Journal of Intelligent & Fuzzy Systems - Volume 32, issue 3

Authors: Naz, Sumera | Rashmanlou, Hossein | Malik, M. Aslam

Article Type: Research Article

Abstract: The concepts of graph theory are applied in many areas of computer science including image segmentation, data mining, clustering, image capturing and networking. Fuzzy graph theory is successfully used in many problems, to handle the uncertainty that occurs in graph theory. A single valued neutrosophic graph (SVNG) is an instance of a neutrosophic graph and a generalization of the fuzzy graph, intuitionistic fuzzy graph, and interval-valued intuitionistic fuzzy graph. In this paper, the basic operations on SVNGs such as direct product, Cartesian product, semi-strong product, strong product, lexicographic product, union, ring sum and join are defined. Moreover, the degree of …a vertex in SVNGs formed by these operations in terms of the degree of vertices in the given SVNGs in some particular cases are determined. Finally, an application of single valued neutrosophic digraph (SVNDG) in traval time is provided. Show more

Keywords: Neutrosophic graph, direct product, Cartesian product, semi-strong product, strong product, lexicographic product, degree of a vertex

DOI: 10.3233/JIFS-161944

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2137-2151, 2017

Authors: Faizi, Shahzad | Rashid, Tabasam | Zafar, Sohail

Article Type: Research Article

Abstract: This article proposes an outranking method for group decision-making (GDM) using hesitant intuitionistic fuzzy linguistic term sets (HIFLTSs). By means of HIFLTSs, the flexibility in generating evaluation information under uncertainty can be achieved to a larger extent than intuitionistic fuzzy sets (IFSs) or hesitant fuzzy linguistic term sets (HFLSs). Based on intuitionistic fuzzy support function (IFSF), intuitionistic fuzzy risk function (IFRF) and intuitionistic fuzzy credibility function (IFCF), the net outranking flow index (NOFI) of each alternative are calculated which represents the net outranking character of an alternative over the other. The linguistic scale functions (LSFs) are employed in this paper …to conduct the transformation between qualitative information and quantitative data. Finally, an outranking approach is constructed for ranking alternatives in multi-criteria group decision-making (MCGDM) problems, and the approach is demonstrated using a numerical example. Show more

Keywords: Linguistic decision-making, hesitant fuzzy linguistic term sets (HFLTSs), hesitant intuitionistic fuzzy linguistic term sets (HIFLTSs), linguistic scale function(LSF), multi-criteria decision-making (MCDM), outranking approach

DOI: 10.3233/JIFS-161976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2153-2164, 2017

Authors: Vidhya, K.A. | Geetha, T.V.

Article Type: Research Article

Abstract: Rough set theory is a mathematical framework that can be visualized as a soft computing tool dealing with the vagueness and uncertainty of data and is applied to pattern recognition, data mining, and knowledge discovery. Document clustering is another area of research with values which are a bag of words that describe contents within clusters. This work analyzes how rough set theory is used for document clustering to fix issues that clustering methods manage. In this survey, an exhaustive literature review of the concept of rough sets, as well as how the lower and upper approximation of a set can …be used for document clustering, has been presented. Rough set clusters are shown to be useful for representing real-time applications such as biomedical inferences, network data handling, and citation analysis. The survey is done in phases, showing how machine learning algorithms have been incorporated for document clustering using rough set theory, as well as how rough set theory has been extended to adapt to document clustering with feature selection techniques and feature/dimensionality reduction and, finally, ending with a view of assorted clustering tasks where rough set theory is applied. The classification of rough set theory for document clustering is depicted and its applications presented in this paper. The rough set theory works with resolving ambiguity and uncertainty in data. To the best of our knowledge, a rough set clustering survey has not been done earlier in the literature reviewed and the survey ends with a critical analysis of rough set theory in each application of clustering. Show more

Keywords: Rough set theory, document clustering, machine learning, approximation space

DOI: 10.3233/JIFS-162006

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2165-2185, 2017

Authors: Sheng, Yuhong | Shi, Gang | Cui, Qing

Article Type: Research Article

Abstract: In many cases, the uncertain factor influencing dynamic systems is not single. Multifactor uncertain differential equation is a type of differential equation driven by the multiple Liu processes. Some concepts of stability in measure and stability in mean for multifactor uncertain differential equations have been proposed. This paper focuses on presenting a concept of the almost sure stability of multifactor uncertain differential equation. A sufficient condition for a multifactor uncertain differential equation being almost surely stable are provided. In addition, this paper discusses some examples to illustrate the theoretical considerations.

Keywords: Uncertainty theory, multifactor uncertain differential equation, almost sure stability

DOI: 10.3233/JIFS-162024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2187-2194, 2017

Authors: Xu, Chao | Meng, Fanyong | Zhang, Qiang

Article Type: Research Article

Abstract: Considering the fact that the players may have different risk attitudes for the approximate values of strategies, this paper uses the possibility and necessity measures to denote the risk attitudes of the players, which are useful tools to address this issue. To do this, the concepts of possibility and necessity expectations of fuzzy variables are introduced, and several desirable properties are studied. Then, two vectors to denote the players’ risk attitudes for their strategies are presented. Furthermore, the concept of possibility and necessity (PN) equilibrium strategy is defined, and the associated models are constructed, by which the optimal possibility and …necessity equilibrium strategy is obtained. Meanwhile, a special case is briefly considered. Finally, a numerical example is offered to show the equilibrium strategy of the players. Show more

Keywords: Matrix game, equilibrium strategy, possibility measure, necessity measure, trapezoidal fuzzy number

DOI: 10.3233/JIFS-16229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2195-2206, 2017

Authors: Zhao, Tong | Liu, Changyou | Yetilmezsoy, Kaan | Gong, Peilin | Li, Jianwei

