classification models,this paper proposes a Computer Aided Design(CAD)system that presents a new method for DED classification called(IAOO-PSO),which is a powerful Feature Selection technique(FS)that integrates with Opposition-Based Learning(OBL)and Particle Swarm Optimization(PSO).We improve the speed of convergence with the PSO algorithmand the exploration with the IAOO algorithm.The IAOO is demonstrated to possess superior global optimization capabilities,as validated on the IEEE Congress on Evolutionary Computation 2022(CEC’22)benchmark suite and compared with seven Metaheuristic(MH)algorithms.Additionally,an IAOO-PSO model based on Support Vector Machines(SVMs)classifier is proposed for FS and classification,where the IAOO-PSO is used to identify the most relevant features.This model was applied to the DED dataset comprising 20,000 cases and 26 features,achieving a high classification accuracy of 99.8%,which significantly outperforms other optimization algorithms.The experimental results demonstrate the reliability,success,and efficiency of the IAOO-PSO technique for both FS and classification in the detection of DED.
