{"id":"https://openalex.org/W3169032700","doi":"https://doi.org/10.1145/3463677.3463715","title":"Machine Learning Predictive Model for the Passive Transparency at the Brazilian Ministry of Mines and Energy","display_name":"Machine Learning Predictive Model for the Passive Transparency at the Brazilian Ministry of Mines and Energy","publication_year":2021,"publication_date":"2021-06-09","ids":{"openalex":"https://openalex.org/W3169032700","doi":"https://doi.org/10.1145/3463677.3463715","mag":"3169032700"},"language":"en","primary_location":{"id":"doi:10.1145/3463677.3463715","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3463677.3463715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"DG.O2021: The 22nd Annual International Conference on Digital Government Research","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/A5057837169","display_name":"Ingrid Palma","orcid":null},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Ingrid Palma","raw_affiliation_strings":["Department of Computer Science, University of Brasilia (UnB), Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Brasilia (UnB), Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038955665","display_name":"Marcelo Bronzo Ladeira","orcid":"https://orcid.org/0000-0001-9064-7462"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marcelo Ladeira","raw_affiliation_strings":["Department of Computer Science, University of Brasilia (UnB), Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Brasilia (UnB), Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003166877","display_name":"Ana Carla Bittencourt Reis","orcid":"https://orcid.org/0000-0002-2141-8997"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ana Carla Bittencourt Reis","raw_affiliation_strings":["Department of Computer Science, University of Brasilia (UnB), Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Brasilia (UnB), Brazil","institution_ids":["https://openalex.org/I150729083"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057837169"],"corresponding_institution_ids":["https://openalex.org/I150729083"],"apc_list":null,"apc_paid":null,"fwci":0.5439,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.71959166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"76","last_page":"81"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9833999872207642,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9833999872207642,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9728000164031982,"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"}},{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.9724000096321106,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7178959250450134},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7168679237365723},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6969113349914551},{"id":"https://openalex.org/keywords/christian-ministry","display_name":"Christian ministry","score":0.6845950484275818},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6564896702766418},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6121998429298401},{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.6081634759902954},{"id":"https://openalex.org/keywords/transparency","display_name":"Transparency (behavior)","score":0.6066293120384216},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6062229871749878},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.45704418420791626},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.4405914843082428},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4338175058364868},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39132755994796753}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7178959250450134},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7168679237365723},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6969113349914551},{"id":"https://openalex.org/C521751864","wikidata":"https://www.wikidata.org/wiki/Q1729207","display_name":"Christian ministry","level":2,"score":0.6845950484275818},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6564896702766418},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6121998429298401},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.6081634759902954},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.6066293120384216},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6062229871749878},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45704418420791626},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.4405914843082428},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4338175058364868},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39132755994796753},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3463677.3463715","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3463677.3463715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"DG.O2021: The 22nd Annual International Conference on Digital Government Research","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1499516075","https://openalex.org/W1572615921","https://openalex.org/W1767453699","https://openalex.org/W1880262756","https://openalex.org/W1971030056","https://openalex.org/W1998328938","https://openalex.org/W2068695859","https://openalex.org/W2112378479","https://openalex.org/W2114609788","https://openalex.org/W2229721480","https://openalex.org/W2272745052","https://openalex.org/W2295598076","https://openalex.org/W2334175399","https://openalex.org/W2552012813","https://openalex.org/W2594210082","https://openalex.org/W3102476541","https://openalex.org/W3123775946"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W4386690025","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W2766514146","https://openalex.org/W4289703016","https://openalex.org/W2885516856"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,35],"case":[4],"study":[5],"based":[6],"on":[7],"the":[8,12,22,27,43,46,51,57,60,70,73,82,87],"CRISP-DM":[9],"Model":[10,38],"and":[11,18,32,63,104,111],"use":[13],"of":[14,30,45,59,72,81],"Text":[15],"Mining":[16],"tools":[17],"techniques":[19],"to":[20,41,68],"automate":[21],"Passive":[23],"Transparency":[24],"process":[25],"at":[26],"Brazilian":[28],"Ministry":[29],"Mines":[31],"Energy.":[33],"Thus,":[34],"Machine":[36],"Learning":[37],"is":[39],"proposed":[40],"predict":[42],"class":[44],"technical":[47],"unit":[48],"responsible":[49],"for":[50,77],"data/information":[52],"requested":[53],"by":[54],"citizens.":[55],"Through":[56],"application":[58],"algorithm":[61],"LDA":[62],"TF-IDF":[64],"it":[65],"was":[66,84],"possible":[67],"map":[69],"topics":[71],"most":[74],"relevant":[75],"subjects":[76],"society.":[78],"The":[79],"stability":[80],"model":[83],"tested":[85],"from":[86],"comparative":[88],"analysis":[89],"between":[90],"5":[91],"known":[92],"classification":[93],"algorithms":[94],"(Random":[95],"Forest,":[96],"Multinomial":[97],"NB,":[98],"Linear":[99],"SVC,":[100],"Logistic":[101],"Regression,":[102],"XGBoost":[103,107],"Gradient":[105],"Boosting).":[106],"presented":[108],"better":[109],"performance":[110],"precision":[112],"in":[113],"multiclass":[114],"learning":[115],"outcomes.":[116]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
