Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is importa...Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.展开更多
为解决网络拥塞问题,实现网络用户的服务区分,提出了一种基于IPv6网络的可区分用户优先级的主动队列管理算法,即优先级区分RED(Random Early Detection)算法。该算法在IPv6基本报头的流标签域中标记用户的优先级,并对不同优先级的数据...为解决网络拥塞问题,实现网络用户的服务区分,提出了一种基于IPv6网络的可区分用户优先级的主动队列管理算法,即优先级区分RED(Random Early Detection)算法。该算法在IPv6基本报头的流标签域中标记用户的优先级,并对不同优先级的数据包进行不同的丢包处理。通过OMNeT++3.2模拟仿真的实验结果表明,基于IPv6网络的优先级区分RED算法能区分用户的优先级,保证了有优先级用户的服务质量。展开更多
文摘Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.
基金国家高技术研究发展计划(863)(the National High- Tech Research and Development Plan of China under Grant No.2003AA121560)江苏省高技术研究计划资助项目(the Jiangsu Province High- Tech Research and Development Plan China under Grant No.BEG200301)
文摘为解决网络拥塞问题,实现网络用户的服务区分,提出了一种基于IPv6网络的可区分用户优先级的主动队列管理算法,即优先级区分RED(Random Early Detection)算法。该算法在IPv6基本报头的流标签域中标记用户的优先级,并对不同优先级的数据包进行不同的丢包处理。通过OMNeT++3.2模拟仿真的实验结果表明,基于IPv6网络的优先级区分RED算法能区分用户的优先级,保证了有优先级用户的服务质量。
