Journal of Intelligent & Fuzzy Systems - Volume 36, issue 4

Authors: López-Ortega, Omar | Castro-Espinoza, Félix

Article Type: Research Article

Abstract: This paper presents an account of fuzzy similarity metrics that have been proposed to quantify consensus in Multi Criteria Group Decision Making. Fuzzy similarity metrics are indispensable to determine consensus when experts evaluate alternatives in fuzzy terms, which capture experts’ uncertainty and hesitancy. Furthermore, factors such as the level of expertise or cognitive bias lead to disagreements within the group. The fuzzy similarity metrics described in this article are used to measure the similarity between type 1 and type 2 fuzzy sets, and fuzzy numbers. Consensus can be quantified at three different levels: criteria judgement, alternative judgement, or expert preferences. …Promising future work includes the incorporation of social fuzzy measures under the umbrella of multi-agent systems as well as the analysis of fuzzy intuitionistic sets. Show more

Keywords: Consensus measure, fuzzy distance, aggregation, decision making

DOI: 10.3233/JIFS-18508

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3095-3104, 2019

Authors: Dixit, Veer Sain | Jain, Parul

Article Type: Research Article

Abstract: Context Aware Recommender Systems exploit specific situation of users for recommendations, hence are more accurate and satisfactory. Neighborhood based collaborative filtering is the most successful approach in this area owing of its simplicity, intuitiveness, efficiency and domain independence. The key of this approach is to find similarity between users or items using user–item–context rating matrix. Typically, context aware datasets are highly sparse since there are not enough or no preferences under most contextual conditions. Traditional similarity measures such as Pearson correlation coefficient, Cosine and Mean squared difference suffer from co-rated item problem and do not consider contextual conditions of the …users. Therefore, these measures are not effective for sparse datasets. Therefore, the aim of this paper is to propose a new similarity measure and its variants based on Bhattacharyya Coefficient which are suitable for sparse datasets weighted by contextual similarity. Subsequently, we have applied them in neighborhood based algorithms where each component is contextually weighted. The experiments are performed on two contextually rich datasets which are especially designed to do personalization research instead traditional well known datasets. The results for Individual and Group recommendations indicate that the proposed similarity measure based algorithms have significantly increased the accuracy of predictions over traditional Pearson correlation coefficient measure based algorithms. Show more

Keywords: Bhattacharya coefficient, Neighborhood based collaborative filtering, Contextual similarity, Sparse datasets, Group recommendations

DOI: 10.3233/JIFS-18341

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3105-3117, 2019

Authors: Sánchez, Octavio | Sierra, Gerardo

Article Type: Research Article

Abstract: This article introduces a different method for text representation in order to perform clustering over different articles which, arguably, has no subjective information with similar topic-sentiment use of language. Using the joint sentiment/topic model, the text is vectorized in a low dimensional space. These vectors were then used as distance measurement for clustering texts. While comparing this unusual method with a traditional bag ofwords representation an improvement in the performance of the algorithms was observed. The authors think this method of representation might have implications for future studies of the computational interpretation of texts.

Keywords: Text representation, Joint Sentiment Topic Modeling, text clustering

DOI: 10.3233/JIFS-18530

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3119-3128, 2019

Authors: Rashid, Ahmar | Kamran, Muhammad | Halim, Zahid

Article Type: Research Article

Abstract: A graph is a network that can represent the communication between a variety of data elements. The data can have uncertainty, primarily due to the heterogeneity of data sources. Moreover, it is sometimes difficult to assure the existence of a link between data elements; compelling to consider the data as a probabilistic entity. Extracting densely connected regions from a graph is a key task of the intelligent systems. The enumeration of dense substructures in a graph can help to identify important patterns. This can have many applications in medical image processing, accident analysis, and surveillance, to name a few. One …such dense substructure is a clique, where all nodes are directly connected to each other. An α -maximal clique in an uncertain graph is a clique with a minimum probability α , such that it is not a subset of any other clique of the same weight. Extracting all α -maximal cliques is an NP-Complete problem. This work focuses on reducing the time consumed to enumerate all α -maximal cliques in a graph. Another focus of this work is to reduce the CPU (Central Processing Unit) cycles for efficient enumeration of all α -maximal cliques. An algorithm is proposed that computes all weighted maximal cliques in an uncertain graph. The worst-case asymptotic time complexity of the algorithm is O(n2n ) . The proposed algorithm utilizes the h -index concept to form cliques with vertex degree greater than h . The algorithm builds cliques at two levels of enumeration. The first level finds the α -maximal cliques with a descending order in sizes. On each successive α -maximal clique iteration of the first level, the second level tracks and deletes all subsets of the clique. The second level is to ensure the fact that all subsets of an α -maximal clique are cliques. The proposed algorithm is compared with two recent maximal clique enumeration algorithms, namely: MULE (Maximal Uncertain Clique Enumeration) and LMC (Listing all maximal cliques in large sparse real-world graphs). Real-world benchmark uncertain graphs are utilized for the experimental evaluation. The results suggest better performance of the proposed approach in terms of the time consumption. Show more

