{"id":"https://openalex.org/W4312499353","doi":"https://doi.org/10.24996/ijs.2022.63.6.32","title":"Telecom Churn Prediction based on Deep Learning Approach","display_name":"Telecom Churn Prediction based on Deep Learning Approach","publication_year":2022,"publication_date":"2022-06-30","ids":{"openalex":"https://openalex.org/W4312499353","doi":"https://doi.org/10.24996/ijs.2022.63.6.32"},"language":"en","primary_location":{"id":"doi:10.24996/ijs.2022.63.6.32","doi":"https://doi.org/10.24996/ijs.2022.63.6.32","is_oa":true,"landing_page_url":"https://doi.org/10.24996/ijs.2022.63.6.32","pdf_url":"https://ijs.uobaghdad.edu.iq/index.php/eijs/article/download/4280/2710","source":{"id":"https://openalex.org/S2764932332","display_name":"Iraqi Journal of Science","issn_l":"0067-2904","issn":["0067-2904","2312-1637"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["poland-20","russia-white-list-3"],"host_organization":"https://openalex.org/P4310319175","host_organization_name":"College of Science for Women, University of Baghdad","host_organization_lineage":["https://openalex.org/P4310319175"],"host_organization_lineage_names":["College of Science for Women, University of Baghdad"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iraqi Journal of Science","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ijs.uobaghdad.edu.iq/index.php/eijs/article/download/4280/2710","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087418529","display_name":"Israa Mahmood","orcid":"https://orcid.org/0000-0002-9157-6800","observed_orcids":["https://orcid.org/0000-0002-9157-6800"]},"institutions":[{"id":"https://openalex.org/I171256487","display_name":"University of Technology - Iraq","ror":"https://ror.org/01w1ehb86","country_code":"IQ","type":"education","lineage":["https://openalex.org/I171256487"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Israa N. Mahmood","raw_affiliation_strings":["Department of Computer Science, University of Technology, Baghdad, Iraq"],"raw_orcid":"https://orcid.org/0000-0002-9157-6800","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Technology, Baghdad, Iraq","institution_ids":["https://openalex.org/I171256487"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023270809","display_name":"Hasanen Samir Abdullah","orcid":"https://orcid.org/0000-0001-7512-249X","observed_orcids":["https://orcid.org/0000-0001-7512-249X"]},"institutions":[{"id":"https://openalex.org/I171256487","display_name":"University of Technology - Iraq","ror":"https://ror.org/01w1ehb86","country_code":"IQ","type":"education","lineage":["https://openalex.org/I171256487"]}],"countries":["IQ"],"is_corresponding":false,"raw_author_name":"Hasanen S. Abdullah","raw_affiliation_strings":["Department of Computer Science, University of Technology, Baghdad, Iraq"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Technology, Baghdad, Iraq","institution_ids":["https://openalex.org/I171256487"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I171256487"],"apc_list":null,"apc_paid":null,"fwci":1.9981,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.88675958,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2667","last_page":"2675"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10154","display_name":"Customer Service Quality and Loyalty","score":0.0,"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/customer-churn-prediction","display_name":"customer churn prediction","score":0.9900000095367432},{"id":"https://openalex.org/keywords/telecommunications-companies","display_name":"telecommunications companies","score":0.7450000047683716},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.7609999775886536},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.8299999833106995},{"id":"https://openalex.org/keywords/customer-churn","display_name":"customer churn","score":0.7170000076293945},{"id":"https://openalex.org/keywords/classification-accuracy-comparison","display_name":"classification accuracy comparison","score":0.4300000071525574},{"id":"https://openalex.org/keywords/gaussian-naive-bayes","display_name":"Gaussian naive Bayes","score":0.7590000033378601},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.8360000252723694},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.9549999833106995}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6984037756919861},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6584191918373108},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6042372584342957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5437802076339722},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5252477526664734},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5142073631286621},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5092865228652954},{"id":"https://openalex.org/C195487862","wikidata":"https://www.wikidata.org/wiki/Q850210","display_name":"Revenue","level":2,"score":0.4894508421421051},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4888426661491394},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.42967474460601807},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.42833593487739563},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.4273631274700165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4236152172088623},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24996/ijs.2022.63.6.32","doi":"https://doi.org/10.24996/ijs.2022.63.6.32","is_oa":true,"landing_page_url":"https://doi.org/10.24996/ijs.2022.63.6.32","pdf_url":"https://ijs.uobaghdad.edu.iq/index.php/eijs/article/download/4280/2710","source":{"id":"https://openalex.org/S2764932332","display_name":"Iraqi