{"id":"https://openalex.org/W4388937376","doi":"https://doi.org/10.1109/icccnt56998.2023.10307002","title":"Comparative Study of CNN Architectures For Driver Drowsiness","display_name":"Comparative Study of CNN Architectures For Driver Drowsiness","publication_year":2023,"publication_date":"2023-07-06","ids":{"openalex":"https://openalex.org/W4388937376","doi":"https://doi.org/10.1109/icccnt56998.2023.10307002"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt56998.2023.10307002","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt56998.2023.10307002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5102819860","display_name":"Bhawna Jain","orcid":"https://orcid.org/0009-0006-7455-2854"},"institutions":[{"id":"https://openalex.org/I4210143260","display_name":"Indira Gandhi Delhi Technical University for Women","ror":"https://ror.org/057c5p638","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210143260"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Bhawna Jain","raw_affiliation_strings":["CSE, IGDTUW,Delhi,India"],"affiliations":[{"raw_affiliation_string":"CSE, IGDTUW,Delhi,India","institution_ids":["https://openalex.org/I4210143260"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065183750","display_name":"Archana Yadav","orcid":"https://orcid.org/0000-0002-4255-9860"},"institutions":[{"id":"https://openalex.org/I4210143260","display_name":"Indira Gandhi Delhi Technical University for Women","ror":"https://ror.org/057c5p638","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210143260"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Archana Yadav","raw_affiliation_strings":["CSE, IGDTUW,Delhi,India"],"affiliations":[{"raw_affiliation_string":"CSE, IGDTUW,Delhi,India","institution_ids":["https://openalex.org/I4210143260"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044949497","display_name":"Ritu R. Gill","orcid":"https://orcid.org/0000-0001-8119-8407"},"institutions":[{"id":"https://openalex.org/I4210143260","display_name":"Indira Gandhi Delhi Technical University for Women","ror":"https://ror.org/057c5p638","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210143260"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ritu Gill","raw_affiliation_strings":["CSE, IGDTUW,Delhi,India"],"affiliations":[{"raw_affiliation_string":"CSE, IGDTUW,Delhi,India","institution_ids":["https://openalex.org/I4210143260"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102819860"],"corresponding_institution_ids":["https://openalex.org/I4210143260"],"apc_list":null,"apc_paid":null,"fwci":0.5224,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.69462558,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9850999712944031,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.9592000246047974,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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":0.7577451467514038},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7181693315505981},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6182457804679871},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5952097177505493},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5435732007026672},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.4813033640384674},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.47272223234176636},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45965757966041565},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4374498426914215},{"id":"https://openalex.org/keywords/statistical-analysis","display_name":"Statistical analysis","score":0.421907901763916},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37593981623649597},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10325580835342407},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09726855158805847},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09296524524688721}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577451467514038},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7181693315505981},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6182457804679871},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5952097177505493},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5435732007026672},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.4813033640384674},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.47272223234176636},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45965757966041565},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4374498426914215},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.421907901763916},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37593981623649597},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10325580835342407},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09726855158805847},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09296524524688721},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt56998.2023.10307002","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt56998.2023.10307002","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2183341477","https://openalex.org/W2795016359","https://openalex.org/W2809254203","https://openalex.org/W2883790098","https://openalex.org/W2912729179","https://openalex.org/W3044774336","https://openalex.org/W3081539007","https://openalex.org/W3102219349","https://openalex.org/W3191242437","https://openalex.org/W4206066711","https://openalex.org/W4225949299","https://openalex.org/W4281773108","https://openalex.org/W4285606031","https://openalex.org/W4286209802","https://openalex.org/W4312345960","https://openalex.org/W4313518751","https://openalex.org/W4317383305","https://openalex.org/W4360994909","https://openalex.org/W4362473236","https://openalex.org/W4362496886","https://openalex.org/W6810966893"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3183901164","https://openalex.org/W4206357785","https://openalex.org/W4281381188","https://openalex.org/W2951211570","https://openalex.org/W3192840557","https://openalex.org/W3167935049"],"abstract_inverted_index":{"Drowsiness":[0],"is":[1,43],"the":[2,34,70,83,86,89,108,113,124,132,140,162,172,214],"sensation":[3],"of":[4,36,41,57,72,88,146,174,191,195,216],"being":[5],"sleepy":[6],"without":[7],"warning.":[8],"Sleep":[9],"deprivation,":[10],"tiredness,":[11],"and":[12,27,85,96,118,134,164,179,193,212],"fatigue":[13],"can":[14,20,200],"all":[15],"contribute":[16],"to":[17,24,103,207,225],"drowsiness,":[18],"which":[19,105,230],"cause":[21,28],"a":[22,55,182],"driver":[23,210,222],"lose":[25],"attention":[26],"irreversible":[29],"damage.":[30],"To":[31],"help":[32],"reduce":[33,213],"occurrence":[35,215],"such":[37],"mishaps,":[38],"early":[39],"detection":[40,74,205],"drowsiness":[42,60,73,204,223],"essential.":[44],"Convolutional":[45],"neural":[46],"networks":[47,68],"(CNNs)":[48],"have":[49],"recently":[50],"shown":[51],"remarkable":[52],"performance":[53],"in":[54,75,101,203],"variety":[56],"applications,":[58],"including":[59,92],"detection.":[61],"This":[62],"study":[63],"focuses":[64],"on":[65,171,220],"fine-tuning":[66],"these":[67],"for":[69,82,112,131,139,161,232],"purpose":[71],"drivers.":[76],"Finally,":[77],"an":[78,189],"evaluation":[79],"was":[80],"done":[81],"comparison":[84],"analysis":[87,133],"latest":[90],"architectures,":[91],"CNN,":[93],"Inception":[94,116],"V3(IV3),":[95],"Visual":[97],"Geometry":[98],"Group(VGG":[99],"16),":[100],"order":[102],"determine":[104],"one":[106],"achieves":[107],"most":[109,125],"favorable":[110],"outcomes":[111],"intended":[114],"task.":[115],"V3":[117],"VGG-16":[119,186,198],"\"transfer":[120],"learning":[121],"models\"":[122],"with":[123],"effective":[126],"classification":[127],"results":[128],"were":[129,169],"chosen":[130],"comparison.":[135],"The":[136,167,197],"dataset":[137],"utilized":[138],"research,":[141],"named":[142],"\"yawn-eye-dataset-new":[143],"(YEDN),\"":[144],"consisting":[145],"2900":[147],"images":[148],"taken":[149],"from":[150],"Kaggle,":[151],"has":[152],"four":[153],"classes:":[154],"two":[155],"states":[156],"(i.":[157],"open,":[158],"ii.":[159],"closed)":[160],"eye":[163],"mouth,":[165],"respectively.":[166],"models":[168],"assessed":[170],"basis":[173],"their":[175],"F-Score,":[176],"sensitivity,":[177],"accuracy,":[178],"precision":[180],"using":[181],"multi-class":[183],"statistical":[184],"analysis.":[185],"technique":[187],"shows":[188],"accuracy":[190],"98.6%":[192],"sensitivity":[194],"98.5%.":[196],"model":[199],"be":[201],"used":[202],"systems":[206],"significantly":[208],"improve":[209],"safety":[211],"unfortunate":[217],"incidents":[218],"brought":[219],"by":[221],"thanks":[224],"its":[226],"high":[227],"success":[228],"rate,":[229],"allows":[231],"this":[233],"conclusion.":[234]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}