Bar chart comparing the average fitness values of various feature selection algorithms.
<p>Bar chart comparing the average fitness values of various feature selection algorithms.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
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| _version_ | 1852017151656853504 |
|---|---|
| author | Doaa Sami Khafaga (21463870) |
| author2 | El-Sayed M. El-kenawy (14581088) Faris H. Rizk (21755909) Marwa M. Eid (11251630) Ehsaneh Khodadadi (22146329) Nima Khodadadi (14581091) |
| author2_role | author author author author author |
| author_facet | Doaa Sami Khafaga (21463870) El-Sayed M. El-kenawy (14581088) Faris H. Rizk (21755909) Marwa M. Eid (11251630) Ehsaneh Khodadadi (22146329) Nima Khodadadi (14581091) |
| author_role | author |
| dc.creator.none.fl_str_mv | Doaa Sami Khafaga (21463870) El-Sayed M. El-kenawy (14581088) Faris H. Rizk (21755909) Marwa M. Eid (11251630) Ehsaneh Khodadadi (22146329) Nima Khodadadi (14581091) |
| dc.date.none.fl_str_mv | 2025-08-29T18:04:21Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0330228.g013 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Bar_chart_comparing_the_average_fitness_values_of_various_feature_selection_algorithms_/30014834 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Sociology Hematology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified false negative rate effective feature selection driven deep learning deep learning models contributions include developing clinical diagnostics quantifiably average classification error automated cytology technology optimization also results metaheuristic optimization techniques enhancing cnn performance ocoa ), designed increases cnn accuracy ocotillo optimization hyperparameter optimization study introduces significantly reduced sensitivity increasing maximum accuracy innovative bio initial accuracy highly dependent findings highlight expert knowledge critical scenarios continuous version binary variant accurate solution 61 %), 48 %. 24 %. (+ 12 (+ 11 |
| dc.title.none.fl_str_mv | Bar chart comparing the average fitness values of various feature selection algorithms. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Bar chart comparing the average fitness values of various feature selection algorithms.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_fbc797b104fec2f4de374464083f7bea |
| identifier_str_mv | 10.1371/journal.pone.0330228.g013 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30014834 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Bar chart comparing the average fitness values of various feature selection algorithms.Doaa Sami Khafaga (21463870)El-Sayed M. El-kenawy (14581088)Faris H. Rizk (21755909)Marwa M. Eid (11251630)Ehsaneh Khodadadi (22146329)Nima Khodadadi (14581091)BiotechnologySociologyHematologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedfalse negative rateeffective feature selectiondriven deep learningdeep learning modelscontributions include developingclinical diagnostics quantifiablyaverage classification errorautomated cytology technologyoptimization also resultsmetaheuristic optimization techniquesenhancing cnn performanceocoa ), designedincreases cnn accuracyocotillo optimizationhyperparameter optimizationstudy introducessignificantly reducedsensitivity increasingmaximum accuracyinnovative bioinitial accuracyhighly dependentfindings highlightexpert knowledgecritical scenarioscontinuous versionbinary variantaccurate solution61 %),48 %.24 %.(+ 12(+ 11<p>Bar chart comparing the average fitness values of various feature selection algorithms.</p>2025-08-29T18:04:21ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0330228.g013https://figshare.com/articles/figure/Bar_chart_comparing_the_average_fitness_values_of_various_feature_selection_algorithms_/30014834CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300148342025-08-29T18:04:21Z |
| spellingShingle | Bar chart comparing the average fitness values of various feature selection algorithms. Doaa Sami Khafaga (21463870) Biotechnology Sociology Hematology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified false negative rate effective feature selection driven deep learning deep learning models contributions include developing clinical diagnostics quantifiably average classification error automated cytology technology optimization also results metaheuristic optimization techniques enhancing cnn performance ocoa ), designed increases cnn accuracy ocotillo optimization hyperparameter optimization study introduces significantly reduced sensitivity increasing maximum accuracy innovative bio initial accuracy highly dependent findings highlight expert knowledge critical scenarios continuous version binary variant accurate solution 61 %), 48 %. 24 %. (+ 12 (+ 11 |
| status_str | publishedVersion |
| title | Bar chart comparing the average fitness values of various feature selection algorithms. |
| title_full | Bar chart comparing the average fitness values of various feature selection algorithms. |
| title_fullStr | Bar chart comparing the average fitness values of various feature selection algorithms. |
| title_full_unstemmed | Bar chart comparing the average fitness values of various feature selection algorithms. |
| title_short | Bar chart comparing the average fitness values of various feature selection algorithms. |
| title_sort | Bar chart comparing the average fitness values of various feature selection algorithms. |
| topic | Biotechnology Sociology Hematology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified false negative rate effective feature selection driven deep learning deep learning models contributions include developing clinical diagnostics quantifiably average classification error automated cytology technology optimization also results metaheuristic optimization techniques enhancing cnn performance ocoa ), designed increases cnn accuracy ocotillo optimization hyperparameter optimization study introduces significantly reduced sensitivity increasing maximum accuracy innovative bio initial accuracy highly dependent findings highlight expert knowledge critical scenarios continuous version binary variant accurate solution 61 %), 48 %. 24 %. (+ 12 (+ 11 |