Keywords: Graphs, clique, enumeration, uncertain data

DOI: 10.3233/JIFS-18263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3129-3141, 2019

Authors: Li, Fei | Yue, Yueli | Yao, Wei

Article Type: Research Article

Abstract: For L being a GL-quantale and based on the concept of L -valued relations, we propose generalized L -fuzzy rough sets as a further generalization of L -fuzzy rough sets, and then characterized the L -fuzzy rough sets are from both constructive and axiomatic approaches.

Keywords: Generalized L-fuzzy rough set, L-valued relation, generalized L-approximation operator, GL-quantale

DOI: 10.3233/JIFS-172301

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3143-3153, 2019

Authors: Przybyszewski, Andrzej W.

Article Type: Research Article

Abstract: Object recognition is a complex neuronal process determined by interactions between many visual areas: from the retina, thalamus to the ventral visual pathway. These structures transform variable, single pixel signal in photoreceptors to a stable object representation. Neurons in visual area V4, midway in ventral stream, represent such stable shape detector. A feed forward hierarchy of increasing in size and complexity receptive fields (RF) leads to grand mother cell concept. Our question is how these processes might identify an object or its elements in order to recognize it in new, unseen conditions? We propose a new approach to this …problem by extending the classical definition of the RF to a fuzzy detector. RF properties are also determined by the computational properties of the bottom-up and top-down pathways comparing stimulus with many predictions. The “driver-type”.ogic (DTL) of bottom-up computations looks for large number of possible object parts (hypotheses –.ough set (RS) upper approximation), as object’s elements are similar to RF properties. The optimal combination is chosen, in unsupervised, parallel, multi-hierarchical pathways by the “modulator-type”.ogic (MTL) of top-down computations (RS lower approximation). Interactions between DTL (hypotheses) and MTL (predictions) terminates when RS boundary became small - the object is recognized. Show more

Keywords: Brain’s logic, ascending, descending pathways, object categorization, predictive coding, inconsistent rules

DOI: 10.3233/JIFS-18401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3155-3167, 2019

Authors: de Jesús Martínez Felipe, Miguel | Felipe Riverón, Edgardo Manuel | Martínez Castro, Jesús Alberto | Pogrebnyak, Oleksiy

Article Type: Research Article

Abstract: In this paper, the problem of image block similarity measuring in noisy environment is considered. In different practical applications often is necessary to find groups of similar image blocks within an ample search area. In such situation, the full search algorithm is very slow; apart, its accuracy is low due to the presence of noise. New algorithms for similar image block matching in noisy environment are presented. The algorithms are based on the dissimilarity measure calculated as the distance between image patches in the discrete cosine transform domain. The proposed algorithms perform the hierarchical search for the similar image blocks …and hereby have a reduced complexity in comparison to the full search algorithm. Adjusting the radius of the distance calculation for spectral coefficient matching, the characteristics of the block matching algorithm can easily be adjusted to obtain a better accuracy of the matched block group. A higher accuracy is obtained using the local adaptation of the radius for the distance calculation outperforming the existing algorithms used to find groups of similar blocks in different applications, such as image noise filtering and image clustering. The performance of the different block matching algorithms were evaluated on the base of the proposed accuracy measure that uses as a reference the list of patches obtained with the full search algorithm in the absence of noise. Show more

Keywords: Dissimilarity measure, noisy image block matching, discrete cosine transform, hierarchical search, local adaptation

DOI: 10.3233/JIFS-18533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3169-3176, 2019

