{"id":"https://openalex.org/W2794518042","doi":"https://doi.org/10.1109/conielecomp.2018.8327186","title":"Deep fraud. A fraud intention recognition framework in public transport context using a deep-learning approach","display_name":"Deep fraud. A fraud intention recognition framework in public transport context using a deep-learning approach","publication_year":2018,"publication_date":"2018-02-01","ids":{"openalex":"https://openalex.org/W2794518042","doi":"https://doi.org/10.1109/conielecomp.2018.8327186","mag":"2794518042"},"language":"en","primary_location":{"id":"doi:10.1109/conielecomp.2018.8327186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/conielecomp.2018.8327186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Electronics, Communications and Computers (CONIELECOMP)","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/A5087396026","display_name":"Juan Luis Herrera","orcid":"https://orcid.org/0000-0002-2280-2878"},"institutions":[{"id":"https://openalex.org/I147280213","display_name":"Universidad Veracruzana","ror":"https://ror.org/03efxn362","country_code":"MX","type":"education","lineage":["https://openalex.org/I147280213"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Juan Luis Lopez Herrera","raw_affiliation_strings":["Universidad Veracruzana, Xalapa, Veracruz, MX"],"affiliations":[{"raw_affiliation_string":"Universidad Veracruzana, Xalapa, Veracruz, MX","institution_ids":["https://openalex.org/I147280213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085639833","display_name":"Homero Vladimir R\u00edos-Figueroa","orcid":"https://orcid.org/0000-0001-8673-8863"},"institutions":[{"id":"https://openalex.org/I147280213","display_name":"Universidad Veracruzana","ror":"https://ror.org/03efxn362","country_code":"MX","type":"education","lineage":["https://openalex.org/I147280213"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Homero Vladimir Rios Figueroa","raw_affiliation_strings":["Universidad Veracruzana, Xalapa, Veracruz, MX"],"affiliations":[{"raw_affiliation_string":"Universidad Veracruzana, Xalapa, Veracruz, MX","institution_ids":["https://openalex.org/I147280213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070845386","display_name":"Ericka Janet Rechy-Ramirez","orcid":"https://orcid.org/0000-0002-8401-1174"},"institutions":[{"id":"https://openalex.org/I147280213","display_name":"Universidad Veracruzana","ror":"https://ror.org/03efxn362","country_code":"MX","type":"education","lineage":["https://openalex.org/I147280213"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Ericka Janet Rechy Ramirez","raw_affiliation_strings":["Universidad Veracruzana, Xalapa, Veracruz, MX"],"affiliations":[{"raw_affiliation_string":"Universidad Veracruzana, Xalapa, Veracruz, MX","institution_ids":["https://openalex.org/I147280213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087396026"],"corresponding_institution_ids":["https://openalex.org/I147280213"],"apc_list":null,"apc_paid":null,"fwci":0.9773,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.81290562,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"118","last_page":"125"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9993000030517578,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9993000030517578,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9984999895095825,"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/T13429","display_name":"Electricity Theft Detection Techniques","score":0.9747999906539917,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7286040782928467},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.5970929265022278},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5951371192932129},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.4939444661140442},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4675297737121582},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4504221975803375},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4372374713420868},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4208064675331116},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4101036787033081}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7286040782928467},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5970929265022278},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5951371192932129},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.4939444661140442},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4675297737121582},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4504221975803375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4372374713420868},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4208064675331116},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4101036787033081},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/conielecomp.2018.8327186","is_oa":false,"landing_page_url":"https://doi.org/10.1109/conielecomp.2018.8327186","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Electronics, Communications and Computers (CONIELECOMP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1766594731","https://openalex.org/W1973533434","https://openalex.org/W2074346829","https://openalex.org/W2110798204","https://openalex.org/W2122646361","https://openalex.org/W2138857742","https://openalex.org/W2145094598","https://openalex.org/W2237938520","https://openalex.org/W2396479204","https://openalex.org/W2498417586","https://openalex.org/W2751596245","https://openalex.org/W2950416298","https://openalex.org/W2962975150","https://openalex.org/W2963320746","https://openalex.org/W2963931490","https://openalex.org/W2997574889","https://openalex.org/W3099128122","https://openalex.org/W4231109964","https://openalex.org/W6676481782","https://openalex.org/W6680300913","https://openalex.org/W6681096077","https://openalex.org/W6764071024"],"related_works":["https://openalex.org/W2669956259","https://openalex.org/W2951850672","https://openalex.org/W2610906757","https://openalex.org/W2743258233","https://openalex.org/W4287995534","https://openalex.org/W3131457563","https://openalex.org/W2998168123","https://openalex.org/W4307834408","https://openalex.org/W4304208077","https://openalex.org/W2921182884"],"abstract_inverted_index":{"In":[0,127],"this":[1,67,128],"paper,":[2],"we":[3],"present":[4],"a":[5,18,23,34,81,87,92,98,136,172],"framework":[6,102],"for":[7],"fraud":[8,32,107,131,142],"intention":[9,108,132,176],"recognition":[10],"of":[11,25,49,83,120,155,181,188],"public":[12],"transport":[13,39],"bus":[14],"operators":[15,111],"based":[16,79,115],"on":[17,51,80,116,122,175],"deep":[19],"learning":[20],"approach":[21,75,150],"using":[22,95],"stack":[24],"denoising":[26],"and":[27,45,63,91,97,167,184],"sparse":[28],"autoencoders.":[29],"Bus":[30],"operator's":[31],"is":[33,76,133,141],"common":[35],"problem":[36],"in":[37,66,109],"passenger":[38,50],"organizations":[40],"with":[41,86,151,178],"cash":[42],"payment":[43],"method":[44],"tickets":[46],"as":[47,58,135],"proof":[48],"board.":[52],"Fraud":[53],"detection":[54,61],"could":[55],"be":[56],"considered":[57],"an":[59,77,179,185],"anomaly":[60],"problem,":[62,139],"several":[64],"techniques":[65],"area":[68],"have":[69],"been":[70],"applied":[71],"to":[72,104],"it.":[73],"Our":[74],"architecture":[78],"sort":[82],"Stacked":[84],"Autoencoders":[85],"Softmax":[88],"Classification":[89],"Layer":[90],"normalization":[93],"pre-phase":[94],"word2vec":[96],"Denoising":[99],"Autoencoder.":[100],"This":[101],"permits":[103],"recognize":[105],"the":[106,110,130,156],"delivers":[112],"account":[113],"time,":[114],"date,":[117],"route,":[118],"count":[119],"passengers":[121],"board,":[123],"among":[124],"other":[125],"features.":[126],"framework,":[129],"modeled":[134],"binary":[137],"classification":[138,158],"there":[140,145],"intention,":[143],"or":[144],"isn't.":[146],"We":[147],"compare":[148],"our":[149],"another":[152],"nondeep":[153],"state":[154],"art":[157],"approaches":[159],"like":[160],"Logistic":[161],"Regression,":[162],"SVM,":[163],"Decision":[164],"Trees,":[165],"KNN":[166],"Adaboost":[168],"ensemble":[169],"method,":[170],"obtaining":[171],"superior":[173],"performance":[174],"recognition,":[177],"accuracy":[180],"87.3":[182],"percent":[183],"F1":[186],"score":[187],"0.752.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}