Journal of Intelligent & Fuzzy Systems - Volume 27, issue 5

Authors: Ju, Yanbing | Zhang, Wenkai | Yang, Shanghong

Article Type: Research Article

Abstract: In this paper, we extend the Hamacher operations to aggregate the dual hesitant fuzzy elements (DHFEs). Firstly, operational rules of DHFEs based on Hamacher t-norm and t-conorm are proposed. Then, we develop some dual hesitant fuzzy Hamacher aggregation operators based on the operational rules of DHFEs, such as dual hesitant fuzzy Hamacher weighted averaging (DHFHWA) operator, dual hesitant fuzzy Hamacher weighted geometric (DHFHWG) operator, dual hesitant fuzzy Hamacher ordered weighted averaging (DHFHOWA) operator, dual hesitant fuzzy Hamacher ordered weighted geometric (DHFHOWG) operator, dual hesitant fuzzy Hamacher hybrid averaging (DHFHHA) operator and dual hesitant fuzzy Hamacher hybrid geometric (DHFHHG) operator are …proposed. Some desirable properties of these operators such as idempotency and boundedness are discussed, and some special cases of these operators are analyzed. Furthermore, a method to multiple attribute decision making (MADM) based on the proposed operators is developed. Finally, a practical example is given to illustrate the developed method and a comparison analysis is also conducted, which further demonstrates the practicality and effectiveness of the proposed approach. Show more

Keywords: Hamacher aggregation operators, dual hesitant fuzzy Hamacher aggregation operators, dual hesitant fuzzy set (DHFS), multiple attribute decision making (MADM)

DOI: 10.3233/IFS-141222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2481-2495, 2014

Authors: Naseem, Muhammad Tahir | Qureshi, Ijaz Mansoor | Atta-ur-Rahman, | Muzaffar, Muhammad Zeeshan

Article Type: Research Article

Abstract: Digital image watermarking is one of the prime areas of research in the field information security and data authentication. There exist different methods to embed watermark information in the image. Image imperceptibility is a factor that limits the amount of information being embedded in the image. There is as such no closed form formula or expression in the literature that could relate image imperceptibility and capacity of watermark information. In this paper, a novel technique is proposed in which a second order fuzzy rule based system (SOFRBS) is designed to maximize the capacity of image based characteristics of upon human …visual system (HVS) and desired peak signal to noise ratio (PSNR) which is coined as imperceptibility factor (IF). First order fuzzy rule based system (FOFRBS) calculates the capacity factor, alpha by taking the brightness, edge and texture sensitivity as input, while second order fuzzy rule based system (SOFRBS) calculates the capacity by taking alpha and IF as input. Moreover, the proposed scheme is also robust against JPEG compression attack. The authenticity of the proposed scheme is validated through simulation of different types of images like natural and medical images. Show more

Keywords: Watermarking, the human visual system (HVS), fuzzy rule base system (FRBS), local binary pattern (LBP)

DOI: 10.3233/IFS-141223

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2497-2509, 2014

Authors: Kavitha, M. | Palani, S.

Article Type: Research Article

Abstract: Nowadays, diagnosis of diabetic retinopathy has caught the eager eyes of enthusiastic experimenters, as it has emerged as the common cause of blindness in the working age group. Several works are available in the literature for the detection of normalities and abnormalities through retinal image processing. Recently, a variety of literatures are presented based on normal/abnormal detection using retinal images. In this paper, we have proposed an efficient technique to detect the hard/soft exudates from abnormal retinal images. At first, the preprocessing step is carried out using Gaussian filter for enhancing the input retinal image. Consequently, normal/abnormal detection is done …using region segmentation, feature extraction and Levenberg-Marquardt-based neural network classifier. From the abnormal retinal image, we have detected the soft/hard exudates using fuzzy c-means clustering, feature extraction and Levenberg-Marquardt-based neural network classifier. Here, region segmentation is done by three ways; (i) blood vessel extraction (ii) optical disc extraction using curvelet transform and (iii) Damage area extraction. In soft/hard exudates detection, fuzzy c-means clustering is utilized for damage area extraction. Then, with the aid of segmented area, features such as mean, variance, area, perimeter, entropy, maximum intensity, minimum intensity, cross correlation, auto correlation and co-variance features are extracted. Once the features are computed, training of Levenberg-Marquardt-based neural network is done to classify the abnormal retinal images into soft or hard exudates. Here, the experimentation is done using Standard Diabetic Retinopathy Database and the performance is analyzed with the standard evaluation metrics of accuracy, specificity and sensitivity. The innovative technique is observed to achieve superb results and a comparison is also made with the existing method. The results have proved that the proposed technique has outperformed the existing method by having superior accuracy of 90.91% when compared with the existing methods. Show more

