Statistical significance analysis of performance gains.
<p>Statistical significance analysis of performance gains.</p>
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2025
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| _version_ | 1852020580377690112 |
|---|---|
| author | Wenhao Ren (2561731) |
| author2 | Zuowei Zhong (21300382) |
| author2_role | author |
| author_facet | Wenhao Ren (2561731) Zuowei Zhong (21300382) |
| author_role | author |
| dc.creator.none.fl_str_mv | Wenhao Ren (2561731) Zuowei Zhong (21300382) |
| dc.date.none.fl_str_mv | 2025-05-09T17:37:11Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0321640.t014 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Statistical_significance_analysis_of_performance_gains_/28992592 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Biotechnology Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> developing varying crack sizes irregular convolution operations hierarchical backbone network handle complex backgrounds global contextual information ensuring structural integrity benchmark dataset demonstrate reducing computational overhead optimizes feature extraction automatic crack detection model &# 8217 novel lightweight approach detecting building cracks proposed approach novel integration feature fusion computational complexity building structures accuracy detection model achieves baseline model detecting micro study introduces results highlight precise localization practical applicability integrating local experimental results convolutional module available datasets attention mechanisms attention mechanism accurate algorithm |
| dc.title.none.fl_str_mv | Statistical significance analysis of performance gains. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Statistical significance analysis of performance gains.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_bc2ce1d42a014a4fa3a4c3f031df8f75 |
| identifier_str_mv | 10.1371/journal.pone.0321640.t014 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28992592 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Statistical significance analysis of performance gains.Wenhao Ren (2561731)Zuowei Zhong (21300382)BiochemistryBiotechnologySociologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> developingvarying crack sizesirregular convolution operationshierarchical backbone networkhandle complex backgroundsglobal contextual informationensuring structural integritybenchmark dataset demonstratereducing computational overheadoptimizes feature extractionautomatic crack detectionmodel &# 8217novel lightweight approachdetecting building cracksproposed approachnovel integrationfeature fusioncomputational complexitybuilding structuresaccuracy detectionmodel achievesbaseline modeldetecting microstudy introducesresults highlightprecise localizationpractical applicabilityintegrating localexperimental resultsconvolutional moduleavailable datasetsattention mechanismsattention mechanismaccurate algorithm<p>Statistical significance analysis of performance gains.</p>2025-05-09T17:37:11ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0321640.t014https://figshare.com/articles/dataset/Statistical_significance_analysis_of_performance_gains_/28992592CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/289925922025-05-09T17:37:11Z |
| spellingShingle | Statistical significance analysis of performance gains. Wenhao Ren (2561731) Biochemistry Biotechnology Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> developing varying crack sizes irregular convolution operations hierarchical backbone network handle complex backgrounds global contextual information ensuring structural integrity benchmark dataset demonstrate reducing computational overhead optimizes feature extraction automatic crack detection model &# 8217 novel lightweight approach detecting building cracks proposed approach novel integration feature fusion computational complexity building structures accuracy detection model achieves baseline model detecting micro study introduces results highlight precise localization practical applicability integrating local experimental results convolutional module available datasets attention mechanisms attention mechanism accurate algorithm |
| status_str | publishedVersion |
| title | Statistical significance analysis of performance gains. |
| title_full | Statistical significance analysis of performance gains. |
| title_fullStr | Statistical significance analysis of performance gains. |
| title_full_unstemmed | Statistical significance analysis of performance gains. |
| title_short | Statistical significance analysis of performance gains. |
| title_sort | Statistical significance analysis of performance gains. |
| topic | Biochemistry Biotechnology Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> developing varying crack sizes irregular convolution operations hierarchical backbone network handle complex backgrounds global contextual information ensuring structural integrity benchmark dataset demonstrate reducing computational overhead optimizes feature extraction automatic crack detection model &# 8217 novel lightweight approach detecting building cracks proposed approach novel integration feature fusion computational complexity building structures accuracy detection model achieves baseline model detecting micro study introduces results highlight precise localization practical applicability integrating local experimental results convolutional module available datasets attention mechanisms attention mechanism accurate algorithm |