{"id":"https://openalex.org/W7225637717","doi":"https://doi.org/10.5281/zenodo.24078673","title":"Human Behavior Recognition using MCNN","display_name":"Human Behavior Recognition using MCNN","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225637717","doi":"https://doi.org/10.5281/zenodo.24078673"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24078673","doi":"https://doi.org/10.5281/zenodo.24078673","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24078673","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24078673","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161315746","display_name":"R.  Aishwarya","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210104573","display_name":"CMR University","ror":"https://ror.org/015waqy33","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210104573","https://openalex.org/I4405282222"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"R.  Aishwarya","raw_affiliation_strings":["CMR Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMR Institute of Technology","institution_ids":["https://openalex.org/I4210104573"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161312472","display_name":"S.  Trisha","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S.  Trisha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5161358669","display_name":"V.  Sajeev","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"V.  Sajeev","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.002400000113993883,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.001500000013038516,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/human-behavior-recognition","display_name":"human behavior recognition","score":0.9660000205039978},{"id":"https://openalex.org/keywords/channel-attention","display_name":"channel attention","score":0.6660000085830688},{"id":"https://openalex.org/keywords/depthwise-separable-convolution","display_name":"depthwise separable convolution","score":0.9909999966621399},{"id":"https://openalex.org/keywords/multiscale-convolutional-neural-network","display_name":"multi-scale convolutional neural network","score":0.9490000009536743},{"id":"https://openalex.org/keywords/3d-convolutional-neural-networks","display_name":"3D convolutional neural networks","score":0.8339999914169312},{"id":"https://openalex.org/keywords/spatiotemporal-feature-extraction","display_name":"spatiotemporal feature extraction","score":0.8579999804496765},{"id":"https://openalex.org/keywords/feature-transfer","display_name":"feature transfer","score":0.7770000100135803},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.6620000004768372}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7267000079154968},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7153000235557556},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6690000295639038},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.6464999914169312},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6322000026702881},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5777000188827515},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.576200008392334},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5220000147819519},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.49950000643730164},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.48739999532699585},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4593999981880188},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.459199994802475},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.35749998688697815},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.29089999198913574},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2750000059604645},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.2621000111103058}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24078673","doi":"https://doi.org/10.5281/zenodo.24078673","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24078673","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},{"id":"doi:10.5281/zenodo.24078674","doi":"https://doi.org/10.5281/zenodo.24078674","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24078674","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24078673","doi":"https://doi.org/10.5281/zenodo.24078673","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24078673","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4297358995","https://openalex.org/W7147730967","https://openalex.org/W7217950228","https://openalex.org/W4405248297","https://openalex.org/W4402691297","https://openalex.org/W4408747690","https://openalex.org/W7162109431","https://openalex.org/W4400945898","https://openalex.org/W3110067803","https://openalex.org/W7094999193"],"abstract_inverted_index":{"Abstract":[0],"-":[1],"Human":[2],"behavior":[3,103],"recognition":[4,168],"needs":[5],"an":[6],"efficient":[7],"spatiotemporal":[8],"feature":[9,65,122,139],"extraction":[10],"and":[11,25,50,92,150],"classification":[12,158],"model.":[13],"However,":[14],"the":[15,110,151,155,182],"current":[16],"channel":[17,39],"attention":[18,40],"mechanism":[19],"depends":[20],"on":[21,97,147],"global":[22],"average":[23],"pooling":[24],"ignores":[26],"local":[27],"spatial":[28],"information.":[29],"To":[30],"overcome":[31],"this":[32,34],"issue,":[33],"paper":[35],"proposes":[36],"two":[37,119],"enhanced":[38],"modules:":[41],"a":[42,51],"Space-Time":[43],"(ST)":[44],"interaction":[45],"module":[46],"using":[47,74],"matrix":[48],"computation":[49],"depth-wise":[52],"separable":[53],"convolution":[54],"module.":[55],"A":[56],"multi-scale":[57],"convolutional":[58],"neural":[59],"network":[60,135],"is":[61,87,95,176],"constructed":[62],"to":[63,100,113,127],"improve":[64],"representation":[66],"with":[67,160,181],"lower":[68,161],"computational":[69,162],"complexity.":[70,163],"Here":[71],"we":[72],"are":[73,107,125],"3D":[75],"CNN":[76],"for":[77,121],"more":[78,177],"accurate":[79,178],"predictions":[80],"or":[81],"recognition.":[82],"The":[83,105,141,164],"input":[84],"image":[85,116],"dataset":[86],"divided":[88],"into":[89],"multiple":[90],"segments,":[91],"low-rank":[93],"learning":[94],"performed":[96],"each":[98],"segment":[99],"extract":[101],"concise":[102],"features.":[104],"features":[106,131],"stacked":[108],"along":[109],"temporal":[111],"dimension":[112],"acquire":[114],"comprehensive":[115],"representations.":[117],"Moreover,":[118],"functions":[120],"difference":[123],"measurement":[124],"presented":[126],"eliminate":[128],"differences":[129],"in":[130],"derived":[132],"from":[133],"different":[134],"architectures,":[136],"facilitating":[137],"effective":[138],"transfer.":[140],"proposed":[142,165],"approach":[143,156,175],"has":[144],"been":[145],"evaluated":[146],"public":[148],"benchmarks,":[149],"results":[152],"demonstrate":[153],"that":[154],"improves":[157,167],"accuracy":[159,169],"model":[166],"while":[170],"preserving":[171],"structural":[172],"compactness.":[173],"This":[174],"when":[179],"compared":[180],"existing":[183],"approaches.":[184]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}