Keywords: Retinal, soft exudates, hard exudates, abnormal, normal, feature, classification, segmentation

DOI: 10.3233/IFS-141224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2511-2528, 2014

Authors: Perez-Tellez, Fernando | Cardiff, John | Rosso, Paolo | Pinto, David

Article Type: Research Article

Abstract: The characterisation and categorisation of weblogs and other short texts has become an important research theme in the areas of topic/trend detection, and pattern recognition, amongst others. The value of analysing and characterising short text is to understand and identify the features that can identify and distinguish them, thereby improving input to the classification process. In this research work, we analyse a large number of text features and establish which combinations are useful to discriminate between the different genres of short text. Having identified the most promising features, we then confirm our findings by performing the categorisation task using three …approaches: the Gaussian and SVM classifiers and the K-means clustering algorithm. Several hundred combinations of features were analysed in order to identify the best combinations and the results confirmed the observations made. The novel aspect of our work is the detection of the best combination of individual metrics which are identified as potential features to be used for the categorisation process. Show more

Keywords: Short text characterisation, feature extraction

DOI: 10.3233/IFS-141227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2529-2544, 2014

Authors: Tirado, Pedro

Article Type: Research Article

Abstract: We analyze the complexity of an expoDC algorithm by deducing the existence of solution for the recurrence inequation associated to this algorithm by means of techniques of Denotational Semantics in the context of fuzzy quasi-metric spaces. The fuzzy quasi-metrics provide an additional parameter “t” such that a suitable use of this ingredient gives rise to extra information on the involved computational process. This analysis is done by means of a fuzzy quasi-metric version of the Banach contraction principle on a space of partial functions endowed by a suitable adaptation of the Baire quasi-metric.

Keywords: ExpoDC algorithms, complexity, recurrence inequation, fuzzy quasi-metric, fixed point

DOI: 10.3233/IFS-141228

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2545-2550, 2014

Authors: Chen, Jeng-Fung | Do, Quang Hung

Article Type: Research Article

Abstract: The accurate prediction of student academic performance facilitates admission decisions and enhances educational services at tertiary institutions. This raises the need to have an effective model that predicts student performance in university that is based on the results of standardized exams and other influential factors, such as socio-economic background. In this study, a novel approach to the prediction of student academic performance based on the Cuckoo Search (CS) – hierarchical Adaptive Neuro-Fuzzy Inference System (HANFIS) model is proposed. Firstly, the most appropriate factors were selected and a dataset was constructed. Then, the proposed model was used to predict academic performance. …In the model, a hierarchical structure of ANFIS was suggested to solve the curse-of-dimensionality problem, the CS algorithm was utilized to optimize the clustering parameters which helped form the rule base, and ANFIS optimized the parameters in the antecedent and consequent parts of each sub-model. The findings showed that the proposed model is accurate and reliable. The results were also compared with those obtained from the Artificial Neural Network (ANN), GA-HANFIS (the combination of Genetic algorithm and HANFIS), and HANFIS models, indicating the proposed approach performed better. It is expected that this work may be used to assist in student admission procedures and strengthen the service system in educational institutions. Show more

Keywords: Cuckoo Search, adaptive neuro-fuzzy inference system, artificial neural network, prediction, higher education

DOI: 10.3233/IFS-141229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2551-2561, 2014

