A new fuzzy set merging technique using inclusion-based fuzzy clustering

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets

Four Reasoning Models for C3 Metamodel

International audienceThe architecture is considered to be the driving aspect of the development process; it allows specifying which aspects and models in each level needed according to the software architecture design. Early Architecture Description Languages (ADLs), nearly exclusive, focus on structural abstraction hierarchy ignoring behavioural description hierarchy, conceptual hierarchy, and metamodeling hierarchy. In our approach these four hierarchies constitute views to appropriately “reason about” the architecture of a system described using our C3 metamodel. C3 is defined to be a minimal and complete architecture description language. In this paper we provide a set of mechanisms to deal with different levels of each type of hierarchy, also we introduce our proper structural definition for connector types used to instantiate any connexion elements deployed at the architectures and application levels

REPRESENTATION AND REASONING MODELS FOR C3 ARCHITECTURE DESCRIPTION LANGUAGE

International audienceComponent-based development is a proven approach to manage the complexity of software and its need for customization. At an architectural level, one describes the principal system components and their pathways of interaction. So, Architecture is considered to be the driving aspect of the development process; it allows specifying which aspects and models in each level needed according to the software architecture design. Early Architecture description languages (ADLs), nearly exclusive, focus on structural abstraction hierarchy ignoring behavioural description hierarchy, conceptual hierarchy, and metamodeling hierarchy. In this paper we focus on those four hierarchies which represent views to appropriately “reason about” software architectures described using our C3 metamodel which is a minimal and complete architecture description language. In this paper we provide a set of mechanisms to deal with different levels of each hierarchy, also we introduce our proper structural definition for connector's elements deployed in C3 Architectures

A comparative study of conversion aided methods for WordNet sentence textual similarity

In this paper, we present a comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion. We use WordNet ontology for determining word level semantic similarity while augmenting WordNet with two other lexicographical databases; namely Categorial Variation Database (CatVar) and Morphosemantic Database in assisting the word category conversion. Using a human annotated benchmark data set, all the three approaches achieved a high positive correlation reaching up to (r = 0.881647) with comparison to human ratings and two other baselines evaluated on the same benchmark data set

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

In this paper, we propose a hybrid approach for sentence paraphrase identification. The proposal addresses the problem of evaluating sentence-to-sentence semantic similarity when the sentences contain a set of named-entities. The essence of the proposal is to distinguish the computation of the semantic similarity of named-entity tokens from the rest of the sentence text. More specifically, this is based on the integration of word semantic similarity derived from WordNet taxonomic relations, and named-entity semantic relatedness inferred from Wikipedia entity co-occurrences and underpinned by Normalized Google Distance. In addition, the WordNet similarity measure is enriched with word part-of-speech (PoS) conversion aided with a Categorial Variation database (CatVar), which enhances the lexico-semantics of words. We validated our hybrid approach using two different datasets; Microsoft Research Paraphrase Corpus (MSRPC) and TREC-9 Question Variants. In our empirical evaluation, we showed that our system outperforms baselines and most of the related state-of-the-art systems for paraphrase detection. We also conducted a misidentification analysis to disclose the primary sources of our system errors

Cancer prediction using graph-based gene selection and explainable classifier

Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is developed for gene selection and cancer prediction. Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction. In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent and explainable decision forest model. The performance of the developed approach is compared to three state-of-the-art cancer prediction including TAGA, HPSO and LL. The reported results on five cancer datasets indicate that the developed model can improve the accuracy of cancer prediction and reduce the execution time