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This paper proposes a new method for partitioning data clustering using PSO. The Proposed methods LPSOC designed for hard clusters. LPSOC alleviate some of the drawbacks of traditional algorithms and the state-of-the-art PSO clustering algorithm. Population-based algorithms such as PSO is less sensitive to initial condition than other algorithms such as K-means since search starts from multiple positions...
In this paper we apply particle swarm optimization (PSO) feature selection to enhance Hidden Markov Model (HMM) states and parameters for face recognition systems. Ideal Feature selection for face images based on the idea of collaborative behavior of bird flocking to reduce the feature size and hence recognition time complicity. The framework has been inspected on 400 face pictures of the Olivetti...
The main contribution of this paper is to present a survey of different approaches and techniques that map fuzzy XML schemas to fuzzy relational databases or fuzzy object oriented databases. Moreover, it presents different fuzzy models and XML data models. In addition, the integration process of fuzzy techniques in different databases has been discussed among several categories of data modeling and...
The autism diagnostic interview-revised (ADI-R) is a semi-structured interview designed to assess the three core aspects of autism spectrum disorder (ASD). In this research a synthetic minority over-sampling technique (SMOT) was presented for handling autism imbalanced data to increase accuracy credibility. SMOT can potentially lead to over fitting on multiple copies of minority class examples. The...
Multi Criteria Decision-Making (MCDM) methods mainly help finding the most desirable alternatives from a set of available alternatives versus the selected criteria. Fuzzy Decision Maps (FDM) method has been proposed in 2006 for handling MCDM problems with dependency and feedback to overcome the complexity drawback of Analytic Network Process (ANP). However, the structure of FDM method was designed...
This paper provides a fuzzy cognitive map (FCM) model that can handles linguistic values. A mathematical description of FCM is presented and a new methodology by using linguistic values is proposed for more appropriate and humanistic way to deal with information uncertainty. An illustrative case study ensures the validity of the proposed model for real world uncertain problems.
In this paper, we improve the Fuzzy Decision Map (FDM) by using linguistic values rather than crisp membership values for the link weights (i.e. preference and causal relationships among criteria with fuzzy linguistic). The proposed method is called the linguistic fuzzy decision network. It provides both local fuzzy weights and global fuzzy weights. The proposed method is quite appropriate to decision...
Shortest path problem got a lot of attention from many researchers, in our case the distances between the nodes are represented by different types of uncertain numbers such as: interval numbers, fuzzy numbers, rough numbers and also some of them could be represented by classical real numbers. These heterogeneous types of numbers are forming a challenge in calculation the shortest path. In this work...
Analytical Hierarchical Process (AHP) is one of the famous methods of solving multi criteria decision making problems, in some cases the preference ratios can be represented by different types of uncertain numbers such as: interval numbers, fuzzy numbers and rough numbers. These heterogeneous types of numbers are forming a challenge in computing and choosing the best alternative. This work proposes...
This paper presents a new extension to Fuzzy Decision Maps (FDMs) by allowing use of fuzzy linguistic values to represent relative importance among criteria in the preference matrix as well as representing relative influence among criteria for computing the steady-state matrix in the stage of Fuzzy Cognitive Map (FCM). The proposed model is called the Linguistic Fuzzy Decision Networks (LFDNs). The...
Particle Swarm Optimization (PSO) is an efficient, simple and fertile Optimization Algorithm. However, it suffers from premature convergence; moreover, the performance of PSO depends significantly on its parameters settings. PSO attracts attention from researchers; they try to improve algorithm performance and avoid its weakness. In this paper, we propose a new methodology that uses chaotic agents...
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