Titlebook: Emerging Trends and Advanced Technologies for Computational Intelligence; Extended and Selecte Liming Chen,Supriya Kapoor,Rahul Bhatia Book

Perigee

蹣跚

slipped-disk

exophthalmos

,Gew?hnliche Differentialgleichungen,f human. The depth image is later fused with the region of interest obtained from the thermal image. Using the initial bounding box, occlusion handling is performed to determine the final position of human in the image. The proposed method significantly improves human detection even in crowded scene and poor illumination.

颶風(fēng)

,A Plantar Inclinometer Based Approach to?Fall Detection in Open Environments,thresholds were selected from plantar angles of fall status in four directions: forward, backward, left and right. Using the selected thresholds, we detected falls of five subjects in different situations for five hundred times and obtained the average detection rate of 85.4?%.

observatory

舉止粗野的人

1860-949X and Information Conference 2015, held at July 28-30 2015, L.This book?is a collection of extended chaptersfrom the selected papers that were published in the proceedings of Science andInformation (SAI) Conference 2015. It contains twenty-one chapters in the fieldof Computational Intelligence, which

無(wú)王時(shí)期,

https://doi.org/10.1007/978-3-662-52940-9age can be done with the gathered physical and psychological data with the sensor network for vital sign monitoring. Through a comparison between with and without consideration of triage, it is found that the time required for evacuation from disaster areas with consideration triage is 30?% less than that without triage.

偽證

Differenzengleichungen und Chaos,linear multi-class Support Vector Machines. We have conducted a comparison study with other machine learning approaches (i.e. Linear SVM, Hidden-Markov and K-nearest models). Experimental results show that our proposed approach outperforms the other methods for the scenarios evaluated in terms of accuracy and processing speed.

obtuse

Modelling and Detection of User Activity Patterns for Energy Saving in Buildings,linear multi-class Support Vector Machines. We have conducted a comparison study with other machine learning approaches (i.e. Linear SVM, Hidden-Markov and K-nearest models). Experimental results show that our proposed approach outperforms the other methods for the scenarios evaluated in terms of accuracy and processing speed.