Published: Oct 1, 2019
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DOI: 10.4018/IJFSA.20191001.pre
Volume 8
B.K. Tripathy
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Tripathy, B.K. "Special Issue on Fuzzy Techniques in Data Clustering, Image Processing and Applications." IJFSA vol.8, no.4 2019: pp.4-6. http://doi.org/10.4018/IJFSA.20191001.pre
APA
Tripathy, B. (2019). Special Issue on Fuzzy Techniques in Data Clustering, Image Processing and Applications. International Journal of Fuzzy System Applications (IJFSA), 8(4), 4-6. http://doi.org/10.4018/IJFSA.20191001.pre
Chicago
Tripathy, B.K. "Special Issue on Fuzzy Techniques in Data Clustering, Image Processing and Applications," International Journal of Fuzzy System Applications (IJFSA) 8, no.4: 4-6. http://doi.org/10.4018/IJFSA.20191001.pre
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Published: Oct 1, 2019
Converted to Gold OA:
DOI: 10.4018/IJFSA.2019100101
Volume 8
Animesh Biswas, Samir Kumar
In this article, the concept of an intuitionistic fuzzy possibility degree (IFPD) for ordering several interval-valued intuitionistic fuzzy numbers (IVIFNs) is introduced. The IFPD ranks IVIFNs by...
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In this article, the concept of an intuitionistic fuzzy possibility degree (IFPD) for ordering several interval-valued intuitionistic fuzzy numbers (IVIFNs) is introduced. The IFPD ranks IVIFNs by distinguishing the comparable and non-comparable components of the joint intervals of membership and non-membership degrees. The incomparable cases of nested joint intervals can also rank respective IVIFNs through the proposed IFPD approach. An intuitionistic fuzzy possibility preference relation based on the proposed IFPD measure for IVIFNs is defined as a more effective tool for modelling uncertainty than existing intuitionistic preference relations. Further, an approach for solving multicriteria interval-valued intuitionistic fuzzy decision-making problems based on IFPD measure of IVIFNs is advanced also provides a possibility degree as supplementary information to the ranking of alternatives. The validity and effectiveness of the advanced approach are demonstrated through two illustrative examples.
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Biswas, Animesh, and Samir Kumar. "Intuitionistic Fuzzy Possibility Degree Measure for Ordering of IVIFNs with Its Application to MCDM." IJFSA vol.8, no.4 2019: pp.1-24. http://doi.org/10.4018/IJFSA.2019100101
APA
Biswas, A. & Kumar, S. (2019). Intuitionistic Fuzzy Possibility Degree Measure for Ordering of IVIFNs with Its Application to MCDM. International Journal of Fuzzy System Applications (IJFSA), 8(4), 1-24. http://doi.org/10.4018/IJFSA.2019100101
Chicago
Biswas, Animesh, and Samir Kumar. "Intuitionistic Fuzzy Possibility Degree Measure for Ordering of IVIFNs with Its Application to MCDM," International Journal of Fuzzy System Applications (IJFSA) 8, no.4: 1-24. http://doi.org/10.4018/IJFSA.2019100101
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Published: Oct 1, 2019
Converted to Gold OA:
DOI: 10.4018/IJFSA.2019100102
Volume 8
Srujan Sai Chinta
Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional...
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Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional clustering algorithms with a meta-heuristic like the Firefly Algorithm improves the stability of the output as well as the speed of convergence. It is well known now that the Euclidean distance as a measure of similarity has certain drawbacks and so in this paper we replace it with kernel functions for the study. In fact, the authors combined Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Means (RIFCM) with Firefly algorithm and replaced Euclidean distance with either Gaussian or Hyper-tangent or Radial basis Kernels. This paper terms these algorithms as Gaussian Kernel based rough Fuzzy C-Means with Firefly Algorithm (GKRFCMFA), Hyper-tangent Kernel based rough Fuzzy C-Means with Firefly Algorithm (HKRFCMFA), Gaussian Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (GKRIFCMFA) and Hyper-tangent Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (HKRIFCMFA), Radial Basis Kernel based rough Fuzzy C-Means with Firefly Algorithm (RBKRFCMFA) and Radial Basis Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (RBKRIFCMFA). In order to establish that these algorithms perform better than the corresponding Euclidean distance-based algorithms, this paper uses measures such as DB and Dunn indices. The input data comprises of three different types of images. Also, this experimentation varies over different number of clusters.
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DOI: 10.4018/IJFSA.2019100103
Volume 8
Shashwati Mishra, Mrutyunjaya Panda
Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery...
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Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.
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Mishra, Shashwati, and Mrutyunjaya Panda. "Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions: A Comparative Study." IJFSA vol.8, no.4 2019: pp.39-59. http://doi.org/10.4018/IJFSA.2019100103
APA
Mishra, S. & Panda, M. (2019). Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions: A Comparative Study. International Journal of Fuzzy System Applications (IJFSA), 8(4), 39-59. http://doi.org/10.4018/IJFSA.2019100103
Chicago
Mishra, Shashwati, and Mrutyunjaya Panda. "Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions: A Comparative Study," International Journal of Fuzzy System Applications (IJFSA) 8, no.4: 39-59. http://doi.org/10.4018/IJFSA.2019100103
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Published: Oct 1, 2019
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DOI: 10.4018/IJFSA.2019100104
Volume 8
S Vijayakumar, V. Santhi
In recent years, image processing has played a vital role in major research areas. In this article, a new approach using a fuzzy inference system is proposed for speckle reduction in SAR images. In...
