{"id":"https://openalex.org/W4410376953","doi":"https://doi.org/10.32604/cmc.2025.062779","title":"A Data-Enhanced Deep Learning Approach for Emergency Domain Question Intention Recognition in Urban Rail Transit","display_name":"A Data-Enhanced Deep Learning Approach for Emergency Domain Question Intention Recognition in Urban Rail Transit","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4410376953","doi":"https://doi.org/10.32604/cmc.2025.062779"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.062779","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.062779","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.062779","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110233297","display_name":"Y. Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yinuo Chen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101439062","display_name":"Xu Wu","orcid":"https://orcid.org/0000-0003-1553-0680"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu Wu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009336243","display_name":"Jiaxin Fan","orcid":"https://orcid.org/0000-0002-2883-9946"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiaxin Fan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5114535244","display_name":"Guangyu Zhu","orcid":"https://orcid.org/0009-0006-2894-5091"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guangyu Zhu","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5110233297"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.052784,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"84","issue":"1","first_page":"1597","last_page":"1613"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.8930000066757202,"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/T10028","display_name":"Topic Modeling","score":0.8930000066757202,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8496000170707703,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11357","display_name":"Risk and Safety Analysis","score":0.796999990940094,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transit","display_name":"Transit (satellite)","score":0.6116395592689514},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5347885489463806},{"id":"https://openalex.org/keywords/urban-rail-transit","display_name":"Urban rail transit","score":0.5342779159545898},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5266002416610718},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4809112548828125},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.45909106731414795},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.383694052696228},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3319365978240967},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.2745160162448883},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22983604669570923}],"concepts":[{"id":"https://openalex.org/C2778022998","wikidata":"https://www.wikidata.org/wiki/Q651136","display_name":"Transit (satellite)","level":3,"score":0.6116395592689514},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5347885489463806},{"id":"https://openalex.org/C2780434240","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Urban rail transit","level":2,"score":0.5342779159545898},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5266002416610718},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4809112548828125},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.45909106731414795},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.383694052696228},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3319365978240967},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.2745160162448883},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22983604669570923},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.062779","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.062779","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.062779","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.062779","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2885141472","https://openalex.org/W2964110616","https://openalex.org/W3035863525","https://openalex.org/W3042207670","https://openalex.org/W3170936934","https://openalex.org/W3186253370","https://openalex.org/W3201476195","https://openalex.org/W4211083023","https://openalex.org/W4221110699","https://openalex.org/W4224279219","https://openalex.org/W4251241535","https://openalex.org/W4289654523","https://openalex.org/W4289781031","https://openalex.org/W4317214808","https://openalex.org/W4319993355","https://openalex.org/W4322154395","https://openalex.org/W4380985975","https://openalex.org/W4386712274","https://openalex.org/W4388112782","https://openalex.org/W4388676640","https://openalex.org/W4388766733","https://openalex.org/W4389453421","https://openalex.org/W4399609673","https://openalex.org/W4401626840","https://openalex.org/W4401836561","https://openalex.org/W4402412234","https://openalex.org/W4404885404"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2377897436","https://openalex.org/W1178454856","https://openalex.org/W2611989081","https://openalex.org/W2897159733","https://openalex.org/W2953473307","https://openalex.org/W654272262","https://openalex.org/W603905442","https://openalex.org/W332282905","https://openalex.org/W2247480929"],"abstract_inverted_index":{"The":[0],"consultation":[1,72],"intention":[2,87,158],"of":[3,21,36,50,107,129,220,225],"emergency":[4,15,26,53,93,108,111,130,233],"decision-makers":[5],"in":[6,18,66,133,235],"urban":[7],"rail":[8],"transit":[9],"(URT)":[10],"is":[11,113,123,135,143,178,188],"input":[12],"into":[13],"the":[14,19,33,46,51,59,71,75,91,126,138,149,157,213,218,223],"knowledge":[16,38,54,96,231],"base":[17,55],"form":[20,42],"domain":[22,60,131,226],"questions":[23,61,132],"to":[24,41],"obtain":[25],"decision":[27],"support":[28],"services.":[29],"This":[30],"approach":[31],"facilitates":[32],"rapid":[34],"collection":[35],"complete":[37],"and":[39,58,74,99,110,125,148,156,169,199,222],"rules":[40],"effective":[43],"decisions.":[44],"However,":[45],"current":[47],"structured":[48,105],"degree":[49],"URT":[52,92,134],"remains":[56],"low,":[57],"lack":[62],"labeled":[63,127],"datasets,":[64],"resulting":[65],"a":[67,85,104,119],"large":[68],"deviation":[69],"between":[70],"outcomes":[73],"intended":[76],"objectives.":[77],"To":[78],"address":[79],"this":[80,82],"issue,":[81],"paper":[83],"proposes":[84],"question":[86,121],"recognition":[88,159],"model":[89,160,215],"for":[90,165,174,232],"domain,":[94],"leveraging":[95],"graph":[97],"(KG)":[98],"data":[100,141],"enhancement":[101,142],"technology.":[102],"First,":[103],"storage":[106],"cases":[109],"plans":[112],"realized":[114],"based":[115],"on":[116],"KG.":[117,139],"Subsequently,":[118],"comprehensive":[120],"template":[122],"developed,":[124],"dataset":[128],"generated":[136,183],"through":[137],"Lastly,":[140],"applied":[144],"by":[145,184],"prompt":[146],"learning":[147],"NLP":[150],"Chinese":[151],"Data":[152],"Augmentation":[153],"(NLPCDA)":[154],"tool,":[155],"combining":[161],"Generalized":[162],"Auto-regression":[163],"Pre-training":[164],"Language":[166],"Understanding":[167],"(XLNet)":[168],"Recurrent":[170],"Convolutional":[171,205],"Neural":[172,196,206],"Network":[173,197,207],"Text":[175],"Classification":[176],"(TextRCNN)":[177],"constructed.":[179],"Word":[180],"embeddings":[181],"are":[182,202],"XLNet,":[185],"context":[186],"information":[187],"further":[189],"captured":[190],"using":[191],"Bidirectional":[192],"Long":[193],"Short-Term":[194],"Memory":[195],"(BiLSTM),":[198],"salient":[200],"features":[201],"extracted":[203],"with":[204],"(CNN).":[208],"Experimental":[209],"results":[210],"demonstrate":[211],"that":[212],"proposed":[214],"can":[216],"enhance":[217],"clarity":[219],"classification":[221],"identification":[224],"questions,":[227],"thereby":[228],"providing":[229],"supportive":[230],"decision-making":[234],"URT.":[236]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}