{"id":"https://openalex.org/W3207807837","doi":"https://doi.org/10.1109/access.2021.3119575","title":"A Novel Method CNN-LSTM Ensembler Based on Black Widow and Blue Monkey Optimizer for Electricity Theft Detection","display_name":"A Novel Method CNN-LSTM Ensembler Based on Black Widow and Blue Monkey Optimizer for Electricity Theft Detection","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3207807837","doi":"https://doi.org/10.1109/access.2021.3119575","mag":"3207807837"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3119575","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3119575","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09568902.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09568902.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058352305","display_name":"Abdulwahab Ali Almazroi","orcid":"https://orcid.org/0000-0001-7181-2100"},"institutions":[{"id":"https://openalex.org/I4210099699","display_name":"University of Jeddah","ror":"https://ror.org/015ya8798","country_code":"SA","type":"education","lineage":["https://openalex.org/I4210099699"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Abdulwahab Ali Almazroi","raw_affiliation_strings":["University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Technology, 21959 Jeddah, Saudi Arabia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Technology, 21959 Jeddah, Saudi Arabia","institution_ids":["https://openalex.org/I4210099699"]}]},{"author_position":"last","author":{"id":null,"display_name":"Nasir Ayub","orcid":"https://orcid.org/0000-0002-3243-4095"},"institutions":[{"id":"https://openalex.org/I40597779","display_name":"Federal Urdu University","ror":"https://ror.org/02b52th27","country_code":"PK","type":"education","lineage":["https://openalex.org/I40597779"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Nasir Ayub","raw_affiliation_strings":["Department of Computer Science, Federal Urdu University of Arts, Science and Technology, 44000, Islamabad, Pakistan. (e-mail: nasirayub@fuuast.edu.pk)","Department of Computer Science, Federal Urdu University of Arts, Science and Technology, 44000, Islamabad, Pakistan"],"raw_orcid":"https://orcid.org/0000-0002-3243-4095","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal Urdu University of Arts, Science and Technology, 44000, Islamabad, Pakistan. (e-mail: nasirayub@fuuast.edu.pk)","institution_ids":["https://openalex.org/I40597779"]},{"raw_affiliation_string":"Department of Computer Science, Federal Urdu University of Arts, Science and Technology, 44000, Islamabad, Pakistan","institution_ids":["https://openalex.org/I40597779"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.7661,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.84643941,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"9","issue":null,"first_page":"141154","last_page":"141166"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13429","display_name":"Electricity Theft Detection Techniques","score":1.0,"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/T13429","display_name":"Electricity Theft Detection Techniques","score":1.0,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9775000214576721,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12169","display_name":"Non-Destructive Testing Techniques","score":0.9728999733924866,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8697828054428101},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6542758941650391},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6488456130027771},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6467543840408325},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5765235424041748},{"id":"https://openalex.org/keywords/smart-grid","display_name":"Smart grid","score":0.49578389525413513},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4855756163597107},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4731660485267639},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46429121494293213},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3751344680786133}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8697828054428101},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6542758941650391},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6488456130027771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6467543840408325},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5765235424041748},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.49578389525413513},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4855756163597107},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4731660485267639},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46429121494293213},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3751344680786133},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2021.3119575","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3119575","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09568902.