This paper focuses on the quantitative analysis issue of the routing metrics tradeoff problem, and presents a Quantified Cost-Balanced overlay multicast routing scheme (QCost-Balanced) to the metric tradeoff problem b...This paper focuses on the quantitative analysis issue of the routing metrics tradeoff problem, and presents a Quantified Cost-Balanced overlay multicast routing scheme (QCost-Balanced) to the metric tradeoff problem between overlay path delay and access bandwidth at Multicast Server Nodes (MSN) for real-time ap-plications over Internet. Besides implementing a dynamic priority to MSNs by weighing the size of its service clients for better efficiency, QCost-Balanced tradeoffs these two metrics by a unified tradeoff metric based on quantitative analysis. Simulation experiments demonstrate that the scheme achieves a better tradeoff gain in both two metrics, and effective performance in metric quantitative control.展开更多
Objective To build a prostate cancer(PCa) risk prediction model based on common clinical indicators to provide a theoretical basis for the diagnosis and treatment of PCa and to evaluate the value of artificial intelli...Objective To build a prostate cancer(PCa) risk prediction model based on common clinical indicators to provide a theoretical basis for the diagnosis and treatment of PCa and to evaluate the value of artificial intelligence(AI) technology under healthcare data platforms.Methods After preprocessing of the data from Population Health Data Archive,smuothly clipped absolute deviation(SCAD) was used to select features.Random forest(RF),support vector machine(SVM),back propagation neural network(BP),and convolutional neural network(CNN) were used to predict the risk of PCa,among which BP and CNN were used on the enhanced data by SMOTE.The performances of models were compared using area under the curve(AUC) of the receiving operating characteristic curve.After the optimal model was selected,we used the Shiny to develop an online calculator for PCa risk prediction based on predictive indicators.Results Inorganic phosphorus,triglycerides,and calcium were closely related to PCa in addition to the volume of fragmented tissue and free prostate-specific antigen(PSA).Among the four models,RF had the best performance in predicting PCa(accuracy:96.80%;AUC:0.975,95% CI:0.964-0.986).Followed by BP(accuracy:85.36%;AUC:0.892,95% CI:0.849-0.934) and SVM(accuracy:82.67%;AUC:0.824,95% CI:0.805-0.844).CNN performed worse(accuracy:72.37%;AUC:0.724,95% CI:0.670-0.779).An online platform for PCa risk prediction was developed based on the RF model and the predictive indicators.Conclusions This study revealed the application value of traditional machine learning and deep learning models in disease risk prediction under healthcare data platform,proposed new ideas for PCa risk prediction in patients suspected for PCa and had undergone core needle biopsy.Besides,the online calculation may enhance the practicability of AI prediction technology and facilitate medical diagnosis.展开更多
Traditional 802.11 power saving mechanism (PSM) treats multicast and broadcast traffic equally, and suffers sig-nificant performance degradation with multicast background traffic. This paper proposes an enhanced PSM t...Traditional 802.11 power saving mechanism (PSM) treats multicast and broadcast traffic equally, and suffers sig-nificant performance degradation with multicast background traffic. This paper proposes an enhanced PSM that effectively dif-ferentiates multicast streams. It re-arranges the virtual bitmap of the traffic indication map (TIM) to carry traffic status for mul-ticast groups and introduces a concept of sequential transmission of multi-addressed data to facilitate differentiation among mul-ticast groups. Our analysis shows that the enhanced PSM can effectively save power in mixed traffic environments.展开更多
文摘This paper focuses on the quantitative analysis issue of the routing metrics tradeoff problem, and presents a Quantified Cost-Balanced overlay multicast routing scheme (QCost-Balanced) to the metric tradeoff problem between overlay path delay and access bandwidth at Multicast Server Nodes (MSN) for real-time ap-plications over Internet. Besides implementing a dynamic priority to MSNs by weighing the size of its service clients for better efficiency, QCost-Balanced tradeoffs these two metrics by a unified tradeoff metric based on quantitative analysis. Simulation experiments demonstrate that the scheme achieves a better tradeoff gain in both two metrics, and effective performance in metric quantitative control.
文摘Objective To build a prostate cancer(PCa) risk prediction model based on common clinical indicators to provide a theoretical basis for the diagnosis and treatment of PCa and to evaluate the value of artificial intelligence(AI) technology under healthcare data platforms.Methods After preprocessing of the data from Population Health Data Archive,smuothly clipped absolute deviation(SCAD) was used to select features.Random forest(RF),support vector machine(SVM),back propagation neural network(BP),and convolutional neural network(CNN) were used to predict the risk of PCa,among which BP and CNN were used on the enhanced data by SMOTE.The performances of models were compared using area under the curve(AUC) of the receiving operating characteristic curve.After the optimal model was selected,we used the Shiny to develop an online calculator for PCa risk prediction based on predictive indicators.Results Inorganic phosphorus,triglycerides,and calcium were closely related to PCa in addition to the volume of fragmented tissue and free prostate-specific antigen(PSA).Among the four models,RF had the best performance in predicting PCa(accuracy:96.80%;AUC:0.975,95% CI:0.964-0.986).Followed by BP(accuracy:85.36%;AUC:0.892,95% CI:0.849-0.934) and SVM(accuracy:82.67%;AUC:0.824,95% CI:0.805-0.844).CNN performed worse(accuracy:72.37%;AUC:0.724,95% CI:0.670-0.779).An online platform for PCa risk prediction was developed based on the RF model and the predictive indicators.Conclusions This study revealed the application value of traditional machine learning and deep learning models in disease risk prediction under healthcare data platform,proposed new ideas for PCa risk prediction in patients suspected for PCa and had undergone core needle biopsy.Besides,the online calculation may enhance the practicability of AI prediction technology and facilitate medical diagnosis.
基金Project (Nos. 60574087 and 60721003) supported by the National Natural Science Foundation of China
文摘Traditional 802.11 power saving mechanism (PSM) treats multicast and broadcast traffic equally, and suffers sig-nificant performance degradation with multicast background traffic. This paper proposes an enhanced PSM that effectively dif-ferentiates multicast streams. It re-arranges the virtual bitmap of the traffic indication map (TIM) to carry traffic status for mul-ticast groups and introduces a concept of sequential transmission of multi-addressed data to facilitate differentiation among mul-ticast groups. Our analysis shows that the enhanced PSM can effectively save power in mixed traffic environments.
