The Flowchart of the Hybrid GWO-BBOA Optimization Process.
<p>The Flowchart of the Hybrid GWO-BBOA Optimization Process.</p>
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2025
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| _version_ | 1852015982953889792 |
|---|---|
| author | Ali Abbas Abbod (22386097) |
| author2 | Matheel E. Abdulmunim (22386100) Ismail A. Mageed (22052111) |
| author2_role | author author |
| author_facet | Ali Abbas Abbod (22386097) Matheel E. Abdulmunim (22386100) Ismail A. Mageed (22052111) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ali Abbas Abbod (22386097) Matheel E. Abdulmunim (22386100) Ismail A. Mageed (22052111) |
| dc.date.none.fl_str_mv | 2025-10-07T17:28:39Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0333374.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_Flowchart_of_the_Hybrid_GWO-BBOA_Optimization_Process_/30297799 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified time surveillance systems grey wolf optimization global search capability experimental results show enhancing dl models effective hyperparameter optimization demonstrated strong potential bboa optimization algorithm 182 annotated images time object detection hybrid metaheuristic approaches model &# 8217 hybrid gwo arson detection work highlights tuning strength study proposes risk environments required iterations recent advances public areas protecting lives proposed gwo optimizing hyperparameters industrial zones highly dependent future work evaluated using enhanced ability deep learning critical role computational efficiency |
| dc.title.none.fl_str_mv | The Flowchart of the Hybrid GWO-BBOA Optimization Process. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The Flowchart of the Hybrid GWO-BBOA Optimization Process.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ea2600a3a96dafa1aedb4d1fedc34ded |
| identifier_str_mv | 10.1371/journal.pone.0333374.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30297799 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The Flowchart of the Hybrid GWO-BBOA Optimization Process.Ali Abbas Abbod (22386097)Matheel E. Abdulmunim (22386100)Ismail A. Mageed (22052111)Space ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtime surveillance systemsgrey wolf optimizationglobal search capabilityexperimental results showenhancing dl modelseffective hyperparameter optimizationdemonstrated strong potentialbboa optimization algorithm182 annotated imagestime object detectionhybrid metaheuristic approachesmodel &# 8217hybrid gwoarson detectionwork highlightstuning strengthstudy proposesrisk environmentsrequired iterationsrecent advancespublic areasprotecting livesproposed gwooptimizing hyperparametersindustrial zoneshighly dependentfuture workevaluated usingenhanced abilitydeep learningcritical rolecomputational efficiency<p>The Flowchart of the Hybrid GWO-BBOA Optimization Process.</p>2025-10-07T17:28:39ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0333374.g005https://figshare.com/articles/figure/The_Flowchart_of_the_Hybrid_GWO-BBOA_Optimization_Process_/30297799CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302977992025-10-07T17:28:39Z |
| spellingShingle | The Flowchart of the Hybrid GWO-BBOA Optimization Process. Ali Abbas Abbod (22386097) Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified time surveillance systems grey wolf optimization global search capability experimental results show enhancing dl models effective hyperparameter optimization demonstrated strong potential bboa optimization algorithm 182 annotated images time object detection hybrid metaheuristic approaches model &# 8217 hybrid gwo arson detection work highlights tuning strength study proposes risk environments required iterations recent advances public areas protecting lives proposed gwo optimizing hyperparameters industrial zones highly dependent future work evaluated using enhanced ability deep learning critical role computational efficiency |
| status_str | publishedVersion |
| title | The Flowchart of the Hybrid GWO-BBOA Optimization Process. |
| title_full | The Flowchart of the Hybrid GWO-BBOA Optimization Process. |
| title_fullStr | The Flowchart of the Hybrid GWO-BBOA Optimization Process. |
| title_full_unstemmed | The Flowchart of the Hybrid GWO-BBOA Optimization Process. |
| title_short | The Flowchart of the Hybrid GWO-BBOA Optimization Process. |
| title_sort | The Flowchart of the Hybrid GWO-BBOA Optimization Process. |
| topic | Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified time surveillance systems grey wolf optimization global search capability experimental results show enhancing dl models effective hyperparameter optimization demonstrated strong potential bboa optimization algorithm 182 annotated images time object detection hybrid metaheuristic approaches model &# 8217 hybrid gwo arson detection work highlights tuning strength study proposes risk environments required iterations recent advances public areas protecting lives proposed gwo optimizing hyperparameters industrial zones highly dependent future work evaluated using enhanced ability deep learning critical role computational efficiency |