{"id":"https://openalex.org/W4379932905","doi":"https://doi.org/10.1145/3590837.3590846","title":"Deep Learning Model for Work-Life Balance Prediction for Working Women in IT Industry","display_name":"Deep Learning Model for Work-Life Balance Prediction for Working Women in IT Industry","publication_year":2022,"publication_date":"2022-12-23","ids":{"openalex":"https://openalex.org/W4379932905","doi":"https://doi.org/10.1145/3590837.3590846"},"language":"en","primary_location":{"id":"doi:10.1145/3590837.3590846","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3590837.3590846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","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/A5069171579","display_name":"Supriya Paigude","orcid":null},"institutions":[{"id":"https://openalex.org/I4210088227","display_name":"MIT World Peace University","ror":"https://ror.org/004ymxd45","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210088227"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Supriya Dhananjay Paigude","raw_affiliation_strings":["Management, Dr. Vishwanath Karad MIT World Peace University, India"],"raw_orcid":"https://orcid.org/0000-0003-0564-0421","affiliations":[{"raw_affiliation_string":"Management, Dr. Vishwanath Karad MIT World Peace University, India","institution_ids":["https://openalex.org/I4210088227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090939783","display_name":"Sajeeda Shikalgar","orcid":"https://orcid.org/0000-0002-1959-3868"},"institutions":[{"id":"https://openalex.org/I4210088227","display_name":"MIT World Peace University","ror":"https://ror.org/004ymxd45","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210088227"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sajeeda Riyaj Shikalgar","raw_affiliation_strings":["Dr. Vishwanath Karad MIT World Peace University, Assistant professor, India"],"raw_orcid":"https://orcid.org/0000-0002-1959-3868","affiliations":[{"raw_affiliation_string":"Dr. Vishwanath Karad MIT World Peace University, Assistant professor, India","institution_ids":["https://openalex.org/I4210088227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5069171579"],"corresponding_institution_ids":["https://openalex.org/I4210088227"],"apc_list":null,"apc_paid":null,"fwci":2.3752,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.90803837,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10722","display_name":"Work-Family Balance Challenges","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10722","display_name":"Work-Family Balance Challenges","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13927","display_name":"Cyberloafing and Workplace Behavior","score":0.9688000082969666,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13585","display_name":"Technostress in Professional Settings","score":0.9670000076293945,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.700985312461853},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6805840730667114},{"id":"https://openalex.org/keywords/feeling","display_name":"Feeling","score":0.6465347409248352},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6346811056137085},{"id":"https://openalex.org/keywords/balance","display_name":"Balance (ability)","score":0.6082634925842285},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.5971759557723999},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5537911653518677},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49420788884162903},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.45073264837265015},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.23486444354057312},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.15345460176467896},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1511119306087494}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.700985312461853},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6805840730667114},{"id":"https://openalex.org/C122980154","wikidata":"https://www.wikidata.org/wiki/Q205555","display_name":"Feeling","level":2,"score":0.6465347409248352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6346811056137085},{"id":"https://openalex.org/C168031717","wikidata":"https://www.wikidata.org/wiki/Q1530280","display_name":"Balance (ability)","level":2,"score":0.6082634925842285},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.5971759557723999},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5537911653518677},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49420788884162903},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.45073264837265015},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.23486444354057312},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.15345460176467896},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1511119306087494},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3590837.3590846","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3590837.3590846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 4th International Conference on Information Management & Machine Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W84758129","https://openalex.org/W1544837231","https://openalex.org/W1863774169","https://openalex.org/W1914674302","https://openalex.org/W1975528784","https://openalex.org/W1990764301","https://openalex.org/W2093206453","https://openalex.org/W2297054381","https://openalex.org/W2569541807","https://openalex.org/W2901410468","https://openalex.org/W3024447651","https://openalex.org/W3118299338","https://openalex.org/W4239748550","https://openalex.org/W4286619089","https://openalex.org/W4311048934","https://openalex.org/W4311048936","https://openalex.org/W4311048958","https://openalex.org/W6922568069"],"related_works":["https://openalex.org/W2291261743","https://openalex.org/W1540119434","https://openalex.org/W4229598134","https://openalex.org/W2071657884","https://openalex.org/W4285126926","https://openalex.org/W3158157485","https://openalex.org/W2243550366","https://openalex.org/W3000407446","https://openalex.org/W2103550798","https://openalex.org/W1926317180"],"abstract_inverted_index":{"Industry":[0],"professionals":[1],"predict":[2],"that":[3,23,194,224],"the":[4,18,68,77,90,112,116,119,133,141,166,176,181,186,195,199,203,220,225,232,247],"use":[5],"of":[6,71,79,211,236,249],"artificial":[7],"intelligence":[8],"and":[9,28,38,94,123,135,189,202],"machine":[10,83,109],"learning":[11,47,84,110],"will":[12,25],"significantly":[13],"impact":[14],"people's":[15],"lives":[16],"in":[17,140,209,234],"near":[19],"future.":[20],"They":[21,125,168,192],"believe":[22],"it":[24],"allow":[26],"individuals":[27],"organizations":[29],"to":[30,57,85,88,99,131,162,174,246],"perform":[31,58],"more":[32,41,59],"efficiently":[33,60],"by":[34,54,184],"improving":[35],"their":[36,44,62,72,212],"processes":[37],"making":[39],"them":[40,56],"effective":[42],"at":[43,61],"jobs.":[45,63],"Machine":[46],"could":[48,65,114,240],"help":[49],"improve":[50,89],"employees'":[51],"work-life":[52,105,146],"balance":[53,91,120],"allowing":[55],"It":[64],"also":[66],"increase":[67],"general":[69],"quality":[70],"lives.":[73],"What":[74],"would":[75],"be":[76,241],"outcome":[78],"an":[80],"experiment":[81],"involving":[82],"find":[86],"ways":[87],"between":[92,121],"work":[93,122],"life?":[95],"The":[96,148,157,178],"study":[97,149],"aimed":[98],"analyze":[100,132,175],"individuals'":[101],"subjective":[102],"feelings":[103],"about":[104,165],"balance.":[106,147],"Through":[107],"a":[108,128,145,160],"method,":[111],"researchers":[113,158,179],"identify":[115],"characteristics":[117],"influencing":[118],"life.":[124],"then":[126,169],"used":[127,159,170],"forecasting":[129,215],"model":[130],"data":[134],"determine":[136],"how":[137],"much":[138],"women":[139],"IT":[142,155],"industry":[143],"observe":[144],"was":[150],"conducted":[151],"on":[152],"150":[153],"female":[154],"professionals.":[156],"questionnaire":[161],"gather":[163],"information":[164],"participants.":[167],"various":[171],"deep-learning":[172],"models":[173,196,222],"data.":[177],"analyzed":[180],"models'":[182],"performance":[183],"calculating":[185],"mean":[187],"absolute":[188],"square":[190],"errors.":[191],"found":[193],"known":[197],"as":[198],"Multilayer":[200],"Perceptron":[201],"Long":[204,227],"Short-term":[205],"Memory":[206,228],"performed":[207,229],"well":[208],"terms":[210,235],"accuracy":[213],"when":[214],"future":[216],"business":[217],"conditions.":[218],"Comparing":[219],"two":[221],"revealed":[223],"Short-Term":[226],"better":[230],"than":[231],"MLP":[233],"accuracy.":[237],"This":[238],"finding":[239],"beneficial":[242],"for":[243],"studies":[244],"related":[245],"behavior":[248],"organizations.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}