{"id":"https://openalex.org/W4311014098","doi":"https://doi.org/10.3390/s22239323","title":"Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data","display_name":"Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data","publication_year":2022,"publication_date":"2022-11-30","ids":{"openalex":"https://openalex.org/W4311014098","doi":"https://doi.org/10.3390/s22239323","pmid":"https://pubmed.ncbi.nlm.nih.gov/36502025"},"language":"en","primary_location":{"id":"doi:10.3390/s22239323","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22239323","pdf_url":"https://www.mdpi.com/1424-8220/22/23/9323/pdf?version=1670296325","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/23/9323/pdf?version=1670296325","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032081151","display_name":"Purna Prakash Kasaraneni","orcid":"https://orcid.org/0000-0003-2672-5707"},"institutions":[{"id":"https://openalex.org/I4210131147","display_name":"SRM University","ror":"https://ror.org/037skf023","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018","https://openalex.org/I4210131147"]},{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":null,"type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Purna Prakash Kasaraneni","raw_affiliation_strings":["School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0003-2672-5707","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India","institution_ids":["https://openalex.org/I4210131147","https://openalex.org/I4401726783"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059534915","display_name":"Y. V. Pavan Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131147","display_name":"SRM University","ror":"https://ror.org/037skf023","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018","https://openalex.org/I4210131147"]},{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":null,"type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yellapragada Venkata Pavan Kumar","raw_affiliation_strings":["School of Electronics Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0002-9048-5157","affiliations":[{"raw_affiliation_string":"School of Electronics Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India","institution_ids":["https://openalex.org/I4210131147","https://openalex.org/I4401726783"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101843777","display_name":"Ganesh Lakshmana Kumar Moganti","orcid":"https://orcid.org/0000-0002-2904-6593"},"institutions":[{"id":"https://openalex.org/I4210131147","display_name":"SRM University","ror":"https://ror.org/037skf023","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018","https://openalex.org/I4210131147"]},{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":null,"type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ganesh Lakshmana Kumar Moganti","raw_affiliation_strings":["School of Electronics Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0002-2904-6593","affiliations":[{"raw_affiliation_string":"School of Electronics Engineering, VIT-AP University, Amaravati 522237, Andhra Pradesh, India","institution_ids":["https://openalex.org/I4210131147","https://openalex.org/I4401726783"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009704763","display_name":"Ramani Kannan","orcid":"https://orcid.org/0000-0001-5672-7055"},"institutions":[{"id":"https://openalex.org/I203899302","display_name":"Universiti Teknologi Petronas","ror":"https://ror.org/048g2sh07","country_code":"MY","type":"education","lineage":["https://openalex.org/I203899302"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Ramani Kannan","raw_affiliation_strings":["Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas (UTP), Seri Iskandar 32610, Malaysia"],"raw_orcid":"https://orcid.org/0000-0001-5672-7055","affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas (UTP), Seri Iskandar 32610, Malaysia","institution_ids":["https://openalex.org/I203899302"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101843777"],"corresponding_institution_ids":["https://openalex.org/I4210131147","https://openalex.org/I4401726783"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":2.4722,"has_fulltext":true,"cited_by_count":31,"citation_normalized_percentile":{"value":0.89591632,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"22","issue":"23","first_page":"9323","last_page":"9323"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9973999857902527,"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":0.9973999857902527,"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/T10603","display_name":"Smart Grid Energy Management","score":0.9958000183105469,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9919000267982483,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.72222900390625},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7037175297737122},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6977939605712891},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6422370672225952},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6398013234138489},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6271670460700989},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6086994409561157},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.600598156452179},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5896134376525879},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5709308981895447},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5430784225463867},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5057778358459473},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.49757006764411926},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.46427056193351746}],"concepts":[{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.72222900390625},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7037175297737122},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6977939605712891},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6422370672225952},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6398013234138489},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6271670460700989},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6086994409561157},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.600598156452179},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5896134376525879},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5709308981895447},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5430784225463867},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5057778358459473},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.49757006764411926},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.46427056193351746}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016000","descriptor_name":"Cluster Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D060388","descriptor_name":"Support