Hybrid journal (It can contain Open Access articles) ISSN (Print) 2052-353X - ISSN (Online) 2052-3548 Published by Inderscience Publishers[439 journals]
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Authors:G.K. Jagatheswari, R. Murugesan Pages: 1 - 15 Abstract: Edge detection is the first step in image as it can be applied in the process of feature extraction, segmentation and classification. This study proposes a new fuzzy logic based edge detection method and computationally efficient algorithm based on triangular fuzzy interpolation technique for intra field deinterlacing of images that reduces the loss in image quality at the edges. The proposed algorithm improves image deinterlacing quality with respect to missing pixels in the edge region. When interpolating the missing pixels, characteristics or features of the region around the pixel are interpolated first. The proposed algorithm is tested with five test images which have both uniform surface and edges. The subjective and objective performance of the proposed algorithm shows greater improvement in the edge quality when correlated with the traditional deinterlacing methods. The designed fuzzy rules become an attractive solution for the betterment of the quality of edges as much as possible. The result reveals that the proposed algorithm provides greater robustness to contrast and lighting variations. Keywords: triangular fuzzy representation; slope scale; missing pixel; edge detection; edge direction gradient; deinterlacing; fuzzy interpolation; Gaussian gradient; rough interlacing line Citation: International Journal of Fuzzy Computation and Modelling, Vol. 4, No. 1 (2022) pp. 1 - 15 PubDate: 2022-07-25T23:20:50-05:00 DOI: 10.1504/IJFCM.2022.124338 Issue No:Vol. 4, No. 1 (2022)
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Authors:Arnab Kundu, Tripti Bej, Samirranjan Adhikari Pages: 16 - 33 Abstract: The human cognitive structure is very uncertain and ever-elusive to arrest. The purpose of this study was to formulate a mathematical model to evade response bias latent in the quantification process in any decision-making by applying intuitionistic fuzzy logic, potent in arresting uncertainties. Following this research aim, a sample problem was adopted from the school setting regarding the election of a class monitor based on an opinion survey among five teachers on a Likert scale. The numerical decision values were converted to intuitionistic fuzzy. Findings revealed a palpable difference between Likert values and their Fuzzified corresponding values wherefrom the authors empirically deduced that fuzzified result is more precise over the quantified Likert values considering respondents' biases, uncertainties, inter-rater agreements, or disagreements. Finally, the researchers proposed the intuitionistic fuzzy score function evolved in this study, needs to be investigated with a larger sample size to draw more authentication. Keywords: response bias; subjective error; survey; fuzzy; intuitionistic fuzzy; class monitor; decision making; uncertainties Citation: International Journal of Fuzzy Computation and Modelling, Vol. 4, No. 1 (2022) pp. 16 - 33 PubDate: 2022-07-25T23:20:50-05:00 DOI: 10.1504/IJFCM.2022.124343 Issue No:Vol. 4, No. 1 (2022)
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Authors:Paul Augustine Ejegwa, Idoko Charles Onyeke Pages: 34 - 50 Abstract: Fermatean fuzzy set is a special extensional fuzzy set which generalises both intuitionistic fuzzy set and Pythagorean fuzzy set by expanding the spatial scope of Pythagorean fuzzy sets and intuitionistic fuzzy sets. Similarity measure is an integral aspect of utilising generalised fuzzy sets in soft computing. In this paper, a novel similarity measure between Fermatean fuzzy sets is introduced with a better and reliable output compare to the dual of the existing Fermatean fuzzy distance measure. Some properties of the new similarity measure are characterised. It is demonstrated that the new similarity measure between Fermatean fuzzy sets is more reliable than the dual of the existing Fermatean fuzzy distance measure. In terms of application, the new Fermatean fuzzy similarity measure is utilised in executing students' admission process using an algorithmic approach implemented by a programming language to enhance accuracy and ease of computations. Keywords: student admission; similarity measure; intuitionistic fuzzy set; Pythagorean fuzzy set; Fermatean fuzzy set Citation: International Journal of Fuzzy Computation and Modelling, Vol. 4, No. 1 (2022) pp. 34 - 50 PubDate: 2022-07-25T23:20:50-05:00 DOI: 10.1504/IJFCM.2022.124368 Issue No:Vol. 4, No. 1 (2022)
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Authors:Paul Augustine Ejegwa, Idoko Charles Onyeke Pages: 51 - 72 Abstract: Modelling the users' perception on network services and characteristics representing the efficiency of social media systems incorporates multiple attributes. In this paper, fuzzy logic theory is employed as a computationally intelligent technique for reviewing and assessing performance of the modern social networking sites with broader deployment. Implementation of the presented fuzzy methodology provides an overview of the accuracy and functionality evolving with the increasing size of social networks with massive data collection and their relationships to the customer behaviour. The proposed model based on the users' social characteristics can be used to evaluate the validity and utility enforcement on online social networks. We have used several error metrics comprising mean gamma deviance, R-squared and RMLSE for experimental verification of the proposed technique against the actual social networking data. Finally, accuracy of our fuzzy optimisation model is compared with the previous works in terms of acquiring considerably lower mean absolute error. Keywords: fuzzy logic; membership function; reliability; social networks; utility Citation: International Journal of Fuzzy Computation and Modelling, Vol. 4, No. 1 (2022) pp. 51 - 72 PubDate: 2022-07-25T23:20:50-05:00 DOI: 10.1504/IJFCM.2022.124375 Issue No:Vol. 4, No. 1 (2022)
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Authors:Sayan Das, Jaya Sil Pages: 73 - 103 Abstract: Scarcity of doctors and lack of skilled manpower in rural India is a real challenge in diagnosing the people even for the common health-related problems such as flu, diarrhea etc. Due to a lack of domain knowledge and expertise, health assistants are unable to consistently categories patients as diseased or not. The paper aims to develop a rough information theoretic approach for diagnosing patients with minimum false cases by reducing uncertainty in health dataset. Knowledge granulation of rough set theory is used to partition the patients into positive region (PR, certainly diagnosed), and boundary region (BR, possibly diagnosed). Conditional entropy of patients in BR is measured, considering patients of PR and accordingly feature values of patients in BR are revised. The model is suitable for providing primary healthcare to the patients based on diagnosis; however, not substitutions of doctors and in emergency cases, patients are referred to the experts. Keywords: uncertainty management; rough set; information theory; remote healthcare; conditional entropy; fuzzy c-mean; rule base; decision system Citation: International Journal of Fuzzy Computation and Modelling, Vol. 4, No. 1 (2022) pp. 73 - 103 PubDate: 2022-07-25T23:20:50-05:00 DOI: 10.1504/IJFCM.2022.124361 Issue No:Vol. 4, No. 1 (2022)
Hybrid journal (It can contain Open Access articles)
[439 journals]