Authors: Hasnat, Abul | Barman, Dibyendu

Article Type: Research Article

Abstract: Image compression is a process that reduces memory space required to store an image. The image compression techniques are broadly classified into two categories a) Lossless technique b) Lossy technique. Lossy compression technique achieves higher result, but due to data loss it may cause spatial inconsistency, blocking artifact, quantization noise in the decompressed image degrading the image quality. Most of the existing compression techniques are applicable on single image separately. In this study an image compression method is proposed where multiple images of the same size are combined together and compressed to achieve higher compression ratio while keeping image quality …as close as standard image compression techniques. Luminance channel of each image is compressed separately using Vector Quantization (VQ) algorithm while two chrominance channels, Cb and Cr of all images are combined into a three dimensional matrix that forms training vector. Clustering is applied on the training vector to get the initial color representatives. Thus, for the chrominance channels of n number of images, the proposed method generates one index matrix and one centroid matrix of size 256×2× n where 256 is the number of clusters. This centroid matrix contains one 256×2 dimensional centroid matrix for each individual image. This 256×2 matrix contains centroid of each cluster. The centroids of each and every cluster of an image are updated individually using optimization technique to get a better centroid (Cb, Cr) pair. This process updates the color representative pair of Cb and Cr further. This method has been applied on standard images in literature and images collected from UCID v. 2 color image database. Experimental results are analyzed in terms of PSNR and space reduction. Experimental results show that the proposed method achieves a higher compression ratio retaining almost similar image information as other standard lossy compression algorithm. Show more

Keywords: Color image quantization, de-correlated color space, JPEG, K-Means clustering, lossless compression, lossy compression, optimization, PSNR, Vector Quantization, YCbCr

DOI: 10.3233/JIFS-18360

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3177-3193, 2019

Authors: Liu, Jiubing | Zhou, Xianzhong | Huang, Bing | Li, Huaxiong | Ju, Hengrong

Article Type: Research Article

Abstract: In the study of intuitionistic fuzzy clustering, the construction of an intuitionistic fuzzy similarity matrix (IFSM) is a fundamental and important issue in the direct clustering analysis, since it determines clustering results and computational efforts. Many methods based on the axioms of intuitionistic fuzzy similarity relations are applicable to IFSM construction. However, most of existing methods may yield a “counterintuitive result” in some cases and consume much computational time. In this paper, we propose a novel intuitionistic fuzzy clustering method to deal with such problems. First, based on the normalized Hamming distance, we define a similarity measure between intuitionistic fuzzy …numbers (IFNs), by which a similarity measure between intuitionistic fuzzy sets (IFSs) is induced. Second, a divergence measure between IFSs is obtained by extending the dissimilarity of IFNs. Third, we construct an IFSM by using together the similarity and divergence measures so as to cluster the intuitionistic fuzzy information. Finally, two examples are presented to show the effectiveness and advantages of our method. Show more

Keywords: Intuitionistic fuzzy sets, similarity measure, divergence measure, intuitionistic fuzzy similarity matrix, clustering analysis

DOI: 10.3233/JIFS-18427

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3195-3209, 2019

Authors: Kumar, Mohit

Article Type: Research Article

Abstract: The correlation between the performance of attributes and the overall satisfaction perceived by the customers is a successful indirect approach to evaluate the importance of the attributes in services. Recently, the fuzzy importance of attributes was discussed as the fuzzy correlation between the performance of attributes and the overall satisfaction. In this paper, the concept of fuzzy set is extended to the idea of intuitionistic fuzzy set and a novel simple approach is presented to evaluate the importance of attributes as the intuitionistic fuzzy correlation between the performance of attributes and the overall satisfaction. The qualitative input data such …as customer’s opinions are available and expressed as linguistic terms. Each of these linguistic terms is mathematically represented by membership and non-membership functions of intuitionistic fuzzy number instead of a fuzzy number or a crisp number. The calculation is based on the weakest triangular norm (t-norm) arithmetic operations on triangular intuitionistic fuzzy numbers. The proposed approach has been applied to a survey with respect to the quality of hotel services in Oradea (Romania) and then compared with other existing approaches to calculating the importance of hotel quality attribute. Show more

Keywords: Correlation coefficient, intuitionistic fuzzy number, weakest t-norm arithmetic, triangular intuitionistic fuzzy number, intuitionistic fuzzy importance

DOI: 10.3233/JIFS-18485

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 4, pp. 3211-3223, 2019