Article Type: Research Article

Abstract: The hydraulic support is the key equipment of the residual coal stoping working face, which provides the safe working space and the wind circulation channel for the remining. Scientific mining of residual coal is on the basis of the optimization of hydraulic support structure and working resistance. The Hydraulic Support Optimization (HSO) is mainly influenced by geological factors, including seam thickness, dip angle, roof rock fracture structure, especially the Roof Caving Zone (RCZ) distribution. The contribution of this paper lies in HSO of 3101 residual coal sublevel caving face at Shenghua Mine, with the combination of physical simulations, theoretical analysis, …and field measurements. Influenced by forward RCZ and sudden instability of coal pillars, the main roof across the caving areas fractured and caused rotary instability towards the goaf. The hinged roof structure of “long key blocks across the caving areas” was thus formed, which resulted in the mutagenic increase of support working resistance. Reasonable working resistance and structure of the support under such conditions were obtained, and the conduction of pre-grouting solidified the roof in the caving area and reinforced the coal pillars, thus enhancing the bearing capacity. Show more

Keywords: Mechanized remining, rock structure, hydraulic support, optimization

DOI: 10.3233/JIFS-162311

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2207-2219, 2017

Authors: Shi, Lukui | Hao, Jiasi | Zhang, Xin

Article Type: Research Article

Abstract: In image recognition, the within-class matrix in some multi-manifold learning algorithms is singular, which affects the recognition effectiveness. To solve the problem, a supervised multi-manifold learning method is proposed, which extracts multi-manifold features of images by maximizing the between-class Laplacian graph and hides the minimization of the within-class Laplacian graph in the maximization of the between-class Laplacian graph by introducing the class labels. This method provides an explicit mapping between the high dimensional images and the low dimensional features, which can project samples out of the training set into the low dimensional space and also overcomes the singular problem of …the within-class matrix. The proposed algorithm is tested on the pavement distress images, ORL and FERET face images. Experiments show that the recognition accuracy is greatly improved, and the dimension of the low dimensional features is determined. And the influence of Euclidean distance and the angle cosine distance on the recognition results is compared by using KNN. Show more

Keywords: Multi-manifold, discriminant analysis, image recognition, Laplacian graph, singular matrix

DOI: 10.3233/JIFS-16232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2221-2232, 2017

Authors: Makui, Ahmad | Moeinzadeh, Pooria | Bagherpour, Morteza

Article Type: Research Article

Abstract: Recent advances in technology and fundamental changes in most scientific areas have affected projects and made their nature and environmental circumstances much more complex than in the past. In such conditions, the customary principles and practices of project management are not anymore able to handle the emerging complexities of projects. Fortunately, in recent years, researchers and practitioners have recognized the importance of complexity and tried to identify the various aspects of project complexity and provide appropriate solutions to deal with them. One of the main steps to manage system complexity is to evaluate and measure it. Because of the ambiguity …and uncertainty of complexity context and the difficulty of its exact quantification based on available information, the application of fuzziness could be very appropriate. Hence, in this paper we tried to design and implement an inference system in fuzzy environment to evaluate project complexity. Also, because of the importance of construction projects, we particularly study this kind of projects. Finally, it should be noted that system complexity can exist in two forms: static and dynamic. Therefore, considering the breadth of issues related to each of these two complexity areas, just the static complexity of construction projects has been studied here. Show more

Keywords: Project management, construction industry, static complexity, evaluation, fuzzy set theory, inference system

DOI: 10.3233/JIFS-16234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2233-2249, 2017

Authors: Kuo, R.J. | Lin, L. | Zulvia, F.E. | Lin, C.C.

Article Type: Research Article

Abstract: This paper proposes a particle swarm K -means optimization (PSKO)-based granular computing (GrC) model to preprocess skewed class distribution in order to enhance the classification accuracy for the class imbalance problem. The GrC model obtains knowledge from information granules rather than from numerical data. It also processes multi-dimensional and sparse data by using singular value decomposition and latent semantic indexing (LSI). The data possessing features of multiple dimensions and scarcity can be preprocessed using LSI in order to reduce the number of data dimensions as well as records. Ten benchmark data sets are employed to demonstrate the effectiveness of the …proposed model. Experiment results indicate that the proposed model has better classification performance with both imbalanced and balanced data. In addition, the computational result for prostate cancer prognosis reveals that the proposed model really can support physicians in judging the condition of prostate cancer patients with a more accurate survival rate estimation. Show more

Keywords: Prostate cancer, granular computing, particle swarm K-means optimization, class imbalance, classification

DOI: 10.3233/JIFS-16236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2251-2267, 2017

Authors: Anuradha, R. | Rajkumar, N.

Article Type: Research Article

Abstract: Machine-learning and data-mining techniques have been developed to turn data into useful task-oriented knowledge. The algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level or multiple levels. Mining associations among itemsets only by using support and confidence thresholds at different levels of hierarchical data would not give interesting rules both for binary or quantitative data. This paper proposes a two phase algorithm that mines rare generalized fuzzy coherent rules at inter-cross level hierarchies. During phase-I both positive and negative fuzzy coherent rules are mined and in Phase-II, rare generalized fuzzy …coherent rules are extracted from the resultant rules obtained from Phase-I. The algorithm framework works on top down methodology in generating positive and negative fuzzy coherent rules and mining rare generalized rules from it. Experiments conducted using synthetic dataset show the performance of the proposed algorithm in terms of the number of rare generalized rules generated, compared to fuzzy multiple-level association rule mining algorithm. Show more

Keywords: Fuzzy association rule, fuzzy coherent rule, rare generalized coherent rule, membership function, taxonomies

DOI: 10.3233/JIFS-16240

Citation: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, pp. 2269-2280, 2017