Fuzzy Binary Rough Set

In this paper, we provide a definition of α-fuzzified lower and upper approximations for fuzzy sets based on the α-cut of fuzzy binary relations. We show that the definition is a proper generalization of the previous one for approximations of crisp sets and compare it with an existing definition in the context of fuzzy tolerance relation.

Possibilistic Residuated Implication Logics with Applications

In this paper, we will develop a class of logics for reasoning about qualitative and quantitative uncertainty. The semantics of the logics is uniformly based on possibility theory. Each logic in the class is parameterized by a t-norm operation on [0,1], and we express the degree of implication between the possibilities of two formulas explicitly by using residuated implication with respect to the t-norm. The logics are then shown to be applicable to possibilistic reasoning, approximate reasoning, and nonmonotonic reasoning.

Efficient simulation of the spatial transmission dynamics of influenza

Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we present a significant improvement to the efficiency of an individual-based stochastic disease simulation framework commonly used in multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations. The ability to perform efficient simulation allows us to run a batch of simulations and take account of their average in real time. The averaged data are stable and can be used to differentiate spreading patterns that are not readily seen by only conducting a few runs.

Efficient simulation of the spatial transmission dynamics of influenza

Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially-resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we describe a significant improvement in the efficiency of an individual-based stochastic disease simulation framework that has been used for multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations.

Medical privacy protection based on granular computing

Based on granular computing methodology, we propose two criteria to quantitatively measure privacy invasion. The total cost criterion measures the effort needed for a data recipient to find private information. The average benefit criterion measures the benefit a data recipient obtains when he received the released data. These two criteria remedy the inadequacy of the deterministic privacy formulation proposed in Proceedings of Asia Pacific Medical Informatics Conference, 2000; Int J Med Inform 2003;71:17-23. Granular computing methodology provides a unified framework for these quantitative measurements and previous bin size and logical approaches. These two new criteria are implemented in a prototype system Cellsecu 2.0. Preliminary system performance evaluation is conducted and reviewed.

Preserving confidentiality when sharing medical database with the Cellsecu system

We propose a computer system called Cellsecu that maintains the anonymity and the confidentiality of each cell containing sensitive information in medical database. Cellsecu attains this by automatically removing, generalizing, and expanding information. It is designed to enhance data privacy protection so a data warehouse can automatically handle queries. In most cases, health organizations collect medical data with explicit identifiers, such as name, address and phone number. Simply removing all explicit identifiers prior to release of the data is not enough to preserve the data confidentiality. Remaining data can be used to re-identify individuals by linking or matching the data to other database, or by looking at unique characteristics found in the database. A formal model based on Modal logic is the theoretical foundation of Cellsecu. As well, a new confidentiality criteria called "non-uniqueness" is defined and implemented. We believe modeling this problem formally can clarify the issue as well as clearly identify the boundary of current technology. Base on our preliminary performance evaluation, the confidentiality check module and the confidentiality enhancing module only slightly degrade system performance.