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In recent years, image processing has played a vital role in major research areas. In this article, a new approach using a fuzzy inference system is proposed for speckle reduction in SAR images. In general, SAR images are predominantly used to monitor coastal regions to detect oil spills, ship wake, sea shores and climate changes. In this article, a gamma distribution model is used in a fuzzy inference system to remove speckle noise from SAR images. The performance of the proposed model is tested using fuzzy inference systems, such as mamdani and sugeno. The experimental results proved the efficiency of the proposed system through objective metrics.
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Vijayakumar, S, and V. Santhi. "Speckle Noise Reduction in SAR Images Using Fuzzy Inference System." IJFSA vol.8, no.4 2019: pp.60-83. http://doi.org/10.4018/IJFSA.2019100104
APA
Vijayakumar, S. & Santhi, V. (2019). Speckle Noise Reduction in SAR Images Using Fuzzy Inference System. International Journal of Fuzzy System Applications (IJFSA), 8(4), 60-83. http://doi.org/10.4018/IJFSA.2019100104
Chicago
Vijayakumar, S, and V. Santhi. "Speckle Noise Reduction in SAR Images Using Fuzzy Inference System," International Journal of Fuzzy System Applications (IJFSA) 8, no.4: 60-83. http://doi.org/10.4018/IJFSA.2019100104
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Published: Oct 1, 2019
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DOI: 10.4018/IJFSA.2019100105
Volume 8
Akarsh Goyal, Rahul Chowdhury
In recent times, an enumerable number of clustering algorithms have been developed whose main function is to make sets of objects have almost the same features. But due to the presence of...
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In recent times, an enumerable number of clustering algorithms have been developed whose main function is to make sets of objects have almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also, some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and therefore have stability issues. Thus, handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 an MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data but cannot be used robustly for hybrid data. In this article, the authors generalize the MMeR algorithm with neighborhood relations and make it a neighborhood rough set model which this article calls MMeNR (Min Mean Neighborhood Roughness). It takes care of the heterogeneous data. Also, the authors have extended the MMeNR method to make it suitable for various applications like geospatial data analysis and epidemiology.
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Goyal, Akarsh, and Rahul Chowdhury. "Clustering Hybrid Data Using a Neighborhood Rough Set Based Algorithm and Expounding its Application." IJFSA vol.8, no.4 2019: pp.84-100. http://doi.org/10.4018/IJFSA.2019100105
APA
Goyal, A. & Chowdhury, R. (2019). Clustering Hybrid Data Using a Neighborhood Rough Set Based Algorithm and Expounding its Application. International Journal of Fuzzy System Applications (IJFSA), 8(4), 84-100. http://doi.org/10.4018/IJFSA.2019100105
Chicago
Goyal, Akarsh, and Rahul Chowdhury. "Clustering Hybrid Data Using a Neighborhood Rough Set Based Algorithm and Expounding its Application," International Journal of Fuzzy System Applications (IJFSA) 8, no.4: 84-100. http://doi.org/10.4018/IJFSA.2019100105
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Published: Oct 1, 2019
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DOI: 10.4018/IJFSA.2019100106
Volume 8
Tripti Bej, Madhumangal Pal
Nearly forty years ago, interval-valued fuzzy sets were propounded by Zadeh as the normal ramification of fuzzy sets. This article focuses on the basics of a theory for such an interval-valued fuzzy...
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Nearly forty years ago, interval-valued fuzzy sets were propounded by Zadeh as the normal ramification of fuzzy sets. This article focuses on the basics of a theory for such an interval-valued fuzzy set becoming interval-valued doubt fuzzy subalgebra and an interval-valued doubt fuzzy ideal of BCK-algebras. Also, the authors discuss fuzzy translation, fuzzy multiplication of an interval-valued doubt fuzzy subalgebra/ideal of a BCK-algebra. Besides this, the authors have attempted to substantiate a few common features relating them. At the same time, some properties of interval-valued doubt fuzzy ideals under homomorphism are investigated and the product of interval-valued doubt fuzzy ideals in BCK-algebras is also established.
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Bej, Tripti, and Madhumangal Pal. "Interval-Valued Doubt Fuzzy Ideals in BCK-Algebras." IJFSA vol.8, no.4 2019: pp.101-121. http://doi.org/10.4018/IJFSA.2019100106
APA
Bej, T. & Pal, M. (2019). Interval-Valued Doubt Fuzzy Ideals in BCK-Algebras. International Journal of Fuzzy System Applications (IJFSA), 8(4), 101-121. http://doi.org/10.4018/IJFSA.2019100106
Chicago
Bej, Tripti, and Madhumangal Pal. "Interval-Valued Doubt Fuzzy Ideals in BCK-Algebras," International Journal of Fuzzy System Applications (IJFSA) 8, no.4: 101-121. http://doi.org/10.4018/IJFSA.2019100106
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