Journal of Intelligent & Fuzzy Systems - Volume 23, issue 2-3

Authors: Abraham, Ajith

Article Type: Other

Abstract: Nature inspired computation is a general term referring to computing inspired by nature. It is an emerging interdisciplinary area and so far a range of techniques and methods are studied for dealing with large, complex, and dynamic problems. The idea is to mimic (concepts, principles and mechanisms) the complex phenomena occurring in the nature as computational processes in order to enhance the way computation is performed mainly from a problem solving point of view. Some of the key paradigms falling under this umbrella are neurocomputing, evolutionary computing, swarm intelligence, membrane computing, artificial immune systems, DNA computation, artificial life and so …on. The recent trend is to formulate adaptive nature inspired computational models combining different knowledge representation schemes, decision making models and learning strategies to solve a computational task. This integration aims at overcoming limitations of individual techniques through hybridization or fusion of various techniques. Show more

DOI: 10.3233/IFS-2012-0499

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 2-3, pp. 41-42, 2012

Authors: Chaira, Tamalika

Article Type: Research Article

Abstract: This paper provides a color cell image clustering algorithm using intuitionistic fuzzy set theory using different color models. The clustering algorithm clusters the blood cells very clearly that helps in detecting various types of human diseases. Clustering of medical images is a challenging task as medical images are vague in nature due to poor illumination. So the boundaries or regions are not clear. Clustering using fuzzy set theory is very robust but still there is some uncertainty present while defining the membership function in fuzzy set theory. This uncertainty is due the lack of knowledge or personal error while defining …the membership function. Intuitionistic fuzzy set takes into account this uncertainty and thus it may be useful in medical or real time image processing. The two uncertainty parameters in intuitionistic fuzzy set thus help in converging the cluster center to a desirable location than the cluster centers obtained by fuzzy C means algorithm. Different color models e.g., RGB, HSV, and CIELab are used in this algorithm and it is found that RGB and CIELab give almost similar result. The algorithm is also tested on conventional fuzzy C means algorithm to show the efficacy of the new algorithm. Show more

Keywords: Intuitionistic fuzzy set, fuzzy clustering, Yager generator, fuzzy complement, hesitation degree

DOI: 10.3233/IFS-2012-0494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 2-3, pp. 43-51, 2012

Authors: Wang, Yuxin | Guo, He | Liu, Hongbo | Abraham, Ajith

Article Type: Research Article

Abstract: Mining design patterns from source code is significant for improving the intelligibility and maintainability of software. In this paper, we present a new design pattern matching method based on fuzzy, in which matrix model is used for describing both design pattern and source code, and design pattern's static and dynamic information is defined as fuzzy attribute value for measuring the matching degree. Experiments on three open-source projects demonstrate the accuracy and efficiency of the proposed methodology.

Keywords: Design pattern, fuzzy matching, pattern mining, matrix model

DOI: 10.3233/IFS-2012-0495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 2-3, pp. 53-60, 2012

Authors: Weiguo, Yi | Mingyu, Lu | Zhi, Liu

Article Type: Research Article

Abstract: This paper analyzes the existing decision tree classification algorithms and finds that these algorithms based on variable precision rough set (VPRS) have better classification accuracy and can tolerate the noise data. But when constructing decision tree based on variable precision rough set, these algorithms have the following shortcomings: the choice of attribute is difficult and the decision tree classification accuracy is not high. Therefore, this paper proposes a new variable precision rough set based decision tree algorithm (IVPRSDT). This algorithm uses a new standard of attribute selection which considers comprehensively the classification accuracy and number of attribute values, that is, …weighted roughness and complexity. At the same time support and confidence are introduced in the conditions of the corresponding node to stop splitting, and they can improve the algorithm's generalization ability. To reduce the impact of noise data and missing values, IVPRSDT uses the label predicted method based on match. The comparing experiments on twelve different data sets from the UCI Machine Learning Repository show that IVPRSDT can effectively improve the classification accuracy. Show more

Keywords: Decision tree, variable precision rough set, weighted roughness, complexity, match

DOI: 10.3233/IFS-2012-0496

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 2-3, pp. 61-70, 2012

Authors: Tay, Kai Meng | Jee, Tze Ling | Lim, Chee Peng

Article Type: Research Article

Abstract: In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. …It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base. Show more

Keywords: Fuzzy inference system, similarity reasoning, analogical reasoning, fuzzy rule interpolation, non-linear programming, monotonicity property, failure mode and effect analysis

DOI: 10.3233/IFS-2012-0497

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 2-3, pp. 71-92, 2012

Authors: Vinotha, J. Merline | Ritha, W. | Abraham, Ajith

Article Type: Research Article

Abstract: This paper proposes a procedure for solving total time minimization in fuzzy transportation problem where the transportation time, source and destination parameters have been expressed as exponential fuzzy numbers by the decision maker. An algorithm is developed to obtain the optimal solution as exponential fuzzy number, which enables the decision maker to obtain more informing results and wider knowledge on the problem under consideration. A numerical example is solved to check the validity of the proposed procedure.

Keywords: Total time minimization, Fuzzy transportation problem, Fuzzy number, Exponential membership function, Graded mean integration representation

DOI: 10.3233/IFS-2012-0498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 23, no. 2-3, pp. 93-99, 2012