{"id":"https://openalex.org/W4412761649","doi":"https://doi.org/10.32604/cmc.2025.066343","title":"Enhancing Classroom Behavior Recognition with Lightweight Multi-Scale Feature Fusion","display_name":"Enhancing Classroom Behavior Recognition with Lightweight Multi-Scale Feature Fusion","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412761649","doi":"https://doi.org/10.32604/cmc.2025.066343"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.066343","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066343","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.066343","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101414898","display_name":"Chuanchuan Wang","orcid":"https://orcid.org/0009-0001-8061-5368"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chuanchuan Wang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073889249","display_name":"Ahmad Sufril Azlan Mohamed","orcid":"https://orcid.org/0000-0002-2838-0872"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmad Sufril Azlan Mohamed","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115589397","display_name":"Xiao Yang","orcid":"https://orcid.org/0009-0001-0138-155X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Yang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100397061","display_name":"Hao Zhang","orcid":"https://orcid.org/0000-0003-3877-7512"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107748233","display_name":"Xiang Li","orcid":"https://orcid.org/0009-0005-2559-687X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058270969","display_name":"Mohd Halim Bin Mohd Noor","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohd Halim Bin Mohd Noor","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101414898"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21371738,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"85","issue":"1","first_page":"855","last_page":"874"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12222","display_name":"IoT-based Smart Home Systems","score":0.8658000230789185,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12222","display_name":"IoT-based Smart Home Systems","score":0.8658000230789185,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.8263999819755554,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.7501999735832214,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/scale","display_name":"Scale (ratio)","score":0.5677804946899414},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5575333833694458},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5382416248321533},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.500903844833374},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.476940780878067},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4364498555660248},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.09410473704338074},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07755544781684875}],"concepts":[{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5677804946899414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5575333833694458},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5382416248321533},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.500903844833374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.476940780878067},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4364498555660248},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09410473704338074},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07755544781684875},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.066343","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066343","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.066343","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.066343","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials &amp; Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2901514867","https://openalex.org/W3093322018","https://openalex.org/W3143820543","https://openalex.org/W4284712931","https://openalex.org/W4380149082","https://openalex.org/W4391560474","https://openalex.org/W4394717425","https://openalex.org/W4408858353","https://openalex.org/W4410401479","https://openalex.org/W4415795831"],"related_works":["https://openalex.org/W3147584709","https://openalex.org/W2099421762","https://openalex.org/W2530546662","https://openalex.org/W2967030268","https://openalex.org/W2977677679","https://openalex.org/W2185253430","https://openalex.org/W1992327129","https://openalex.org/W4210345652","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Classroom":[0],"behavior":[1,25,234],"recognition":[2,26,32],"is":[3,66,243],"a":[4,10,67],"hot":[5],"research":[6],"topic,":[7],"which":[8],"plays":[9],"vital":[11],"role":[12],"in":[13,104,115,235],"assessing":[14],"and":[15,44,69,127,139,145,160,189,213,247],"improving":[16],"the":[17,60,88,101,105,110,116,120,135,141,146,157,169,172,186,195,207,215,221],"quality":[18],"of":[19,112,143,148],"classroom":[20,24,237,250],"teaching.":[21],"However,":[22],"existing":[23],"methods":[27],"have":[28],"challenges":[29],"for":[30,73,245],"high":[31],"accuracy":[33,178],"with":[34,36,41,96,227],"datasets":[35],"problems":[37],"such":[38],"as":[39],"scenes":[40],"blurred":[42],"pictures,":[43],"inconsistent":[45],"objects.":[46],"To":[47],"address":[48,109],"this":[49],"challenge,":[50],"we":[51,118],"proposed":[52],"an":[53],"effective,":[54],"lightweight":[55,68],"object":[56],"detector":[57],"method":[58,209],"called":[59],"RFNet":[61,173,199,242],"model":[62,174],"(YOLO-FR).":[63],"The":[64,198],"YOLO-FR":[65],"effective":[70,78],"model.":[71],"Specifically,":[72],"efficient":[74],"multi-scale":[75,113],"feature":[76,79,89],"extraction,":[77],"pyramid":[80],"shared":[81],"convolutional":[82,94],"(FPSC)":[83],"was":[84],"designed":[85],"to":[86,108,133,168,183,192],"improve":[87,134],"extract":[90],"performance":[91,137],"by":[92,152],"leveraging":[93],"layers":[95],"varying":[97],"dilation":[98],"rates":[99],"from":[100,181,190],"input":[102],"image":[103],"backbone.":[106],"Secondly,":[107],"problem":[111],"variability":[114],"scene,":[117],"design":[119],"Rep":[121],"Ghost":[122],"fusion":[123],"Cross":[124],"Stage":[125],"Partial":[126],"Efficient":[128],"Layer":[129],"Aggregation":[130],"Network":[131],"(RGCSPELAN)":[132],"network":[136],"further":[138],"reduce":[140],"amount":[142],"computation":[144],"number":[147],"parameters.":[149],"In":[150],"addition,":[151],"conducting":[153],"experimental":[154],"valuation":[155],"on":[156,185,194,220],"SCB":[158,187,222],"dataset3":[159,188],"STBD-08":[161,196],"dataset.":[162,197],"Experimental":[163],"results":[164,239],"indicate":[165],"that,":[166],"compared":[167],"baseline":[170,208],"model,":[171],"has":[175,201],"increased":[176],"mean":[177],"precision":[179,203],"(mAP@50)":[180],"69.6%":[182],"71.0%":[184],"91.8%":[191],"93.1%":[193],"approach":[200],"effectiveness":[202],"at":[204,211],"68.6%,":[205],"surpassing":[206],"(YOLOv11)":[210],"3.3%":[212],"archieve":[214],"minimal":[216],"size":[217],"(4.9":[218],"M)":[219],"dataset3.":[223],"Finally,":[224],"comparing":[225],"it":[226,230],"other":[228],"algorithms,":[229],"accurately":[231],"detects":[232],"student":[233],"complex":[236],"environments":[238],"confirmed":[240],"that":[241],"well-suited":[244],"real-time":[246],"efficiently":[248],"recognizing":[249],"behaviors.":[251]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
