{"id":"https://openalex.org/W7084155735","doi":"https://doi.org/10.1142/s0218126625504481","title":"Abnormal Behavior Recognition Approach for Enterprises Using Graph Convolution Network and Bidirectional Recurrent Neural Network","display_name":"Abnormal Behavior Recognition Approach for Enterprises Using Graph Convolution Network and Bidirectional Recurrent Neural Network","publication_year":2025,"publication_date":"2025-08-14","ids":{"openalex":"https://openalex.org/W7084155735","doi":"https://doi.org/10.1142/s0218126625504481"},"language":"en","primary_location":{"id":"doi:10.1142/s0218126625504481","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126625504481","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"},"type":"article","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":null,"display_name":"Zhipeng Chen","orcid":"https://orcid.org/0009-0009-4712-3175"},"institutions":[{"id":"https://openalex.org/I1296452710","display_name":"Hunan International Economics University","ror":"https://ror.org/01551ga11","country_code":"CN","type":"education","lineage":["https://openalex.org/I1296452710"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhipeng Chen","raw_affiliation_strings":["Hunan International Economics University, Changsha 410205, P.\u00a0R.\u00a0China"],"raw_orcid":"https://orcid.org/0009-0009-4712-3175","affiliations":[{"raw_affiliation_string":"Hunan International Economics University, Changsha 410205, P.\u00a0R.\u00a0China","institution_ids":["https://openalex.org/I1296452710"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yuanyuan Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]},{"id":"https://openalex.org/I198357462","display_name":"Changsha University","ror":"https://ror.org/011d8sm39","country_code":"CN","type":"education","lineage":["https://openalex.org/I198357462"]},{"id":"https://openalex.org/I56934997","display_name":"Changsha University of Science and Technology","ror":"https://ror.org/03yph8055","country_code":"CN","type":"education","lineage":["https://openalex.org/I56934997"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Shi","raw_affiliation_strings":["Hunan University of Information Technology, Changsha 410100, P.\u00a0R.\u00a0China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan University of Information Technology, Changsha 410100, P.\u00a0R.\u00a0China","institution_ids":["https://openalex.org/I16609230","https://openalex.org/I198357462","https://openalex.org/I56934997"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"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":0.58034014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"35","issue":"05","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.13410000503063202,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.13410000503063202,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11247","display_name":"Plant nutrient uptake and metabolism","score":0.0908999964594841,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13937","display_name":"Genetics, Bioinformatics, and Biomedical Research","score":0.04659999907016754,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6004999876022339},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5440000295639038},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5378000140190125},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4796000123023987},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4562999904155731},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4537999927997589},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.43720000982284546},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.3937999904155731}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7950999736785889},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6004999876022339},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5910999774932861},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5440000295639038},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5378000140190125},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45649999380111694},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4562999904155731},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4537999927997589},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.43720000982284546},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.39089998602867126},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3878999948501587},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C149859251","wikidata":"https://www.wikidata.org/wiki/Q483426","display_name":"Enterprise private network","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218126625504481","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218126625504481","pdf_url":null,"source":{"id":"https://openalex.org/S167602672","display_name":"Journal of Circuits Systems and Computers","issn_l":"0218-1266","issn":["0218-1266","1793-6454"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Circuits, Systems and Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5358476638793945,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2976051608","https://openalex.org/W2992102026","https://openalex.org/W3027660196","https://openalex.org/W3089258356","https://openalex.org/W3111921609","https://openalex.org/W3216512238","https://openalex.org/W4285612255","https://openalex.org/W4296849331","https://openalex.org/W4386038360","https://openalex.org/W4391731033","https://openalex.org/W4397031067","https://openalex.org/W4397049784","https://openalex.org/W4400033187"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,11,31,57,95,98,114,119,123,153,159],"rapid":[2],"development":[3],"of":[4,15,33,59,66,69,94,100,155,161],"big":[5,60],"data":[6,18,61],"and":[7,13,21,48,73,81,88,126,131],"artificial":[8],"intelligence":[9],"technology,":[10],"scale":[12],"complexity":[14],"enterprise":[16,102,124,143],"behavior":[17,103],"are":[19,54,71,77],"increasing,":[20],"traditional":[22],"anomaly":[23,162],"detection":[24],"methods":[25],"have":[26],"been":[27],"difficult":[28],"to":[29],"meet":[30],"needs":[32],"practical":[34],"applications.":[35],"In":[36],"this":[37],"study,":[38],"two":[39],"advanced":[40],"neural":[41],"network":[42],"architectures,":[43],"Graph":[44],"Convolutional":[45],"Network":[46,52],"(GCN)":[47],"Bidirectional":[49],"Recurrent":[50],"Neural":[51],"(BiRNN),":[53],"introduced.":[55],"From":[56],"perspective":[58],"analysis,":[62],"a":[63,138,149],"large":[64],"number":[65],"business":[67,75],"behaviors":[68,76],"enterprises":[70],"analyzed":[72],"abnormal":[74,101,120],"found.":[78],"The":[79,92,109],"GCNs":[80],"GCN-BiRNNs":[82],"composite":[83],"model,":[84],"which":[85],"includes":[86],"GCN":[87],"BiRNN,":[89],"is":[90,105],"constructed.":[91],"effectiveness":[93],"algorithm":[96],"in":[97,122,158],"task":[99],"identification":[104],"verified":[106],"by":[107],"experiments.":[108],"experimental":[110],"results":[111],"show":[112],"that":[113],"model":[115],"can":[116],"accurately":[117],"identify":[118],"situation":[121],"behavior,":[125],"has":[127],"high":[128],"recognition":[129],"accuracy":[130],"robustness.":[132],"This":[133],"research":[134],"not":[135],"only":[136],"provides":[137,148],"new":[139,150],"technical":[140],"means":[141],"for":[142,152],"risk":[144],"management,":[145],"but":[146],"also":[147],"idea":[151],"application":[154],"deep":[156],"learning":[157],"field":[160],"detection.":[163]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}