Vector Machine","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22239323","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22239323","pdf_url":"https://www.mdpi.com/1424-8220/22/23/9323/pdf?version=1670296325","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36502025","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36502025","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:26d453be460f457e883f45a13060fce5","is_oa":true,"landing_page_url":"https://doaj.org/article/26d453be460f457e883f45a13060fce5","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":"Sensors, Vol 22, Iss 23, p 9323 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/23/9323/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22239323","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 23; Pages: 9323","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9741071","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9741071","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22239323","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22239323","pdf_url":"https://www.mdpi.com/1424-8220/22/23/9323/pdf?version=1670296325","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9100000262260437,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4311014098.pdf","grobid_xml":"https://content.openalex.org/works/W4311014098.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W2122131850","https://openalex.org/W2316007180","https://openalex.org/W2552500151","https://openalex.org/W2560464769","https://openalex.org/W2618986947","https://openalex.org/W2808622828","https://openalex.org/W2819873736","https://openalex.org/W2883446184","https://openalex.org/W2913259953","https://openalex.org/W2926030578","https://openalex.org/W2958786037","https://openalex.org/W2969938686","https://openalex.org/W3007665943","https://openalex.org/W3042795714","https://openalex.org/W3045430323","https://openalex.org/W3083176882","https://openalex.org/W3099229606","https://openalex.org/W3102259139","https://openalex.org/W3118434263","https://openalex.org/W3118677457","https://openalex.org/W3122263456","https://openalex.org/W3129225410","https://openalex.org/W3170657538","https://openalex.org/W3176484245","https://openalex.org/W3179164182","https://openalex.org/W3188373407","https://openalex.org/W3195694313","https://openalex.org/W3197922937","https://openalex.org/W3201145990","https://openalex.org/W3204223310","https://openalex.org/W3214210995","https://openalex.org/W4205784232","https://openalex.org/W4214656630","https://openalex.org/W4226239514","https://openalex.org/W4226412754","https://openalex.org/W4286484218","https://openalex.org/W4294233923","https://openalex.org/W4312269475","https://openalex.org/W6785481892","https://openalex.org/W6889752795"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"Addressing":[0],"data":[1,40,52,71,151],"anomalies":[2,111,124],"(e.g.,":[3],"garbage":[4],"data,":[5,8],"outliers,":[6],"redundant":[7],"and":[9,103,125,128,146,158,177],"missing":[10],"data)":[11],"plays":[12],"a":[13,77],"vital":[14],"role":[15],"in":[16,112],"performing":[17],"accurate":[18],"analytics":[19],"(billing,":[20],"forecasting,":[21],"load":[22],"profiling,":[23],"etc.)":[24],"on":[25],"smart":[26,67,113],"homes'":[27],"energy":[28,69,115],"consumption":[29,70,116],"data.":[30,117],"From":[31,182],"the":[32,39,63,74,109,167,189,198,204],"literature,":[33],"it":[34,185],"has":[35,193],"been":[36],"identified":[37,187],"that":[38,188],"imputation":[41],"with":[42],"machine":[43,94],"learning":[44],"(ML)-based":[45],"single-classifier":[46,156],"approaches":[47,57],"are":[48,58,180],"used":[49],"to":[50,61,73,106],"address":[51,62],"quality":[53],"issues.":[54],"However,":[55],"these":[56,183],"not":[59],"effective":[60],"hidden":[64],"issues":[65],"of":[66,76,79,138],"home":[68,114],"due":[72],"presence":[75],"variety":[78],"anomalies.":[80],"Hence,":[81],"this":[82,131],"paper":[83],"proposes":[84],"ML-based":[85],"ensemble":[86,162,190,206],"classifiers":[87,163,201,207],"using":[88],"random":[89],"forest":[90],"(RF),":[91],"support":[92],"vector":[93],"(SVM),":[95],"decision":[96],"tree":[97],"(DT),":[98],"naive":[99],"Bayes,":[100],"K-nearest":[101],"neighbor,":[102],"neural":[104],"networks":[105],"handle":[107],"all":[108,123],"possible":[110],"The":[118,134],"proposed":[119],"approach":[120],"initially":[121],"identifies":[122],"removes":[126],"them,":[127],"then":[129],"imputes":[130],"removed/missing":[132],"information.":[133],"entire":[135],"implementation":[136],"consists":[137],"four":[139],"parts.":[140],"Part":[141],"1":[142],"presents":[143,150,155,161],"anomaly":[144,209],"detection":[145],"removal,":[147],"part":[148,153,159],"2":[149],"imputation,":[152],"3":[154],"approaches,":[157],"4":[160],"approaches.":[164],"To":[165],"assess":[166],"classifiers'":[168],"performance,":[169],"various":[170],"metrics,":[171,184],"namely,":[172],"accuracy,":[173],"precision,":[174],"recall/sensitivity,":[175],"specificity,":[176],"F1":[178],"score":[179],"computed.":[181],"is":[186],"classifier":[191],"\"RF+SVM+DT\"":[192],"shown":[194],"superior":[195],"performance":[196],"over":[197],"conventional":[199],"single":[200],"as":[202],"well":[203],"other":[205],"for":[208],"handling.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}