{"id":"https://openalex.org/W3090248696","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206907","title":"Intelligent Classification and Automatic Annotation of Violations based on Neural Network Language Model","display_name":"Intelligent Classification and Automatic Annotation of Violations based on Neural Network Language Model","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090248696","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206907","mag":"3090248696"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206907","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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":"https://openalex.org/A5081116432","display_name":"Yaoquan Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yaoquan Yu","raw_affiliation_strings":["Qingyuan Power Supply Bureau, Qingyuan, China"],"affiliations":[{"raw_affiliation_string":"Qingyuan Power Supply Bureau, Qingyuan, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077331417","display_name":"Yuefeng Guo","orcid":"https://orcid.org/0000-0002-4981-844X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuefeng Guo","raw_affiliation_strings":["Qingyuan Power Supply Bureau, Qingyuan, China"],"affiliations":[{"raw_affiliation_string":"Qingyuan Power Supply Bureau, Qingyuan, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403356","display_name":"Zhiyuan Zhang","orcid":"https://orcid.org/0000-0003-2995-5815"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyuan Zhang","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009075081","display_name":"Mengshi Li","orcid":"https://orcid.org/0000-0001-7044-1782"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengshi Li","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020048163","display_name":"Tianyao Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyao Ji","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016974124","display_name":"Wenhu Tang","orcid":"https://orcid.org/0009-0005-5786-4568"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhu Tang","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016939864","display_name":"Qinghua Wu","orcid":"https://orcid.org/0000-0002-0598-8367"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Wu","raw_affiliation_strings":["School of Electric Power Engineering, South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Electric Power Engineering, South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5081116432"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9279,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.80630095,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"4","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13734","display_name":"Advanced Computational Techniques and Applications","score":0.958899974822998,"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/T13734","display_name":"Advanced Computational Techniques and Applications","score":0.958899974822998,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9376000165939331,"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/T13676","display_name":"Educational and Technological Research","score":0.9319999814033508,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7928664088249207},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.704085648059845},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.665205180644989},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6507512331008911},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6341099739074707},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5888801217079163},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5439578890800476},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.48407140374183655},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4628846347332001},{"id":"https://openalex.org/keywords/bayes-classifier","display_name":"Bayes classifier","score":0.4593070447444916},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4006790518760681},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3942292034626007},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33318978548049927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7928664088249207},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.704085648059845},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.665205180644989},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6507512331008911},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6341099739074707},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5888801217079163},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5439578890800476},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.48407140374183655},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4628846347332001},{"id":"https://openalex.org/C185207860","wikidata":"https://www.wikidata.org/wiki/Q17004744","display_name":"Bayes classifier","level":4,"score":0.4593070447444916},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4006790518760681},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3942292034626007},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33318978548049927},{"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:10.1109/ijcnn48605.2020.9206907","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206907","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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":25,"referenced_works":["https://openalex.org/W1211014924","https://openalex.org/W2030960331","https://openalex.org/W2073293214","https://openalex.org/W2096352448","https://openalex.org/W2138881118","https://openalex.org/W2146338426","https://openalex.org/W2170505850","https://openalex.org/W2208845717","https://openalex.org/W2282174177","https://openalex.org/W2382467334","https://openalex.org/W2407776548","https://openalex.org/W2735085484","https://openalex.org/W2735751793","https://openalex.org/W2766385906","https://openalex.org/W2900766077","https://openalex.org/W2906130987","https://openalex.org/W2907212742","https://openalex.org/W2912330373","https://openalex.org/W2913389484","https://openalex.org/W2914382336","https://openalex.org/W2979066089","https://openalex.org/W3105513113","https://openalex.org/W6627915109","https://openalex.org/W6713582272","https://openalex.org/W6758879197"],"related_works":["https://openalex.org/W2374047926","https://openalex.org/W2394466068","https://openalex.org/W2381401419","https://openalex.org/W2383501440","https://openalex.org/W4312866165","https://openalex.org/W2957157835","https://openalex.org/W145653800","https://openalex.org/W83434975","https://openalex.org/W2393473353","https://openalex.org/W2606238806"],"abstract_inverted_index":{"To":[0],"solve":[1],"the":[2,70,74,94,98,113,121,127],"problem":[3],"of":[4,11,15,49,72,76,97,112,124,129],"lack":[5],"on":[6,29,61],"automatic":[7],"classification":[8,95,110,125],"and":[9,85,115,126],"annotation":[10],"a":[12,22],"large":[13],"number":[14],"violation":[16],"cases":[17],"in":[18,34],"power":[19],"supply":[20],"enterprises,":[21],"Neural":[23],"Network":[24],"Language":[25],"Model":[26],"(NNLM)":[27],"based":[28],"word":[30,40],"vector":[31,41],"is":[32,78,100,106],"proposed":[33],"this":[35],"paper.":[36],"This":[37],"model":[38],"adopts":[39],"to":[42,68],"represent":[43],"text":[44,50],"features,":[45],"extracts":[46],"essential":[47],"features":[48],"information":[51],"by":[52],"using":[53],"neural":[54],"network.":[55],"Simulation":[56],"studies":[57],"are":[58],"carried":[59],"out":[60],"data":[62],"collected":[63],"from":[64],"South":[65],"China":[66],"Grid":[67],"evaluate":[69],"performance":[71],"NNLM,":[73],"result":[75],"which":[77,105],"compared":[79],"with":[80],"Naive":[81],"Bayes":[82],"Classifier":[83],"(NBC)":[84],"Logistic":[86],"Regression":[87],"(LR).":[88],"The":[89,117],"experimental":[90],"results":[91],"show":[92],"that":[93],"accuracy":[96,111,122],"NNLM":[99,118],"as":[101,103],"high":[102],"99%,":[104],"much":[107],"higher":[108],"than":[109],"NBC":[114],"LR.":[116],"effectively":[119],"improves":[120],"rate":[123],"efficiency":[128],"annotation.":[130]},"counts_by_year":[{"year":2023,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}