An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs
[EN] The networks of various problems have competing constituents, and there is a concern to compute the strength of competition among these entities. Competition hypergraphs capture all groups of predators that are competing in a community through their hyperedges. This paper reintroduces competiti...
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Online Access: | http://hdl.handle.net/10366/149783 https://doi.org/10.1007/s00521-021-06414-8 |
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ftunivsalamanca:oai:gredos.usal.es:10366/149783 2023-05-15T15:43:39+02:00 An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs Nawaz, Hafiza Saba Akram, Muhammad Rodríguez Alcantud, José Carlos 2021 application/pdf http://hdl.handle.net/10366/149783 https://doi.org/10.1007/s00521-021-06414-8 eng eng Springerlink https://doi.org/10.1007/s00521-021-06414-8 Nawaz, H.S., Akram, M. & Alcantud, J.C.R. (2022). An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs. Neural Comput & Applic 34, 1099–1121. https://doi.org/10.1007/s00521-021-06414-8 0941-0643 http://hdl.handle.net/10366/149783 doi:10.1007/s00521-021-06414-8 1433-3058 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess CC-BY-NC-ND Pythagorean fuzzy sets Hypergraphs Predator-prey interactions Algorithm 12 Matemáticas 1203.02 Lenguajes Algorítmicos info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2021 ftunivsalamanca https://doi.org/10.1007/s00521-021-06414-8 2022-05-24T23:13:36Z [EN] The networks of various problems have competing constituents, and there is a concern to compute the strength of competition among these entities. Competition hypergraphs capture all groups of predators that are competing in a community through their hyperedges. This paper reintroduces competition hypergraphs in the context of Pythagorean fuzzy set theory, thereby producing Pythagorean fuzzy competition hypergraphs. The data of real-world ecological systems posses uncertainty, and the proposed hypergraphs can efficiently deal with such information to model wide range of competing interactions. We suggest several extensions of Pythagorean fuzzy competition hypergraphs, including Pythagorean fuzzy economic competition hypergraphs, Pythagorean fuzzy row as well as column hypergraphs, Pythagorean fuzzy k-competition hypergraphs, m-step Pythagorean fuzzy competition hypergraphs and Pythagorean fuzzy neighborhood hypergraphs. The proposed graphical structures are good tools to measure the strength of direct and indirect competing and non-competing interactions. Their aptness is illustrated through examples, and results support their intrinsic interest. We propose algorithms that help to compose some of the presented graphical structures. We consider predator-prey interactions among organisms of the Bering Sea as an application: Pythagorean fuzzy competition hypergraphs encapsulate the competing relationships among its inhabitants. Specifically, the algorithm which constructs the Pythagorean fuzzy competition hypergraphs can also compute the strength of competing and non-competing relations of this scenario. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE Article in Journal/Newspaper Bering Sea Universidad de Salamanca: Gredos (Gestión del Repositorio Documental de la Universidad de Salamanca) Bering Sea Neural Computing and Applications 34 2 1099 1121 |
institution |
Open Polar |
collection |
Universidad de Salamanca: Gredos (Gestión del Repositorio Documental de la Universidad de Salamanca) |
op_collection_id |
ftunivsalamanca |
language |
English |
topic |
Pythagorean fuzzy sets Hypergraphs Predator-prey interactions Algorithm 12 Matemáticas 1203.02 Lenguajes Algorítmicos |
spellingShingle |
Pythagorean fuzzy sets Hypergraphs Predator-prey interactions Algorithm 12 Matemáticas 1203.02 Lenguajes Algorítmicos Nawaz, Hafiza Saba Akram, Muhammad Rodríguez Alcantud, José Carlos An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs |
topic_facet |
Pythagorean fuzzy sets Hypergraphs Predator-prey interactions Algorithm 12 Matemáticas 1203.02 Lenguajes Algorítmicos |
description |
[EN] The networks of various problems have competing constituents, and there is a concern to compute the strength of competition among these entities. Competition hypergraphs capture all groups of predators that are competing in a community through their hyperedges. This paper reintroduces competition hypergraphs in the context of Pythagorean fuzzy set theory, thereby producing Pythagorean fuzzy competition hypergraphs. The data of real-world ecological systems posses uncertainty, and the proposed hypergraphs can efficiently deal with such information to model wide range of competing interactions. We suggest several extensions of Pythagorean fuzzy competition hypergraphs, including Pythagorean fuzzy economic competition hypergraphs, Pythagorean fuzzy row as well as column hypergraphs, Pythagorean fuzzy k-competition hypergraphs, m-step Pythagorean fuzzy competition hypergraphs and Pythagorean fuzzy neighborhood hypergraphs. The proposed graphical structures are good tools to measure the strength of direct and indirect competing and non-competing interactions. Their aptness is illustrated through examples, and results support their intrinsic interest. We propose algorithms that help to compose some of the presented graphical structures. We consider predator-prey interactions among organisms of the Bering Sea as an application: Pythagorean fuzzy competition hypergraphs encapsulate the competing relationships among its inhabitants. Specifically, the algorithm which constructs the Pythagorean fuzzy competition hypergraphs can also compute the strength of competing and non-competing relations of this scenario. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCLE |
format |
Article in Journal/Newspaper |
author |
Nawaz, Hafiza Saba Akram, Muhammad Rodríguez Alcantud, José Carlos |
author_facet |
Nawaz, Hafiza Saba Akram, Muhammad Rodríguez Alcantud, José Carlos |
author_sort |
Nawaz, Hafiza Saba |
title |
An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs |
title_short |
An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs |
title_full |
An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs |
title_fullStr |
An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs |
title_full_unstemmed |
An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs |
title_sort |
algorithm to compute the strength of competing interactions in the bering sea based on pythagorean fuzzy hypergraphs |
publisher |
Springerlink |
publishDate |
2021 |
url |
http://hdl.handle.net/10366/149783 https://doi.org/10.1007/s00521-021-06414-8 |
geographic |
Bering Sea |
geographic_facet |
Bering Sea |
genre |
Bering Sea |
genre_facet |
Bering Sea |
op_relation |
https://doi.org/10.1007/s00521-021-06414-8 Nawaz, H.S., Akram, M. & Alcantud, J.C.R. (2022). An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs. Neural Comput & Applic 34, 1099–1121. https://doi.org/10.1007/s00521-021-06414-8 0941-0643 http://hdl.handle.net/10366/149783 doi:10.1007/s00521-021-06414-8 1433-3058 |
op_rights |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.1007/s00521-021-06414-8 |
container_title |
Neural Computing and Applications |
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34 |
container_issue |
2 |
container_start_page |
1099 |
op_container_end_page |
1121 |
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1766377833309929472 |