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