作者: Arun Sai Krishnan , Xiping Hu , Jun-qi Deng , Li Zhou , Edith C.-H. Ngai
关键词: Process (engineering) 、 Cloud computing 、 Key (music) 、 Music mood 、 Context based 、 Engineering 、 Recommender system 、 Risk analysis (engineering) 、 Multimedia 、 SAFER 、 Delivery system
摘要: Road safety is a huge concern due to the large number of fatalities and injuries caused by road accidents. Research has shown that fatigue can adversely affect driving performance increase risk It been enhanced stress-relieving music which thereby promotes safer driving. Context-aware delivery systems promote through intelligent recommendations based on contextual knowledge. Two key aspects situation-aware are effectiveness efficiency recommendation. Efficiency critical aspect in real-time context recommendation as system should quickly sense any change situation deliver suitable before sensed context-data becomes obsolete. We focus this paper. Music mood-mapping process helps understanding mood song hence used systems. This requires processing time complex calculations sizes files involved. Hence, optimizing improving context-aware Here, we propose novel cloud crowd-sensing approach considerably optimize