26,781 research outputs found
A Neutrosophic Description Logic
Description Logics (DLs) are appropriate, widely used, logics for managing
structured knowledge. They allow reasoning about individuals and concepts, i.e.
set of individuals with common properties. Typically, DLs are limited to
dealing with crisp, well defined concepts. That is, concepts for which the
problem whether an individual is an instance of it is yes/no question. More
often than not, the concepts encountered in the real world do not have a
precisely defined criteria of membership: we may say that an individual is an
instance of a concept only to a certain degree, depending on the individual's
properties. The DLs that deal with such fuzzy concepts are called fuzzy DLs. In
order to deal with fuzzy, incomplete, indeterminate and inconsistent concepts,
we need to extend the fuzzy DLs, combining the neutrosophic logic with a
classical DL. In particular, concepts become neutrosophic (here neutrosophic
means fuzzy, incomplete, indeterminate, and inconsistent), thus reasoning about
neutrosophic concepts is supported. We'll define its syntax, its semantics, and
describe its properties.Comment: 18 pages. Presented at the IEEE International Conference on Granular
Computing, Georgia State University, Atlanta, USA, May 200
Visualization of protein-protein interaction network for knowledge discovery
Paper presented at the 2006 IEEE International Conference on Granular Computing, Atlanta, GA.A new visualization tool, called "Visual Concept
Explorer (VCE)", was developed to visualize concept relationships
in bio-medical literatura VCE integrates Pathfinder Network
Scaling and Kohonen Self-organizing Feature Map Algorithm for
visual mapping. As a case study, VCE was applied to visualize a
chromatin protein-protein interaction (PPI) network The mapping
results demonstrated that VCE could explore the semantic structure
and latent domain knowledge hidden in protein-protein interaction
data sets generatedfrom bio-medical literature
Information Flow Model for Commercial Security
Information flow in Discretionary Access Control (DAC) is a well-known difficult problem. This paper formalizes the fundamental concepts and establishes a theory of information flow security. A DAC system is information flow secure (IFS), if any data never flows into the hands of owner’s enemies (explicitly denial access list.
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
We evaluate a version of the recently-proposed classification system named
Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space
of sequences of generic objects. The ODSE system has been originally presented
as a classification system for patterns represented as labeled graphs. However,
since ODSE is founded on the dissimilarity space representation of the input
data, the classifier can be easily adapted to any input domain where it is
possible to define a meaningful dissimilarity measure. Here we demonstrate the
effectiveness of the ODSE classifier for sequences by considering an
application dealing with the recognition of the solubility degree of the
Escherichia coli proteome. Solubility, or analogously aggregation propensity,
is an important property of protein molecules, which is intimately related to
the mechanisms underlying the chemico-physical process of folding. Each protein
of our dataset is initially associated with a solubility degree and it is
represented as a sequence of symbols, denoting the 20 amino acid residues. The
herein obtained computational results, which we stress that have been achieved
with no context-dependent tuning of the ODSE system, confirm the validity and
generality of the ODSE-based approach for structured data classification.Comment: 10 pages, 49 reference
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
The data mining along with emerging computing techniques have astonishingly
influenced the healthcare industry. Researchers have used different Data Mining
and Internet of Things (IoT) for enrooting a programmed solution for diabetes
and heart patients. However, still, more advanced and united solution is needed
that can offer a therapeutic opinion to individual diabetic and cardio
patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced
healthcare system for proficient diabetes and cardiovascular diseases have been
proposed. The hybridization of data mining and IoT with other emerging
computing techniques is supposed to give an effective and economical solution
to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining,
Internet of Things, chatbots, contextual entity search (CES), bio-sensors,
semantic analysis and granular computing (GC). The bio-sensors of the proposed
system assist in getting the current and precise status of the concerned
patients so that in case of an emergency, the needful medical assistance can be
provided. The novelty lies in the hybrid framework and the adequate support of
chatbots, granular computing, context entity search and semantic analysis. The
practical implementation of this system is very challenging and costly.
However, it appears to be more operative and economical solution for diabetes
and cardio patients.Comment: 11 PAGE
Interpretations of Association Rules by Granular Computing
We present interpretations for association rules. We first introduce Pawlak's method, and the corresponding algorithm of finding decision rules (a kind of association rules). We then use extended random sets to present a new algorithm of finding interesting rules. We prove that the new algorithm is faster than Pawlak's algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. We also provide two measures for dealing with uncertainties in association rules
Mining top-k granular association rules for recommendation
Recommender systems are important for e-commerce companies as well as
researchers. Recently, granular association rules have been proposed for
cold-start recommendation. However, existing approaches reserve only globally
strong rules; therefore some users may receive no recommendation at all. In
this paper, we propose to mine the top-k granular association rules for each
user. First we define three measures of granular association rules. These are
the source coverage which measures the user granule size, the target coverage
which measures the item granule size, and the confidence which measures the
strength of the association. With the confidence measure, rules can be ranked
according to their strength. Then we propose algorithms for training the
recommender and suggesting items to each user. Experimental are undertaken on a
publicly available data set MovieLens. Results indicate that the appropriate
setting of granule can avoid over-fitting and at the same time, help obtaining
high recommending accuracy.Comment: 12 pages, 5 figures, submitted to Advances in Granular Computing and
Advances in Rough Sets, 2013. arXiv admin note: substantial text overlap with
arXiv:1305.137
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