{"id":"https://openalex.org/W4405522443","doi":"https://doi.org/10.1109/iscit63075.2024.10793572","title":"Predicting Marriage Rate Using Machine Learning and Panel Data Analysis: A Case Study of China","display_name":"Predicting Marriage Rate Using Machine Learning and Panel Data Analysis: A Case Study of China","publication_year":2024,"publication_date":"2024-09-23","ids":{"openalex":"https://openalex.org/W4405522443","doi":"https://doi.org/10.1109/iscit63075.2024.10793572"},"language":"en","primary_location":{"id":"doi:10.1109/iscit63075.2024.10793572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscit63075.2024.10793572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 23rd International Symposium on Communications and Information Technologies (ISCIT)","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/A5005782288","display_name":"D. H. Zhang","orcid":"https://orcid.org/0000-0003-0298-7868"},"institutions":[{"id":"https://openalex.org/I34002243","display_name":"Mae Fah Luang University","ror":"https://ror.org/00mwhaw71","country_code":"TH","type":"education","lineage":["https://openalex.org/I34002243"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Deyu Zhang","raw_affiliation_strings":["Mae Fah Luang University,School of Information Technology,Chiang Rai,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mae Fah Luang University,School of Information Technology,Chiang Rai,Thailand","institution_ids":["https://openalex.org/I34002243"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003819242","display_name":"Worasak Rueangsirarak","orcid":"https://orcid.org/0000-0002-8749-1396"},"institutions":[{"id":"https://openalex.org/I34002243","display_name":"Mae Fah Luang University","ror":"https://ror.org/00mwhaw71","country_code":"TH","type":"education","lineage":["https://openalex.org/I34002243"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Worasak Rueangsirarak","raw_affiliation_strings":["School of Information Technology Mae Fah Luang University,Computer and Communication Engineering for Capacity Building Research Unit,Chiang Rai,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology Mae Fah Luang University,Computer and Communication Engineering for Capacity Building Research Unit,Chiang Rai,Thailand","institution_ids":["https://openalex.org/I34002243"]}]},{"author_position":"last","author":{"id":null,"display_name":"Surapong Uttama","orcid":null},"institutions":[{"id":"https://openalex.org/I34002243","display_name":"Mae Fah Luang University","ror":"https://ror.org/00mwhaw71","country_code":"TH","type":"education","lineage":["https://openalex.org/I34002243"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Surapong Uttama","raw_affiliation_strings":["School of Information Technology Mae Fah Luang University,Center of Excellence in Artificial Intelligence and Emerging Technologies,Chiang Rai,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Technology Mae Fah Luang University,Center of Excellence in Artificial Intelligence and Emerging Technologies,Chiang Rai,Thailand","institution_ids":["https://openalex.org/I34002243"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I34002243"],"apc_list":null,"apc_paid":null,"fwci":0.8011,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79857609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"163","last_page":"168"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12728","display_name":"Demographic Trends and Gender Preferences","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/3318","display_name":"Gender Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12728","display_name":"Demographic Trends and Gender Preferences","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/3318","display_name":"Gender Studies"},"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/china","display_name":"China","score":0.6725644469261169},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5792765021324158},{"id":"https://openalex.org/keywords/panel-data","display_name":"Panel data","score":0.4578262269496918},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4483203589916229},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4107486605644226},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38590767979621887},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2984294891357422},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1366998255252838},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.11800819635391235},{"id":"https://openalex.org/keywords/software-engineering","display_name":"Software engineering","score":0.06909167766571045}],"concepts":[{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.6725644469261169},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5792765021324158},{"id":"https://openalex.org/C6422946","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Panel data","level":2,"score":0.4578262269496918},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4483203589916229},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4107486605644226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38590767979621887},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2984294891357422},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1366998255252838},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.11800819635391235},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.06909167766571045},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iscit63075.2024.10793572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscit63075.2024.10793572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 23rd International Symposium on Communications and Information Technologies (ISCIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","score":0.7799999713897705,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1992972735","https://openalex.org/W2060629457","https://openalex.org/W2079414367","https://openalex.org/W2192510903","https://openalex.org/W2903819268","https://openalex.org/W2910871764","https://openalex.org/W3046693005","https://openalex.org/W4300642372","https://openalex.org/W4313479017","https://openalex.org/W4318186085","https://openalex.org/W4362545047","https://openalex.org/W4378189441","https://openalex.org/W4378979525","https://openalex.org/W4386778031","https://openalex.org/W4387443394","https://openalex.org/W4388448099"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"After":[0],"China's":[1,91],"accession":[2],"to":[3,53,145],"the":[4,12,29,36,41,74,101,135,141,146,150,155,160,172,202],"WTO":[5],"and":[6,39,85,104,181,187,190,208],"20":[7],"years":[8],"of":[9,124,138,169],"rapid":[10],"development,":[11],"marriage":[13,37,42,54,93,147,156],"rate":[14,38,55,148],"has":[15,140],"shown":[16],"a":[17],"downward":[18],"trend.":[19],"This":[20],"paper":[21],"aims":[22],"at":[23],"fitting":[24],"machine":[25],"learning":[26],"models":[27,79,132],"with":[28,164,201],"marriage-related":[30],"data,":[31],"understanding":[32],"which":[33],"attributes":[34],"affect":[35],"predicting":[40,90],"rate.":[43,94,157],"The":[44,95,194],"data":[45],"collection":[46],"scope":[47],"includes":[48],"seven":[49],"independent":[50],"variables":[51],"related":[52],"such":[56],"as":[57,108,171],"GDP,":[58,178],"house":[59,151,179],"prices,":[60,180],"birth":[61],"rate,":[62],"education":[63,139,191],"level":[64],"etc.":[65],"over":[66],"31":[67],"regions":[68],"in":[69,89,199],"China":[70],"during":[71],"2003\u20132022.":[72],"Then":[73],"study":[75],"applied":[76],"three":[77],"regression":[78],"-":[80,88],"Pooled":[81,102],"OLS,":[82],"Random":[83,96,161,195],"Effects,":[84],"Fixed":[86,105,126],"Effects":[87,97,106,127,162,196],"crude":[92],"model":[98,128,197],"outperformed":[99],"both":[100],"OLS":[103],"models,":[107],"evidenced":[109],"by":[110],"its":[111],"highest":[112],"R<sup":[113,166],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[114,167],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[115,168],"value":[116],"(0.2910).":[117],"However,":[118],"based":[119],"on":[120],"Hausman":[121],"Test,":[122],"p-value":[123],"6.458e-16.the":[125],"is":[129],"preferrable.":[130],"All":[131],"suggested":[133],"that":[134],"average":[136],"year":[137],"most":[142],"positive":[143],"effect":[144],"while":[149],"price":[152],"greatly":[153],"negates":[154],"Results":[158],"show":[159],"model,":[163],"an":[165],"0.2910,":[170],"best":[173],"fit.":[174],"Key":[175],"predictors":[176],"include":[177],"gross":[182],"dependency":[183],"ratio":[184,189],"(negative":[185],"effects),":[186],"sex":[188],"(positive":[192],"effects).":[193],"excels":[198],"prediction,":[200],"lowest":[203],"MSE":[204],"(1.6610),":[205],"RMSE":[206],"(1.2888)":[207],"MAE":[209],"(1.0888).":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
