2017
DOI: 10.3390/s17102433
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mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification

Abstract: The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demon… Show more

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Cited by 11 publications
(4 citation statements)
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References 38 publications
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“…Scholars have long pondered the nature of self-awareness [25][26][27] and its neural basis in stochastic, non-deterministic approaches [28]. The emerging research in self-awareness of contexts includes using cross-domain computing environments to automatically identify the context of the user [29]. The environmental impacts on driving styles are predicted using new methods and models [30].…”
Section: The Literature Reviewmentioning
confidence: 99%
“…Scholars have long pondered the nature of self-awareness [25][26][27] and its neural basis in stochastic, non-deterministic approaches [28]. The emerging research in self-awareness of contexts includes using cross-domain computing environments to automatically identify the context of the user [29]. The environmental impacts on driving styles are predicted using new methods and models [30].…”
Section: The Literature Reviewmentioning
confidence: 99%
“…The Data Curation Layer (DCL) manages the data in raw as well as processed format through data acquisition and synchronization, life-log representation and mapping, lifelog monitoring and big data storage processes [67]. Information Curation Layer (ICL) identifies end-user's activities and context from multimodal sensory data managed in hierarchical models [83]. It employs emotion, location, and multiple activity recognizers, respectively.…”
Section: A Mining Minds In a Nut Shellmentioning
confidence: 99%
“…For this, we used a set of 9 SPARQL-based query templates for retrieving and interpreting rules to deal with underlying temporal sensor state relations, as well as their structural properties. Moreover, the SPARQL queries require additional parameters in order to correlate, interpret, and aggregate sensor states within the endpoints of the sliding window [25]. Some of the initializing parameters include start-time, end-time, and a list of sensors within the sliding window identified based on the start-time and datatype properties.…”
Section: Semantic Segmentationmentioning
confidence: 99%