Authors: Ling, Wang | Lu, Wu Lu

Article Type: Research Article

Abstract: This paper presents a fuzzy rules extraction algorithm based on output-interval clustering and support vector regression. The approach is unlike most existing clustering algorithms for structure identification of fuzzy systems, where the focus is on combined input–output clustering. The output-interval clustering algorithm divides the output space into several partitions and each output partition is considered to be an interval; then, input data are projected into sub-clusters that are based on the input distribution constrained by the output intervals. Fuzzy rules are extracted from sub-clusters within each output interval. In order to have a more compact final system structure and better …accuracy, local functions associated with each of the sub-clusters based on support vector regression are constructed. The fuzzy rule-based modeling scheme gradually adapts its structure and rules antecedent and consequent parameters from data. Its main purpose is continuous learning, and adaptation to unknown environments. To illustrate the effectiveness of the approach, the paper considers a 2-D nonlinear function approximation, chaotic time series prediction and an operation learning application of steel mechanical property forecasting. Show more

Keywords: Fuzzy rule, clustering, support vector regression, forecasting

DOI: 10.3233/IFS-141230

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2563-2571, 2014

Authors: Ramathilagam, S. | Devi, R. | Hong, Tzung-Pei | Kannan, S.R.

Article Type: Research Article

Abstract: Identifying subgroups of genes from the gene expression of microarray high-dimensionality database is useful in discovering subtypes of cancers in Colon cancer database. Using clustering analysis for identifying cancer types in Colon cancer database is an extremely difficult task because of high-dimensionality gene with noise. Most of the existing clustering methods for colon to achieve types of cancers often hamper the interpretability of the structure. Therefore the aim of this paper is to develop suitable clustering techniques based on fuzzy c-means, the typicality of possibilistic c-means approaches, kernel functions, and neighborhood term to identify similar characters of genes and samples …for getting cancer subtypes in the colon cancer database. In order to avoid the random selection of initial prototypes of fuzzy clustering based techniques, this paper presents an algorithm to initialize the cluster prototypes. The performance of proposed methods has been evaluated through experimental work on Synthetic dataset, Wine dataset, IRIS dataset, Checkerboard, Time series, and Thyroid dataset. This paper successfully implements the proposed methods in finding subtypes of cancers in Colon cancer database. Compared with the results of recent existed clustering methods on benchmark datasets and Colon cancer database, this paper has shown that the proposed clustering approach can identify more similar objects of the subgroups than the existed methods. The superiority of the proposed methods has been proved through clustering accuracy. Show more

Keywords: Clustering, fuzzy C-means, possibilistic C-means, medical database, colon cancer

DOI: 10.3233/IFS-141231

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2573-2595, 2014

Authors: Wang, Bing | Pang, Bin | Ding, Guiyan

Article Type: Research Article

Abstract: In this paper, a completion theorem for gradual metric space and a completion theorem for gradual normed linear space are proved. The completion spaces are defined by means of an equivalence relation obtained by convergence of sequences via the gradual metrics and the gradual norms, respectively.

Keywords: Gradual number, gradual metric, gradual norm , completion

DOI: 10.3233/IFS-141232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2597-2602, 2014

Authors: Chandra, S. | Aggarwal, A.

Article Type: Research Article

Abstract: The celebrated Zimmermann's approach for solving fuzzy linear programming problems is re-looked and apparently a new formulation leading to a new interpretation is presented. The basic feature of this formulation is that it attempts to trade-off between the twin objectives of ‘satisfaction of fuzzy constraints’ and ‘attainment of the aspiration level of the objective function’. For this a bi-objective optimization problem involving these twin objectives is constructed and its efficient solution is interpreted as a solution of the given fuzzy linear programming problem. As an outcome of this study a new two phase approach to solve fuzzy linear programming problems …is obtained. This new two phase approach is different from those available in the literature and is in the true spirit of conventional two phase approach for solving crisp linear programming problems. Further, two additional models are also proposed on similar lines which also provide an efficient solution of the bi-objective optimization problem under consideration. Certain small numerical examples are included to illustrate the results. Show more

