Titlebook: Radio Design in Nanometer Technologies; MOHAMMED ISMAIL,DELIA RODRíGUEZ DE LLERA GONZáLEZ Book 2006 Springer Science+Business Media B.V. 2

變量

Obvious

細(xì)胞膜

condition of ensuring node-perceived data privacy, RPSAQ collects sensing data of nodes by traversing the grids in the query area, which not only reduces the packet loss rate and energy consumption of sensor nodes, but also ensures the sensing data’s privacy. The experiment results show that RPSAQ o

interpose

Delia Rodríguez de Llera González,Ana Rusu,Mohammed Ismailckets in bypassing machine learning detection techniques. We then propose a voting ensemble method to improve adversarial attack detection. The voting ensemble proposed increases the accuracy of adversarial detection, providing a new level of protection against adversarial example attacks.

驚惶

Elyse Rosenbaum,Sami Hyvonenf two objectives, we propose a novel framework that disentangle the original NER labels into two additional training labels for entity segmentation and entity classification respectively. Then we introduce two dedicated expert models to effectively extract specific knowledge from the disentangled la

Glucocorticoids

NICHE

Niklas Troedsson,Henrik Sj?landtion of 2D image processing and 3D depth-sensing technologies results in a system that can monitor a physical space where entities come into proximity to one another. Furthermore, the system can be used to prevent transgressions between physical entities within a relative distance of a few centimetr

可耕種

Waleed Khalil,Bertan Bakkaloglunes the two results to clearly evaluate the discriminating power of most features via smooth distributed score vector. It is extremely efficient when cluster number is small. Experimental results over various real-life data demonstrate the effectiveness of the proposed algorithm.

PET-scan

James Wilson,Mohammed Ismailof the classifiers, an ensemble classifier based on kernel method for multi-situation DNA microarray data was proposed by adopting the idea of ensemble learning. The ensemble classifier combines the classification results of the SVM, KSUB-C and KPLS-DA classifiers. Experimental results involving thr

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