{"id":"https://openalex.org/W4312951777","doi":"https://doi.org/10.1145/3565291.3565332","title":"Online Convolutional Neural Network for Image Streams Classification","display_name":"Online Convolutional Neural Network for Image Streams Classification","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4312951777","doi":"https://doi.org/10.1145/3565291.3565332"},"language":"en","primary_location":{"id":"doi:10.1145/3565291.3565332","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3565291.3565332","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Big Data Technologies","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061897035","display_name":"Tianxiang Zheng","orcid":"https://orcid.org/0000-0003-4405-3502"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianxiang Zheng","raw_affiliation_strings":["Guangzhou University, China"],"raw_orcid":"https://orcid.org/0000-0003-4405-3502","affiliations":[{"raw_affiliation_string":"Guangzhou University, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102885970","display_name":"Zhijie Wen","orcid":"https://orcid.org/0000-0002-2706-4616"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijie Wen","raw_affiliation_strings":["Guangzhou University, China"],"raw_orcid":"https://orcid.org/0000-0002-2706-4616","affiliations":[{"raw_affiliation_string":"Guangzhou University, China","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"255","last_page":"259"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12676","display_name":"Machine Learning and ELM","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/computer-science","display_name":"Computer science","score":0.8476693034172058},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.7315675020217896},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.699891984462738},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6568341255187988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5987860560417175},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5567688345909119},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.5473754405975342},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5268204212188721},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47592809796333313},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47504663467407227},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.474176287651062},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4371473491191864},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43414053320884705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3996681571006775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8476693034172058},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.7315675020217896},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.699891984462738},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6568341255187988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5987860560417175},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5567688345909119},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.5473754405975342},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5268204212188721},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47592809796333313},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47504663467407227},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.474176287651062},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4371473491191864},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43414053320884705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3996681571006775},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3565291.3565332","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3565291.3565332","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th International Conference on Big Data Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1570963478","https://openalex.org/W1988790447","https://openalex.org/W2029171653","https://openalex.org/W2106887613","https://openalex.org/W2150621701","https://openalex.org/W2194775991","https://openalex.org/W2560674852","https://openalex.org/W2962677625","https://openalex.org/W2963739929"],"related_works":["https://openalex.org/W2950475743","https://openalex.org/W3008580913","https://openalex.org/W2896628485","https://openalex.org/W1701967867","https://openalex.org/W2953302367","https://openalex.org/W2945706271","https://openalex.org/W4387435415","https://openalex.org/W2114169842","https://openalex.org/W2535808783","https://openalex.org/W3183408266"],"abstract_inverted_index":{"In":[0,56,94],"recent":[1],"years,":[2],"convolutional":[3],"neural":[4,110],"networks":[5],"(CNN)":[6],"have":[7,15],"been":[8],"rapidly":[9],"developed":[10],"for":[11,45],"offline":[12,38],"learning":[13,35,39],"and":[14,32,106,133,169,177],"shown":[16],"powerful":[17],"capabilities":[18],"in":[19,37,54],"areas":[20],"such":[21],"as":[22,83,99,119],"image":[23],"classification.":[24],"However,":[25],"since":[26],"these":[27],"models":[28],"require":[29],"sufficient":[30],"time":[31],"batch":[33],"data":[34,51,84,149,179],"convergence":[36],"mode,":[40],"this":[41],"is":[42,74,113],"not":[43],"scalable":[44],"many":[46],"realistic":[47],"scenarios":[48],"where":[49,77],"new":[50,61],"arrives":[52],"sequentially":[53],"streams.":[55,150,180],"response,":[57],"we":[58,96,162],"propose":[59],"a":[60,107,146],"model:":[62],"Online":[63],"Convolutional":[64],"Neural":[65],"Network":[66],"(OCNN).":[67],"Unlike":[68],"traditional":[69],"CNN":[70,98],"model,":[71,105],"the":[72,100,104,120,135,153,164],"OCNN":[73,138],"very":[75],"flexible":[76],"its":[78],"structure":[79],"can":[80,139],"change":[81],"dynamically":[82,140],"streams":[85],"come":[86],"in,":[87],"allowing":[88],"it":[89],"to":[90,127,130,142,172],"work":[91],"well":[92],"online.":[93],"particular,":[95],"use":[97],"feature":[101,117],"extractor":[102,118],"of":[103,148,155],"fully":[108],"connected":[109,114],"network":[111],"(DNN)":[112],"after":[115],"each":[116,131],"classifier.":[121],"By":[122],"using":[123],"Hedge":[124],"Backpropagation":[125],"method":[126],"assign":[128],"weights":[129],"classifier":[132],"get":[134],"prediction":[136],"results,":[137],"learn":[141],"adaptive":[143],"depth":[144],"from":[145],"sequence":[147],"We":[151],"validated":[152],"efficacy":[154],"our":[156],"model":[157,174],"on":[158,175],"several":[159],"large-scale":[160],"datasets:":[161],"improved":[163],"classification":[165],"accuracy":[166],"by":[167],"4.95":[168],"20.74":[170],"compared":[171],"baseline":[173],"Fashion-MNIST":[176],"CIFAR-10":[178]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}