A Fuzzy Neural Network Approach for Nanomaterials Analysis in Nanoelectronics Under Fuzzy Credibility Information
Abstract Nanomaterials are a key component of nanoelectronics and selecting the most suitable nanomaterial for nanoelectronics remains a significant challenge for companies. The classical decision making process is often difficult and uncertain when identifying the ideal nanomaterial. To address thi...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00938-w |
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| _version_ | 1849331770733887488 |
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| author | Ihsan Ullah Saleem Abdullah Marya Nawaz Hameed Gul Ahmadzai |
| author_facet | Ihsan Ullah Saleem Abdullah Marya Nawaz Hameed Gul Ahmadzai |
| author_sort | Ihsan Ullah |
| collection | DOAJ |
| description | Abstract Nanomaterials are a key component of nanoelectronics and selecting the most suitable nanomaterial for nanoelectronics remains a significant challenge for companies. The classical decision making process is often difficult and uncertain when identifying the ideal nanomaterial. To address this, we develop a novel decision making model based on a fuzzy credibility neural network with Hamacher aggregation operators. We apply the proposed model to select the most suitable nanomaterial for nanoelectronics. For this purpose, we collect information matrices from three experts regarding various nanomaterials. To analyze these matrices, we use entropy to calculate the weights of each criterion. After determining the weights, we apply fuzzy credibility Hamacher weighted aggregation operators to combine the input signals and their corresponding weights in order to compute the hidden layer information for the nanomaterials. To ensure accurate and reliable results, we apply the fuzzy credibility Hamacher weighted aggregation operator once again to the hidden layer information, aggregating it with the appropriate weights to generate the output layer information. Next, we use a score function based on fuzzy credibility numbers to calculate the score values of the output information. After this, we apply three activation functions to compute the final output of the proposed model. Based on the results, graphene is identified as the best nanomaterial for nanoelectronics. Furthermore, we perform a sensitivity analysis of the proposed model by varying the Hamacher parameter. To confirm the effectiveness and accuracy of the proposed approach, we finally validate the results using three well-known MCDM methods. |
| format | Article |
| id | doaj-art-d3aeb4738e7048bc9138a63c739d580a |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-d3aeb4738e7048bc9138a63c739d580a2025-08-20T03:46:24ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-08-0118112310.1007/s44196-025-00938-wA Fuzzy Neural Network Approach for Nanomaterials Analysis in Nanoelectronics Under Fuzzy Credibility InformationIhsan Ullah0Saleem Abdullah1Marya Nawaz2Hameed Gul Ahmadzai3Department of Mathematics, Abdul Wali Khan University MardanDepartment of Mathematics, Abdul Wali Khan University MardanDepartment of Mathematics, Abdul Wali Khan University MardanFaculty of Education, Department of Mathematics, Paktia UniversityAbstract Nanomaterials are a key component of nanoelectronics and selecting the most suitable nanomaterial for nanoelectronics remains a significant challenge for companies. The classical decision making process is often difficult and uncertain when identifying the ideal nanomaterial. To address this, we develop a novel decision making model based on a fuzzy credibility neural network with Hamacher aggregation operators. We apply the proposed model to select the most suitable nanomaterial for nanoelectronics. For this purpose, we collect information matrices from three experts regarding various nanomaterials. To analyze these matrices, we use entropy to calculate the weights of each criterion. After determining the weights, we apply fuzzy credibility Hamacher weighted aggregation operators to combine the input signals and their corresponding weights in order to compute the hidden layer information for the nanomaterials. To ensure accurate and reliable results, we apply the fuzzy credibility Hamacher weighted aggregation operator once again to the hidden layer information, aggregating it with the appropriate weights to generate the output layer information. Next, we use a score function based on fuzzy credibility numbers to calculate the score values of the output information. After this, we apply three activation functions to compute the final output of the proposed model. Based on the results, graphene is identified as the best nanomaterial for nanoelectronics. Furthermore, we perform a sensitivity analysis of the proposed model by varying the Hamacher parameter. To confirm the effectiveness and accuracy of the proposed approach, we finally validate the results using three well-known MCDM methods.https://doi.org/10.1007/s44196-025-00938-wFuzzy credibility neural networkHamacher aggregation operatorDecision making modelNanomaterial |
| spellingShingle | Ihsan Ullah Saleem Abdullah Marya Nawaz Hameed Gul Ahmadzai A Fuzzy Neural Network Approach for Nanomaterials Analysis in Nanoelectronics Under Fuzzy Credibility Information International Journal of Computational Intelligence Systems Fuzzy credibility neural network Hamacher aggregation operator Decision making model Nanomaterial |
| title | A Fuzzy Neural Network Approach for Nanomaterials Analysis in Nanoelectronics Under Fuzzy Credibility Information |
| title_full | A Fuzzy Neural Network Approach for Nanomaterials Analysis in Nanoelectronics Under Fuzzy Credibility Information |
| title_fullStr | A Fuzzy Neural Network Approach for Nanomaterials Analysis in Nanoelectronics Under Fuzzy Credibility Information |
| title_full_unstemmed | A Fuzzy Neural Network Approach for Nanomaterials Analysis in Nanoelectronics Under Fuzzy Credibility Information |
| title_short | A Fuzzy Neural Network Approach for Nanomaterials Analysis in Nanoelectronics Under Fuzzy Credibility Information |
| title_sort | fuzzy neural network approach for nanomaterials analysis in nanoelectronics under fuzzy credibility information |
| topic | Fuzzy credibility neural network Hamacher aggregation operator Decision making model Nanomaterial |
| url | https://doi.org/10.1007/s44196-025-00938-w |
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