Session Program

Abstract - User preference discovery aims to detect the patterns of user preferences for various topics of interest or items such as movie genre or category. Preferences discovery is a crucial stage in the development of intelligent personalization systems. Although a variety of studies have been proposed in the literature addressing a wide range of applications such as recommender systems or personalized search, only a few of them have considered the management of imprecision in the representation of user and item features. This paper aims to address the above issue by using fuzzy sets. The paper proposes a general framework for preferences discovery through fuzzy sets and fuzzy models and it introduces a new algorithm for representing and discovering fuzzy user interest profile. Based on the results of the empirical evaluation, the proposed approach outperforms two well-known recommendation approaches in terms of well-known quality assessment metrics, namely: discounted cumulative gain, precision, recall, as well as F1-measure.

Abstract - In recent years, a plethora of different studies for design of traditional ensemble classifiers has been proposed in order to improve final recognition accuracy. However, among the ensemble classifiers, combination methods are focused on build- ing independent classifiers of the same or different algorithms using majority voting methods. In this paper, we present a new fusion scheme for ensemble classifiers based on a new concept called Generalized Fuzzy Soft Set (GFSS), which we apply in activity classification. Essentially, we apply a weighted aggregate operator to the output of each classifier in order to fuse the GFSS into a more reliable classifier. The proposed fusion method is based on a new ranking algorithm to classify activities. We show that the proposed method produces more accurate results than the best single classifier and its effectiveness is demonstrated by comparing it with single classifier in terms of activity recognition accuracy.

Abstract - Fuzzy discrete event systems are useful models for solving complex practical problems in biomedical and other fields. The theory of detectabilities in conventional crisp discrete event systems investigates state determination or estimation based on event observation. The theory is also important to fuzzy discrete event systems (e.g., disease diagnosis and treatment effectiveness evaluation). In this paper, we investigate fuzzy detectabilities for fuzzy discrete event systems. We first introduce fuzzy discrete event systems with constraints, which provide a new and more realistic model for complex systems. We also extend detectabilities of crisp discrete event systems to N-detectabilities and prove the relation between detectabilities and N-detectabilities. We then define fuzzy N- detectabilities and develop an algorithm to check fuzzy N-detectabilities of a fuzzy discrete event system. The computational complexity of the algorithm is analyzed. To model a fuzzy discrete event systems with constraints, we propose to use a (crisp) automaton to model the constraints in addition to a fuzzy automaton. This new model significantly enhances the modeling power of fuzzy discrete event systems and contains the previous model as a special case. In biomedical applications such as disease diagnosis and treatment, it is often important to know a patient's condition. Therefore, state estimation and detectabilities are important in such applications and we need to investigate fuzzy detectabilities of fuzzy discrete event systems.

Abstract - In this paper, we propose a neutrosophic recommender system for medical diagnosis using both neutrosophic similarity measure and neutrosophic clustering to capture the treatment of similar patients at a different levels within a concurrent group. The proposed algorithm allows similar patients being treated concurrently in a group. Firstly, the similarities are measured based on the algebraic operations and their theoretic properties. Secondly, a clustering algorithm is used to identify neighbors that are in the same cluster and share common characteristics. Then, a prediction formula using results of both the clustering algorithm and the similarity measures is designed. Experiment indicates the advantages and superiority of the proposal.

Abstract - Many real-world applications require ontology alignment to integrate semantic information from different sources. However, most of the work in the field is restricted to finding synonymy relationships, and hyponymy relationships have not received similar attention. In this paper, we discuss some extensions of our previous work in the discovery of subsumption relationships with fuzzy clustering, aggregation operators, Formal Concept Analysis, and synonymy relationships.

Abstract - In the automated search system, similarity is a key concept for solving the human task. The human process is a natural categorization, which underlies many natural abilities such as image recovery, language comprehension, decision making or pattern recognition. In this paper, the focus is on the use of similarities in image retrieval search using near sets of similarity approaches. The results showed that a general framework for Near set is compatible with these foundations, and that similarity measurements can be involved in all steps of the image research process. We therefore focus on the fuzzy logic which provides interesting tools for data mining mainly because of its ability to represent imperfect information. We then introduce a new category of a fuzzy set : the Beta function. We finally illustrate our work with examples of similarities used in the real world of image retrieval problems.