Keywords: Fuzzy optimization, fuzzy goals, fuzzy decision

DOI: 10.3233/IFS-141233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2603-2610, 2014

Authors: Mohtashami, Ali

Article Type: Research Article

Abstract: This paper proposes a novel meta-heuristic based algorithm which provides the optimal solution for several different degrees of feasibility for fuzzy linear and nonlinear programming problems. The proposed method has got the ability for solving those problems in which all coefficients of the objective function and constraints are represented by LR fuzzy numbers with linear and/or non-linear membership function. To solve the fuzzy problems, this paper provides a new hybrid genetic algorithm accompanied by a new proposed method of simulating fuzzy coefficients which 1) eliminates the need of applying defuzzification methods and/or expected interval methods, and 2) allows dealing with …different types of fuzzy numbers, properly. In order to show the performance of the proposed method, it is compared with “M. Jiménez, M. Arenas, A. Bilbao and M.V. Rodríguez, Linear programming with fuzzy parameters: An interactive method resolution, European Journal of Operational Research 177 (2007), 1599–1609.”. Computational results reveal that the proposed method is superior to the Jiménez et al. [13] method from the viewpoint of feasibility and optimality. Show more

Keywords: Genetic algorithm, fuzzy linear programming, fuzzy non-linear programming, fuzzy number, hybrid genetic algorithm (HGA)

DOI: 10.3233/IFS-141234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2611-2622, 2014

Authors: Azadbakht, Bakhtiar | Zolata, Hamidreza | Khayat, Omid

Article Type: Research Article

Abstract: Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. The primary purpose of this work is to develop a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. To this aim, the EMG signals of five skeletal muscles as biceps, deltoid, triceps, tibialis anterior and quadriceps muscles are recorded in three states of isometric contraction (ISO), maximum voluntary contraction (MVC) and dynamic contraction from 22 normal subjects aged between 20 and 30 half of them are male. Totally, 14 combinatory extracted features are analyzed …to find which of them or a combinatory set of them are discriminative and selective for muscle force quantification and classification. The neuro-fuzzy system is trained with 70 percent of the recorded EMG cut off windows and then it is employed for classification and modeling purposes. For each muscle the most effective extracted features are found for males and females separately by a reference classifier. In the experiments, after the optimum set of combinatory features is found by a reference classifier, the neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Then, different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used. Show more

Keywords: EMG signal characterization, neuro-fuzzy classifier, contraction states, feature extraction

DOI: 10.3233/IFS-141235

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2623-2634, 2014

Authors: Kannan, P. | Shantha Selva Kumari, R.

Article Type: Research Article

Abstract: VLSI architecture for face recognition system based on Local Gabor XOR Pattern (LGXP) feature extraction method is presented in this paper. LGXP is utilized to encode Gabor phase variations and to extract feature with the help of Gabor filter and Local XOR Pattern (LXP) operator. VLSI architecture for Gabor Filter and a Behavioral model for LXP operator for feature extraction are investigated. Also a behavioral model for Similarity matching is designed using Verilog language. The similarity matching for face recognition is executed by L1 distance measure. Therefore our approach explores the effectiveness of Gabor phase information on FPGA platform by …addressing the drawbacks like computational complexity and hardware complexity by mapping the algorithms. The proposed approach is designed on virtex-5 device using Veriolg HDL in Xilinx ISE tool and the logic utilization results will be generated using synthesis tool while the power consumption report will be analyzed using Xpower analysis tool. Also the effectiveness of our design is evaluated with FAR, FRR and accuracy plot in Matlab simulation environment. Research outcome of our proposed face recognition system over UPC face database is 72.225% Accuracy for distance matching threshold of ‘5’. Show more

Keywords: VLSI architecture, LGXP, gabor filter, LXP, similarity matching, L1 distance

DOI: 10.3233/IFS-1412366

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2635-2647, 2014

Authors: Maheswari, R.V. | Subburaj, P. | Vigneshwaran, B. | Kalaivani, L.