Journal of Science","issn_l":"0067-2904","issn":["0067-2904","2312-1637"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["poland-20","russia-white-list-3"],"host_organization":"https://openalex.org/P4310319175","host_organization_name":"College of Science for Women, University of Baghdad","host_organization_lineage":["https://openalex.org/P4310319175"],"host_organization_lineage_names":["College of Science for Women, University of Baghdad"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iraqi Journal of Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.24996/ijs.2022.63.6.32","doi":"https://doi.org/10.24996/ijs.2022.63.6.32","is_oa":true,"landing_page_url":"https://doi.org/10.24996/ijs.2022.63.6.32","pdf_url":"https://ijs.uobaghdad.edu.iq/index.php/eijs/article/download/4280/2710","source":{"id":"https://openalex.org/S2764932332","display_name":"Iraqi Journal of Science","issn_l":"0067-2904","issn":["0067-2904","2312-1637"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["poland-20","russia-white-list-3"],"host_organization":"https://openalex.org/P4310319175","host_organization_name":"College of Science for Women, University of Baghdad","host_organization_lineage":["https://openalex.org/P4310319175"],"host_organization_lineage_names":["College of Science for Women, University of Baghdad"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iraqi Journal of Science","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4312499353.pdf","grobid_xml":"https://content.openalex.org/works/W4312499353.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W57045458","https://openalex.org/W1524761913","https://openalex.org/W1622811911","https://openalex.org/W1680797894","https://openalex.org/W1981755567","https://openalex.org/W2001706356","https://openalex.org/W2089215879","https://openalex.org/W2096451472","https://openalex.org/W2106817624","https://openalex.org/W2140190241","https://openalex.org/W2161560928","https://openalex.org/W2204900049","https://openalex.org/W2341593984","https://openalex.org/W2515331167","https://openalex.org/W2577513693","https://openalex.org/W2625392185","https://openalex.org/W2747027866","https://openalex.org/W2758113456","https://openalex.org/W2793003883","https://openalex.org/W2807824418","https://openalex.org/W2890205819","https://openalex.org/W2903461962","https://openalex.org/W2943920414","https://openalex.org/W2978481032","https://openalex.org/W3103404646"],"related_works":["https://openalex.org/W7135059091","https://openalex.org/W4408477196","https://openalex.org/W7154711205","https://openalex.org/W7217729982","https://openalex.org/W4410295917","https://openalex.org/W4414041315","https://openalex.org/W7084497080","https://openalex.org/W4400233072","https://openalex.org/W4206457777","https://openalex.org/W3128136252"],"abstract_inverted_index":{"The":[0,44,53,76,96],"transition":[1],"of":[2,55,113],"customers":[3,37],"from":[4],"one":[5],"telecom":[6],"operator":[7],"to":[8,24,39,41],"another":[9,42],"has":[10],"a":[11,31,48,109,125],"direct":[12],"impact":[13],"on":[14,47,81],"the":[15,56,120,131],"company's":[16],"growth":[17],"and":[18,70,90,107],"revenue.":[19],"Traditional":[20],"classification":[21,62],"algorithms":[22],"fail":[23],"predict":[25],"churn":[26,133],"effectively.":[27],"This":[28],"research":[29],"introduces":[30],"deep":[32,98,121],"learning":[33,99,122],"model":[34,45,57,100,123],"for":[35,130],"predicting":[36,74],"planning":[38],"leave":[40],"operator.":[43],"works":[46],"high-dimensional":[49],"large-scale":[50],"data":[51],"set.":[52],"performance":[54],"was":[58,78,117],"measured":[59],"against":[60],"other":[61,104],"algorithms,":[63],"such":[64],"as":[65],"Gaussian":[66],"NB,":[67],"Random":[68],"Forrest,":[69],"Decision":[71],"Tree":[72],"in":[73],"churn.":[75],"evaluation":[77],"performed":[79],"based":[80],"accuracy,":[82],"precision,":[83],"recall,":[84],"F-measure,":[85],"Area":[86],"Under":[87],"Curve":[88],"(AUC),":[89],"Receiver":[91],"Operating":[92],"Characteristic":[93],"(ROC)":[94],"Curve.":[95],"proposed":[97],"performs":[101],"better":[102],"than":[103],"prediction":[105],"models":[106],"achieves":[108],"high":[110],"accuracy":[111],"rate":[112],"91%.":[114],"Furthermore,":[115],"it":[116],"noticed":[118],"that":[119],"outperforms":[124],"small":[126],"size":[127],"Neural":[128],"Network":[129],"customer":[132],"prediction.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2023-01-05T00:00:00"}