Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Meenakshi Kaushal"'
Autor:
Meenakshi Kaushal, Q. M. Danish Lohani
Publikováno v:
IEEE Access, Vol 10, Pp 26271-26281 (2022)
Atanassov intuitionistic fuzzy set (AIFS) has the capability to deal with various uncertain situations, so its popularity among researchers is quite high. It has been observed that Euclidean distance measure based AIFS clustering algorithms perform w
Externí odkaz:
https://doaj.org/article/f3e22acdad8b4f538f3d31f48ff8fa85
Publikováno v:
Journal of Inequalities and Special Functions. 13:1-13
In machine learning, distance measure plays an important role in defining the similarity between two data-items. In the paper, we discuss some of the drawbacks of distance measures (metrics) with their possibly induced clustering algorithms. Further,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8df807e95a77d562c1a532c34bb61161
https://doi.org/10.54379/jiasf-2022-1-1
https://doi.org/10.54379/jiasf-2022-1-1
Publikováno v:
Information Sciences. 642:119087
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c6f95c4d1bc67699447eebddeba54d6
https://doi.org/10.1016/j.ins.2023.119087
https://doi.org/10.1016/j.ins.2023.119087
Fuzzy data envelopment analysis (FDEA) is an efficient modeling technique to rank decision-making units (DMUs) with imprecise inputs/outputs. It is a linear programming problem that constructs an optimal frontier line, known as an efficient frontier.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f493d6559715cd478a1633370a49e3c
https://doi.org/10.21203/rs.3.rs-2373131/v1
https://doi.org/10.21203/rs.3.rs-2373131/v1
Autor:
Meenakshi Kaushal, Q. M. Danish Lohani
Publikováno v:
Granular Computing. 7:183-195
In real-world scenario, mostly, the datasets are either imprecise or uncertain in their original form. Due to this reason, the clustering of such datasets is unsatisfactory and we often get compromised results. The information present in the dataset
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::499a1992d1cd5a6b2b9a143992d97459
https://doi.org/10.1007/s41066-021-00259-1
https://doi.org/10.1007/s41066-021-00259-1
Autor:
Meenakshi Kaushal, Q.M. Danish Lohani
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::385fa5564dcd267b628c5e9104264a31
https://doi.org/10.2139/ssrn.4158293
https://doi.org/10.2139/ssrn.4158293
Publikováno v:
FUZZ-IEEE
A Fuzzy Data Envelopment Analysis (FDEA) is a popular technique to measure the relative efficiency of a Decision Making Unit (DMU) with respect to other DMUs under uncertain/imprecise information represented in form of fuzzy input and fuzzy output. H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1fec4ecbcea15058e4aa1dddef3532f1
https://doi.org/10.1109/fuzz45933.2021.9494600
https://doi.org/10.1109/fuzz45933.2021.9494600
Publikováno v:
Proceedings of International Joint Conference on Computational Intelligence ISBN: 9789811375637
IJCCI
IJCCI
In real-world scenario, the information about the process is incomplete and imprecise. The Atanassov Intuitionistic Fuzzy Set (AIFS) is a powerful and flexible tool to handle such uncertainty in a system efficiently. The first two components of AIFS,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::450dd866f998fa2d182daa1a22546133
https://doi.org/10.1007/978-981-13-7564-4_61
https://doi.org/10.1007/978-981-13-7564-4_61
A novel distance measure over intuitionistic fuzzy sets with its applications in Pattern Recognition
Publikováno v:
SSCI
In this paper, we propose a novel distance measure that relies on noble idea of implementing probability over IFSs. The probability for membership value, non-membership value and hesitation part could be appropriately determined through the methodolo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fbc91db7e32d8cb221caf00a2c4b7aae
https://doi.org/10.1109/ssci.2018.8628798
https://doi.org/10.1109/ssci.2018.8628798
Publikováno v:
FUZZ-IEEE
We often have many datasets where hard clustering algorithms do not deliver satisfactory clustering results. It is found that many times fuzzy clustering technique improves the clustering results obtained by hard clustering algorithms. Fuzzy c-means
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb50f200f8dd61f902869485488ea245
https://doi.org/10.1109/fuzz-ieee.2018.8491602
https://doi.org/10.1109/fuzz-ieee.2018.8491602