{"id":"https://openalex.org/W7140228068","doi":"https://doi.org/10.1504/ijcse.2026.152467","title":"Prediction model for recruitment of railway bureaus and enrolment of railway schools based on deep learning","display_name":"Prediction model for recruitment of railway bureaus and enrolment of railway schools based on deep learning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140228068","doi":"https://doi.org/10.1504/ijcse.2026.152467"},"language":"en","primary_location":{"id":"doi:10.1504/ijcse.2026.152467","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijcse.2026.152467","pdf_url":null,"source":{"id":"https://openalex.org/S65043351","display_name":"International Journal of Computational Science and Engineering","issn_l":"1742-7185","issn":["1742-7185","1742-7193"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Science and Engineering","raw_type":"journal-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":null,"display_name":"Haijun Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210167358","display_name":"Employment Agency","ror":"https://ror.org/05tnmhm33","country_code":"BG","type":"government","lineage":["https://openalex.org/I4210167358"]}],"countries":["BG"],"is_corresponding":false,"raw_author_name":"Haijun Wang","raw_affiliation_strings":["Admissions and Employment Guidance Office, Hunan High speed Railway Vocational and Technical College, 421002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Admissions and Employment Guidance Office, Hunan High speed Railway Vocational and Technical College, 421002, China","institution_ids":["https://openalex.org/I4210167358"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wei He","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106931","display_name":"Jiaxing Vocational Technical College","ror":"https://ror.org/02cydx698","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210106931"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei He","raw_affiliation_strings":["Marxist College, Hunan Environmental Biology Vocational and Technical College, 421001, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Marxist College, Hunan Environmental Biology Vocational and Technical College, 421001, China","institution_ids":["https://openalex.org/I4210106931"]}]},{"author_position":"last","author":{"id":null,"display_name":"Junlun Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I921716337","display_name":"Northeast Petroleum University","ror":"https://ror.org/03net5943","country_code":"CN","type":"education","lineage":["https://openalex.org/I921716337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junlun Sun","raw_affiliation_strings":["School of Mathematics and Statistics, Northeast Petroleum University, 163318, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Northeast Petroleum University, 163318, China","institution_ids":["https://openalex.org/I921716337"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30339711,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":"2","first_page":"129","last_page":"146"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.2215999960899353,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.2215999960899353,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10842","display_name":"Railway Engineering and Dynamics","score":0.059700001031160355,"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"}},{"id":"https://openalex.org/T13812","display_name":"AI and HR Technologies","score":0.04610000178217888,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"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/deep-learning","display_name":"Deep learning","score":0.536300003528595},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.5006999969482422},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4909000098705292},{"id":"https://openalex.org/keywords/rail-network","display_name":"Rail network","score":0.45910000801086426},{"id":"https://openalex.org/keywords/vocational-education","display_name":"Vocational education","score":0.45410001277923584},{"id":"https://openalex.org/keywords/urbanization","display_name":"Urbanization","score":0.44020000100135803},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.40310001373291016}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.536300003528595},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.5006999969482422},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4909000098705292},{"id":"https://openalex.org/C2994220825","wikidata":"https://www.wikidata.org/wiki/Q3565868","display_name":"Rail network","level":2,"score":0.45910000801086426},{"id":"https://openalex.org/C668760","wikidata":"https://www.wikidata.org/wiki/Q6869278","display_name":"Vocational education","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.453900009393692},{"id":"https://openalex.org/C39853841","wikidata":"https://www.wikidata.org/wiki/Q161078","display_name":"Urbanization","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4284999966621399},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.4187999963760376},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.40310001373291016},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.37860000133514404},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.32519999146461487},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.31949999928474426},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3181999921798706},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C2984780852","wikidata":"https://www.wikidata.org/wiki/Q3565868","display_name":"Rail transportation","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C2987165512","wikidata":"https://www.wikidata.org/wiki/Q3565868","display_name":"Railway system","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.28200000524520874},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1504/ijcse.2026.152467","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijcse.2026.152467","pdf_url":null,"source":{"id":"https://openalex.org/S65043351","display_name":"International Journal of Computational Science and Engineering","issn_l":"1742-7185","issn":["1742-7185","1742-7193"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7944275736808777,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"With":[0],"urbanisation":[1],"accelerating,":[2],"the":[3,91,135,150],"demand":[4],"for":[5,16,59,101,120,144],"railway":[6,17,23,64,99,156],"transportation":[7],"is":[8],"increasing,":[9],"making":[10],"it":[11],"essential":[12],"to":[13,28,55,89,134],"plan":[14],"recruitment":[15,31,57,137,145,157],"bureaus":[18,65],"and":[19,53,74,86,111,117,132,146],"adjust":[20],"enrolment":[21,123,147],"at":[22],"schools.":[24],"This":[25],"study":[26],"aims":[27],"accurately":[29],"predict":[30,90],"needs":[32],"using":[33],"historical":[34],"data.":[35],"We":[36,77],"applied":[37],"deep":[38,153],"learning":[39,154],"models,":[40],"including":[41],"back":[42],"propagation":[43],"neural":[44,47],"network":[45],"(BP":[46],"network),":[48],"long":[49],"short-term":[50],"memory":[51],"(LSTM),":[52],"LSTM-attention,":[54],"forecast":[56],"numbers":[58],"eight":[60,97],"positions":[61],"across":[62],"18":[63],"in":[66,96,125,155],"2025,":[67,102],"yielding":[68],"MAE":[69],"values":[70,108],"of":[71,93,130,152],"100,000,":[72],"0.16,":[73],"0.13,":[75],"respectively.":[76],"also":[78],"used":[79],"linear":[80],"regression,":[81,83,85],"ridge":[82],"LASSO":[84],"random":[87],"forests":[88],"number":[92],"remaining":[94],"graduates":[95],"major":[98],"programs":[100],"with":[103],"most":[104],"models":[105],"showing":[106],"MSE":[107],"between":[109],"0":[110],"4.":[112],"Finally,":[113],"we":[114],"established":[115],"upper":[116],"lower":[118],"limits":[119],"vocational":[121],"student":[122],"quotas":[124],"2025":[126],"by":[127],"applying":[128],"factors":[129],"80%":[131],"75%":[133],"predicted":[136],"numbers.":[138],"These":[139],"findings":[140],"provide":[141],"valuable":[142],"insights":[143],"planning,":[148],"enhancing":[149],"application":[151],"forecasting.":[158]},"counts_by_year":[],"updated_date":"2026-03-25T13:09:30.665167","created_date":"2026-03-25T00:00:00"}