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It is becoming harder to find an app on one's smart phone due to the increasing number of apps available and installed on smart phones today. We collect sensory data including app use from smart phones, to perform a comprehensive analysis of the context related to mobile app use, and build prediction models that calculate the probability of an app in the(More)
Fingerprint classification reduces the number of possible matches in automated fingerprint identification systems by categorizing fingerprints into predefined classes. Support vector machines (SVMs) are widely used in pattern classification and have produced high accuracy when performing fingerprint classification. In order to effectively apply SVMs to(More)
As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to user's physical location. As increasing information and services, it becomes difficult to find a proper service that reflects the individual preference at proper time. Due to the small screen(More)
Much research in ubiquitous computing assumes that a user's phone will be always on and at-hand, for collecting user context and for communicating with a user. Previous work with the previous generation of mobile phones has shown that such an assumption is false. Here, we investigate whether this assumption about users' proximity to their mobile phones(More)
Lymphoma cancer classification with DNA microarray data is one of important problems in bioinformatics. Many machine learning techniques have been applied to the problem and produced valuable results. However the medical field requires not only a high-accuracy classifier, but also the in-depth analysis and understanding of classification rules obtained.(More)
OBJECT The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity(More)
With the growth on the concern about context-aware applications, it becomes important to recognize and share user context. Even though there are some applications, it is still limited in managing simple contexts. In this paper, we propose a context-aware messenger application that exploits dynamic Bayes-ian networks to automatically infer a user's context(More)