{"id":"https://openalex.org/W7152423270","doi":"https://doi.org/10.1109/icecet63943.2025.11472388","title":"Girl Child Dropout Prediction using Machine Learning","display_name":"Girl Child Dropout Prediction using Machine Learning","publication_year":2025,"publication_date":"2025-07-03","ids":{"openalex":"https://openalex.org/W7152423270","doi":"https://doi.org/10.1109/icecet63943.2025.11472388"},"language":null,"primary_location":{"id":"doi:10.1109/icecet63943.2025.11472388","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet63943.2025.11472388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET)","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/A5133312736","display_name":"Anirudh Sharma","orcid":null},"institutions":[{"id":"https://openalex.org/I4210100254","display_name":"Clinical Insights","ror":"https://ror.org/0168sna55","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210100254"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anirudh Sharma","raw_affiliation_strings":["Data Science & Insights Genpact India Private Limited,Bengaluru,India"],"affiliations":[{"raw_affiliation_string":"Data Science & Insights Genpact India Private Limited,Bengaluru,India","institution_ids":["https://openalex.org/I4210100254"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064349429","display_name":"Bidisha Borkakaty","orcid":null},"institutions":[{"id":"https://openalex.org/I4210100254","display_name":"Clinical Insights","ror":"https://ror.org/0168sna55","country_code":"US","type":"facility","lineage":["https://openalex.org/I4210100254"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bidisha Borkakaty","raw_affiliation_strings":["Data Science & Insights Genpact India Private Limited,Bengaluru,India"],"affiliations":[{"raw_affiliation_string":"Data Science & Insights Genpact India Private Limited,Bengaluru,India","institution_ids":["https://openalex.org/I4210100254"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5133312736"],"corresponding_institution_ids":["https://openalex.org/I4210100254"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.83726354,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13289","display_name":"Infant Health and Development","score":0.03849999979138374,"subfield":{"id":"https://openalex.org/subfields/3611","display_name":"Pharmacy"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T13289","display_name":"Infant Health and Development","score":0.03849999979138374,"subfield":{"id":"https://openalex.org/subfields/3611","display_name":"Pharmacy"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11184","display_name":"Neonatal and fetal brain pathology","score":0.03440000116825104,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12060","display_name":"Child Development and Digital Technology","score":0.024399999529123306,"subfield":{"id":"https://openalex.org/subfields/3304","display_name":"Education"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/girl","display_name":"Girl","score":0.5950000286102295},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.49059998989105225},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.25949999690055847},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.23729999363422394}],"concepts":[{"id":"https://openalex.org/C2778047097","wikidata":"https://www.wikidata.org/wiki/Q3031","display_name":"Girl","level":2,"score":0.5950000286102295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5828999876976013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5239999890327454},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.49059998989105225},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.4797999858856201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3531000018119812},{"id":"https://openalex.org/C138496976","wikidata":"https://www.wikidata.org/wiki/Q175002","display_name":"Developmental psychology","level":1,"score":0.27090001106262207},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.23729999363422394},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2363000065088272}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icecet63943.2025.11472388","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icecet63943.2025.11472388","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","score":0.48447275161743164,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2031459800","https://openalex.org/W2804302410","https://openalex.org/W2907353166","https://openalex.org/W2940151662","https://openalex.org/W2982391067","https://openalex.org/W3019435264","https://openalex.org/W3154656145","https://openalex.org/W4308096262","https://openalex.org/W4387913013","https://openalex.org/W4398188609","https://openalex.org/W4400034608","https://openalex.org/W4400232915","https://openalex.org/W4402980193"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1,115],"uses":[2],"machine":[3],"learning":[4],"(ML)":[5],"models":[6],"to":[7,16,68,79,119,140,147],"predict":[8],"girl":[9,125],"child":[10,126],"dropout":[11,55,149],"rates":[12],"in":[13],"education,":[14],"aiming":[15,146],"identify":[17],"key":[18],"influencing":[19],"factors":[20,136],"and":[21,39,50,83,97,104,132,144,154],"evaluate":[22],"algorithm":[23],"performance.":[24],"During":[25],"data":[26,45],"preprocessing,":[27],"missing":[28],"values":[29],"are":[30,34,37,102],"addressed,":[31],"categorical":[32],"variables":[33],"converted,":[35],"outliers":[36],"removed,":[38],"feature":[40,63],"scaling":[41],"is":[42],"applied.":[43],"Exploratory":[44],"analysis":[46],"(EDA)":[47],"uncovers":[48],"patterns":[49],"relationships,":[51],"offering":[52,128],"insights":[53,129],"into":[54,130],"factors.":[56],"Feature":[57],"selection":[58],"pinpoints":[59],"impactful":[60],"variables,":[61],"while":[62],"engineering":[64],"develops":[65],"weighted":[66],"features":[67],"capture":[69],"significant":[70],"patterns.":[71],"Cross-validation":[72],"evaluates":[73],"how":[74],"well":[75],"a":[76],"model":[77,112],"performs":[78],"ensure":[80],"its":[81],"robustness":[82],"generalizability.":[84],"Several":[85],"ML":[86],"models,":[87],"such":[88],"as":[89],"Random":[90],"Forest,":[91],"Decision":[92],"Trees,":[93],"K":[94],"Nearest":[95],"Neighbors,":[96],"Light":[98],"Gradient":[99],"Boosting":[100],"Machine,":[101],"implemented":[103],"compared":[105],"for":[106],"prediction":[107],"accuracy.":[108],"Hyperparameter":[109],"tuning":[110],"optimizes":[111],"parameters.":[113],"The":[114,134],"provides":[116],"data-driven":[117],"strategies":[118],"tackle":[120],"the":[121],"global":[122],"issue":[123],"of":[124],"dropouts,":[127],"causes":[131],"solutions.":[133],"identified":[135],"guide":[137],"policy":[138],"interventions":[139],"direct":[141],"educational":[142],"programs":[143],"resources,":[145],"lower":[148],"rates,":[150],"promote":[151],"gender":[152],"equality,":[153],"foster":[155],"societal":[156],"progress.":[157]},"counts_by_year":[],"updated_date":"2026-04-11T06:13:24.991567","created_date":"2026-04-10T00:00:00"}