{"id":"https://openalex.org/W4414231638","doi":"https://doi.org/10.1109/sera65747.2025.11154628","title":"A Hybrid Deep Learning Approach for Predicting Campaign Success","display_name":"A Hybrid Deep Learning Approach for Predicting Campaign Success","publication_year":2025,"publication_date":"2025-05-29","ids":{"openalex":"https://openalex.org/W4414231638","doi":"https://doi.org/10.1109/sera65747.2025.11154628"},"language":"en","primary_location":{"id":"doi:10.1109/sera65747.2025.11154628","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sera65747.2025.11154628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACIS 23rd International Conference on Software Engineering Research, Management and Applications (SERA)","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/A5107607323","display_name":"Melvin Ajuluchukwu","orcid":null},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Melvin Ajuluchukwu","raw_affiliation_strings":["Georgia Southern University,Information Technology,Statesboro,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia Southern University,Information Technology,Statesboro,GA,USA","institution_ids":["https://openalex.org/I39815113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117309143","display_name":"Lord Coffie","orcid":null},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lord Coffie","raw_affiliation_strings":["Georgia Southern University,Information Technology,Statesboro,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia Southern University,Information Technology,Statesboro,GA,USA","institution_ids":["https://openalex.org/I39815113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047923732","display_name":"Jongyeop Kim","orcid":"https://orcid.org/0000-0002-1068-9855"},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jongyeop Kim","raw_affiliation_strings":["Georgia Southern University,Information Technology,Statesboro,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia Southern University,Information Technology,Statesboro,GA,USA","institution_ids":["https://openalex.org/I39815113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5107607323"],"corresponding_institution_ids":["https://openalex.org/I39815113"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13575506,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"411","last_page":"418"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.73580002784729,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.73580002784729,"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/T11674","display_name":"Sports Analytics and Performance","score":0.626800000667572,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.5989999771118164,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"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/deep-learning","display_name":"Deep learning","score":0.7493000030517578},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.7117999792098999},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6704000234603882},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.5938000082969666},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5371999740600586},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5188000202178955},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.398499995470047}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7529000043869019},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7493000030517578},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7117999792098999},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6704000234603882},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.644599974155426},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.5938000082969666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5777000188827515},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5371999740600586},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5188000202178955},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.398499995470047},{"id":"https://openalex.org/C3018790387","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Hybrid learning","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.3601999878883362},{"id":"https://openalex.org/C23213687","wikidata":"https://www.wikidata.org/wiki/Q301468","display_name":"Consumer behaviour","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3562999963760376},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.35420000553131104},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.3407000005245209},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.3172999918460846},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2619999945163727}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sera65747.2025.11154628","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sera65747.2025.11154628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACIS 23rd International Conference on Software Engineering Research, Management and Applications (SERA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"the":[1,11,85,129,147],"success":[2,53],"of":[3,37,84,149],"marketing":[4],"campaigns":[5],"is":[6,40,93],"a":[7,45,158],"critical":[8],"challenge":[9,16],"in":[10,66,139],"fast-moving":[12],"business":[13],"world.":[14],"This":[15],"requires":[17],"advanced":[18],"models":[19],"that":[20,50,141],"can":[21],"help":[22],"deal":[23],"with":[24,104],"complicated":[25],"consumer":[26,162],"behavior":[27,163],"and":[28,43,57,80,123,164],"tell":[29],"us":[30],"what":[31],"actions":[32],"to":[33,41,69,118,132,160],"take.":[34],"The":[35,61,73,99],"goal":[36],"this":[38,91,153],"study":[39],"create":[42],"test":[44],"hybrid":[46,62,100],"deep":[47],"learning":[48,97],"model":[49,63,101],"predicts":[51],"campaign":[52],"using":[54],"feature":[55,151],"embeddings":[56],"dense":[58],"neural":[59],"networks.":[60],"uses":[64],"TabNet":[65,122],"its":[67,96],"analysis":[68,74],"identify":[70],"key":[71,86],"features.":[72],"identifies":[75],"NumWebPurchases,":[76],"MntGoldProds,":[77],"Teenhome,":[78],"Income,":[79],"Recency":[81],"as":[82,171],"some":[83],"predictors.":[87],"Moreover,":[88],"through":[89],"which":[90,167],"knowledge":[92],"integrated":[94],"into":[95],"frameworks.":[98],"performed":[102],"better":[103],"$91.24":[105],"\\%$":[106,109,112,115],"accuracy,":[107],"$89.76":[108],"precision,":[110],"$89.32":[111],"recall,":[113],"$89.54":[114],"F1-score":[116],"compared":[117],"Multi-Layer":[119],"Perceptron":[120],"(MLP),":[121],"Deep":[124],"Belief":[125],"Networks":[126],"(DBN).":[127],"Presently,":[128],"model\u2019s":[130],"capability":[131],"reduce":[133],"false":[134],"negatives":[135],"assists":[136],"target":[137],"differentiations":[138],"ways":[140],"present-day":[142],"methods":[143],"do":[144],"not.":[145],"Highlighting":[146],"relevance":[148],"examining":[150],"importance,":[152],"exploration":[154],"also":[155],"gives":[156],"marketers":[157],"chance":[159],"grasp":[161],"market":[165],"trends":[166],"could":[168],"be":[169],"seen":[170],"beneficial.":[172]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