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:427dd2d4fd8b49a586b882dd6ff399e7","is_oa":true,"landing_page_url":"https://doaj.org/article/427dd2d4fd8b49a586b882dd6ff399e7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 141154-141166 (2021)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2021.3119575","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3119575","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09568902.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3207807837.pdf","grobid_xml":"https://content.openalex.org/works/W3207807837.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W1937275748","https://openalex.org/W2028868384","https://openalex.org/W2212529815","https://openalex.org/W2392544820","https://openalex.org/W2608730641","https://openalex.org/W2776990447","https://openalex.org/W2786330811","https://openalex.org/W2790074059","https://openalex.org/W2794620754","https://openalex.org/W2801761896","https://openalex.org/W2893605396","https://openalex.org/W2904322393","https://openalex.org/W2910992412","https://openalex.org/W2922515190","https://openalex.org/W2970705010","https://openalex.org/W2978986055","https://openalex.org/W2980570898","https://openalex.org/W2995221930","https://openalex.org/W3000313095","https://openalex.org/W3005173691","https://openalex.org/W3005498056","https://openalex.org/W3007211815","https://openalex.org/W3008749860","https://openalex.org/W3016889527","https://openalex.org/W3033222914","https://openalex.org/W3039814348","https://openalex.org/W3039985903","https://openalex.org/W3088839307","https://openalex.org/W3090158923","https://openalex.org/W3094328446","https://openalex.org/W3097369108","https://openalex.org/W3106556155","https://openalex.org/W3121647401","https://openalex.org/W3121703777","https://openalex.org/W3123336980","https://openalex.org/W3128592100","https://openalex.org/W3129175333","https://openalex.org/W3132166193","https://openalex.org/W3138780148","https://openalex.org/W3157463800","https://openalex.org/W3164801929","https://openalex.org/W3167354048","https://openalex.org/W3168515984","https://openalex.org/W3176484245","https://openalex.org/W3176996088","https://openalex.org/W3190512233","https://openalex.org/W3193536302","https://openalex.org/W3198120912","https://openalex.org/W3198933754","https://openalex.org/W4231400843","https://openalex.org/W4247574165","https://openalex.org/W6788949440","https://openalex.org/W6789550188","https://openalex.org/W6794326529"],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4318719684","https://openalex.org/W4389724018","https://openalex.org/W4206951940","https://openalex.org/W4293868382","https://openalex.org/W4382602594","https://openalex.org/W4387850423","https://openalex.org/W4388528726"],"abstract_inverted_index":{"Enhanced":[0],"metering":[1,33],"infrastructure":[2],"is":[3,121],"a":[4],"key":[5],"component":[6],"of":[7,31,59,74,109,143,147,149,170,185,204],"the":[8,29,32,40,57,77,80,86,98,103,107,141,150,179,197,202,205],"electrical":[9],"system,":[10],"offering":[11],"many":[12],"advantages,":[13],"including":[14,125],"load":[15],"management":[16],"and":[17,72,90,129,165,172,192],"demand":[18],"response.":[19],"However,":[20],"several":[21],"additional":[22],"energy":[23,45],"theft":[24,46],"channels":[25],"are":[26,134],"introduced":[27],"by":[28],"automation":[30],"system.":[34],"With":[35],"data":[36],"analysis":[37,199],"techniques,":[38,124],"adapting":[39],"smart":[41],"grid":[42],"significantly":[43],"reduces":[44],"loss.":[47],"In":[48,76],"this":[49],"article,":[50],"we":[51],"proposed":[52,162,175,206],"deep":[53],"learning":[54],"methods":[55,163,176],"for":[56,140],"identification":[58],"power":[60],"theft.":[61],"A":[62],"three-stage":[63],"technique":[64,96],"has":[65,188],"been":[66],"devised,":[67],"which":[68],"includes":[69],"selection,":[70],"extraction,":[71],"classification":[73],"features.":[75,105],"selection":[78],"phase,":[79],"average":[81],"hybrid":[82],"feature":[83,94],"importance":[84],"determines":[85],"most":[87],"important":[88],"features":[89],"high":[91,190],"priority.":[92],"The":[93,145,183],"extraction":[95],"employs":[97],"ZFNET":[99],"method":[100],"to":[101,136],"remove":[102],"unwanted":[104],"For":[106],"detection":[108],"electric":[110],"fraud,":[111],"Convolutional":[112],"Neural":[113],"Network":[114],"based":[115],"Long":[116],"Short":[117],"Term":[118],"Memory":[119],"(CNN-LSTM)":[120],"utilized.":[122],"Meta-heuristic":[123],"BlackWidow":[126],"Optimization":[127,132],"(BWO)":[128],"Blue":[130],"Monkey":[131],"(BMO),":[133],"used":[135],"calculate":[137],"optimized":[138],"values":[139],"hyperparameters":[142,148],"CNN-LSTM.":[144],"tuning":[146],"classifier":[151],"helps":[152],"in":[153],"better":[154],"training":[155],"on":[156],"data.":[157],"After":[158],"extensive":[159],"simulation,":[160],"our":[161,186],"CNN-LSTM-BMO":[164],"CNN-LSTM-BWO":[166],"achieved":[167],"an":[168],"accuracy":[169,191],"91%":[171],"93%.":[173],"Our":[174],"outperform":[177],"all":[178],"existing":[180],"compared":[181],"schemes.":[182],"performance":[184],"models":[187],"attained":[189],"low":[193],"error":[194],"rate.":[195],"Furthermore,":[196],"statistical":[198],"also":[200],"shows":[201],"superiority":[203],"methods.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2025-10-10T00:00:00"}