Article Type: Research Article

Abstract: Partial discharge (PD) is an important tool for assessing the quality of the insulation system in High Voltage (HV) power apparatus. In this work, four different PD sources namely corona and surface discharges in both air and oil are measured in the HV laboratory. Initially 3-D (�-q-n) PD patterns are extracted from the PD data. Then it is subjected to two different fractal image compression techniques namely box counting method and semi variance method. For box counting method, the fractal dimensions like fractal dimension average, standard deviation and lacunarity are evaluated. For semi variance method, horizontal and vertical fractal dimension …averages are evaluated. The extracted fractal features from 3-D PD patterns are used as input parameters for non linear Support Vector Machine (SVM) for PD recognition. The performance of non linear SVM is compared with Artificial Neural Network (ANN) and linear SVM classifiers. The non linear SVM with semi variance method provides outer performance as compared with other methods due to its gain flexibility and good out-of-sample generalization. Show more

Keywords: Partial discharge (PD), fractal image compression techniques, artificial neural network (ANN), affine transformation (AT), support vector machine (SVM)

DOI: 10.3233/IFS-141237

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2649-2664, 2014

Authors: Liang, Cheng-Yu | Shi, Fu-Gui

Article Type: Research Article

Abstract: In this paper, the degrees to which a mapping is continuous, open or closed are introduced in (L, M)-fuzzy topological spaces by using implication operation and some characterizations of them are presented. Also their relationships with the degrees of compactness, connectedness, T1 and T2 axioms in (L, M)-fuzzy topological spaces are discussed.

Keywords: (L, M)-fuzzy topological space, continuous mapping, open mapping, closed mapping, homeomorphism

DOI: 10.3233/IFS-141238

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2665-2677, 2014

Authors: Zhou, Wei | Meng, Sun | Chen, Minghui

Article Type: Research Article

Abstract: This study proposes the hybrid Atanassov intuitionistic fuzzy number (HAIFN) to effectively and accurately represent membership and non-membership degrees in an Atanassov's intuitionistic fuzzy environment. The HAIFN is obtained by combining the crisp number with the interval-valued number, in which decision makers provide only an interval-valued membership degree, thereby avoiding the intricate and difficult evaluation of the non-membership degree. Based on the HAIFN, the hybrid Atanassov intuitionistic fuzzy Bonferroni mean and the generalized hybrid Atanassov intuitionistic fuzzy Bonferroni mean are introduced to aggregate the hybrid Atanassov intuitionistic fuzzy information and capture their interrelationship. The hybrid Atanassov intuitionistic fuzzy weighted Bonferroni …mean, the generalized hybrid Atanassov intuitionistic fuzzy weighted Bonferroni mean, and their desired properties are further investigated given the distinct importance of each criterion. A practical case is provided at the end to demonstrate the application of the proposed fuzzy numbers and aggregation operators. Show more

Keywords: Hybrid Atanassov intuitionistic fuzzy number, Bonferroni mean, fuzzy sets, multi-criteria aggregation

DOI: 10.3233/IFS-141239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2679-2690, 2014

Authors: Zhang, Jingyu | Zhou, Jian | Zhong, Shuya

Article Type: Research Article

Abstract: An inverse minimum spanning tree problem is to make the least modification on the edge weights such that a predetermined spanning tree is a minimum spanning tree with respect to the new edge weights. In this paper, a type of fuzzy inverse minimum spanning tree problem is introduced from a LAN reconstruction problem, where the weights of edges are assumed to be fuzzy variables. The concept of fuzzy α-minimum spanning tree is initialized, and subsequently a fuzzy α-minimum spanning tree model and a credibility maximization model are presented to formulate the problem according to different decision criteria. In order to …solve the two fuzzy models, a fuzzy simulation for computing credibility is designed and then embedded into a genetic algorithm to produce some hybrid intelligent algorithms. Finally, some computational examples are given to illustrate the effectiveness of the proposed algorithms. Show more

Keywords: Minimum spanning tree, inverse optimization, fuzzy programming, genetic algorithm

DOI: 10.3233/IFS-141384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 27, no. 5, pp. 2691-2702, 2014