iDispenser - Big Data Enabled Intelligent Dispenser

被引:3
|
作者
Ilapakurti, Anitha [1 ]
Vuppalapati, Jaya Shankar [1 ]
Kedari, Santosh [1 ]
Kedari, Sharat [1 ]
Chauhan, Chitanshu [1 ]
Vuppalapati, Chandrasekar [1 ,2 ]
机构
[1] Hanumayamma Innovat & Technol Inc, 628 Crescent Terrace, Fremont, CA 94536 USA
[2] San Jose State Univ, Dept Comp Engn, San Jose, CA 95192 USA
关键词
Airborne diseases; IoT; healthcare-associated infections (HAIs); CEP; Internet Of Things; Venue Analytics; iOS; Android; Complex Event Processing; IoT reference architecture; Decision Tree; Machine Learning; Regression Analysis; Term Frequency and Inverse Document Frequency;
D O I
10.1109/BigDataService.2017.53
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With healthcare-associated infections (HAIs) in the U.S. accounting for an estimated 1.7 million infections and 99,000 deaths annually, reducing and preventing these infections is a top goal for healthcare facilities throughout the country [1]. Not only healthcare facilities, in other captive environments, for instance, ships and cruises, provide environment that may increase risk of infection. According to Minooee and Rickman [2], "Ships provide an isolated environment that may increase the passenger's risk of infection if exposed to respiratory viruses. High attack rates of influenza, for example, are typically seen in closed settings such as cruises, military vessels, aircraft, and institutions."
引用
收藏
页码:124 / 130
页数:7
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