Fuzzy Control
Understanding and Using Context
Personal and Ubiquitous Computing
a CAPpella: programming by demonstration of context-aware applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Enhancing Situation-Aware Systems through Imprecise Reasoning
IEEE Transactions on Mobile Computing
Using fuzzy decision tree to handle uncertainty in context deduction
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
A framework for fuzzy recognition technology
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Control and learning of ambience by an intelligent building
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Context-Dependent Algorithm for Merging Uncertain Information in Possibility Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
International Journal of Metadata, Semantics and Ontologies
Situation recognition in sensor based environments using concept lattices
Proceedings of the CUBE International Information Technology Conference
Multivariate context collection in mobile sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
| Hi-index | 0.00 |
Context-aware computing relies on sensing environmental parameters--context (e.g., illumination, location), classifying context, and inferring further knowledge about context, i.e., the user's situation. Therefore, the relevant applications cannot handle context as flexibly as their users would expect. To overcome this deficiency, we propose an extension of context representation, classification, and inference. Our model relies on fuzzy-set theory to accommodate the imperfect nature of sensed context. We develop two fuzzy inference engines dealing with context specialization and compatibility relations. We evaluate such engines through a series of experiments involving real users. Our findings indicate the efficiency of the proposed context classification and inference processes.