Exploiting fuzzy weights in CNN model-based taxonomic classification of 500-bp sequence bacterial dataset.

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Bibliographic Details
Title: Exploiting fuzzy weights in CNN model-based taxonomic classification of 500-bp sequence bacterial dataset.
Authors: Algarni AD; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia., Abd El-Samie FE; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia., Soliman NF; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia., Emara HM; Department of Electronics and Electrical Communications Engineering, Ministry of Higher Education Pyramids Higher Institute (PHI) for Engineering and Technology, 6th of October City, Giza, 3220412, Egypt., Algarni F; Ministry of Education, Riyadh, Saudi Arabia., Abd-Alhalem SM; Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, 35712, Egypt. samia.mohammed@deltauniv.edu.eg., Marie HS; Faculty of Artificial Intelligence, Delta University for Science and Technology, Gamasa, 35712, Egypt.
Source: Scientific reports [Sci Rep] 2025 Dec 23; Vol. 15 (1), pp. 44733. Date of Electronic Publication: 2025 Dec 23.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-025-24836-5