{"id":"https://openalex.org/W4387970478","doi":"https://doi.org/10.1109/iicaiet59451.2023.10291637","title":"Driver-Drowsiness Detection System Using Deep Learning (CNN)","display_name":"Driver-Drowsiness Detection System Using Deep Learning (CNN)","publication_year":2023,"publication_date":"2023-09-12","ids":{"openalex":"https://openalex.org/W4387970478","doi":"https://doi.org/10.1109/iicaiet59451.2023.10291637"},"language":"en","primary_location":{"id":"doi:10.1109/iicaiet59451.2023.10291637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iicaiet59451.2023.10291637","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","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/A5029300133","display_name":"Firman Ridwan","orcid":"https://orcid.org/0000-0002-4084-3577"},"institutions":[{"id":"https://openalex.org/I161371597","display_name":"Universiti of Malaysia Sabah","ror":"https://ror.org/040v70252","country_code":"MY","type":"education","lineage":["https://openalex.org/I161371597"]}],"countries":["MY"],"is_corresponding":true,"raw_author_name":"Firman Ridwan","raw_affiliation_strings":["Universiti Malaysia Sabah,Faculty of Computing and Informatics,Kota Kinabalu,Sabah","Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah"],"affiliations":[{"raw_affiliation_string":"Universiti Malaysia Sabah,Faculty of Computing and Informatics,Kota Kinabalu,Sabah","institution_ids":["https://openalex.org/I161371597"]},{"raw_affiliation_string":"Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah","institution_ids":["https://openalex.org/I161371597"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059108056","display_name":"Lai Po Hung","orcid":"https://orcid.org/0000-0002-3599-2930"},"institutions":[{"id":"https://openalex.org/I161371597","display_name":"Universiti of Malaysia Sabah","ror":"https://ror.org/040v70252","country_code":"MY","type":"education","lineage":["https://openalex.org/I161371597"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Lai Po Hung","raw_affiliation_strings":["Universiti Malaysia Sabah,Faculty of Computing and Informatics,Kota Kinabalu,Sabah","Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah"],"affiliations":[{"raw_affiliation_string":"Universiti Malaysia Sabah,Faculty of Computing and Informatics,Kota Kinabalu,Sabah","institution_ids":["https://openalex.org/I161371597"]},{"raw_affiliation_string":"Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu, Sabah","institution_ids":["https://openalex.org/I161371597"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5029300133"],"corresponding_institution_ids":["https://openalex.org/I161371597"],"apc_list":null,"apc_paid":null,"fwci":0.2616,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.60583664,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"78","last_page":"83"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9991000294685364,"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":0.9991000294685364,"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/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.9819999933242798,"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"}},{"id":"https://openalex.org/T12597","display_name":"Fire Detection and Safety Systems","score":0.9315000176429749,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6644303798675537},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.551070511341095},{"id":"https://openalex.org/keywords/alarm","display_name":"ALARM","score":0.5329583287239075},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5245385766029358},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.49790143966674805},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.45687544345855713},{"id":"https://openalex.org/keywords/usability","display_name":"Usability","score":0.4359915852546692},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4224957823753357},{"id":"https://openalex.org/keywords/authentication","display_name":"Authentication (law)","score":0.41838520765304565},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.35889333486557007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33958590030670166},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3299192190170288},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.2826250493526459},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.26296696066856384},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2064478099346161}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6644303798675537},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.551070511341095},{"id":"https://openalex.org/C2779119184","wikidata":"https://www.wikidata.org/wiki/Q294350","display_name":"ALARM","level":2,"score":0.5329583287239075},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5245385766029358},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.49790143966674805},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.45687544345855713},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.4359915852546692},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4224957823753357},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.41838520765304565},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.35889333486557007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33958590030670166},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3299192190170288},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2826250493526459},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.26296696066856384},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2064478099346161},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iicaiet59451.2023.10291637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iicaiet59451.2023.10291637","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6100000143051147,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2164868643","https://openalex.org/W2166374993","https://openalex.org/W2251810906","https://openalex.org/W2948593529","https://openalex.org/W2963566548","https://openalex.org/W2973699017","https://openalex.org/W2981459112","https://openalex.org/W3015871410","https://openalex.org/W3093414282","https://openalex.org/W3094292917","https://openalex.org/W6684178448"],"related_works":["https://openalex.org/W1584123598","https://openalex.org/W2429057255","https://openalex.org/W2187546663","https://openalex.org/W148745890","https://openalex.org/W4389670110","https://openalex.org/W2611942503","https://openalex.org/W4315621326","https://openalex.org/W2899790217","https://openalex.org/W2598865957","https://openalex.org/W1576092969"],"abstract_inverted_index":{"Drowsiness":[0],"and":[1,22,61,67,118,148,150,166,189,194,203,208,220,229,271,293,297],"fatigue":[2],"of":[3,10,20,33,71,115,130,198,226,282],"drivers":[4,26,49,288],"are":[5,27,50],"amongst":[6],"the":[7,18,31,40,48,74,182,186,195,199,234,253,261,280],"significant":[8],"causes":[9],"road":[11,103,283,301],"accidents.":[12],"Every":[13],"year,":[14],"this":[15,83],"factor":[16],"increases":[17],"amounts":[19],"deaths":[21],"fatalities":[23],"globally.":[24],"When":[25],"drowsy":[28,287],"or":[29,52,80],"fatigued,":[30],"frequency":[32],"yawning":[34,117],"is":[35,122,211,236,250,263,276,295],"different":[36],"from":[37,178,185],"those":[38],"in":[39,243],"normal":[41],"state.":[42],"This":[43],"behavior":[44],"can":[45,65],"determine":[46],"whether":[47,77],"fatigued":[51],"not.":[53],"It's":[54],"critical":[55],"to":[56,59,95,101,169,265,268,278],"use":[57,131],"technologies":[58],"create":[60],"build":[62],"systems":[63,154],"that":[64,233,260],"detect":[66,96],"monitor":[68],"drivers'":[69],"levels":[70],"attention":[72],"throughout":[73],"driving":[75,100,291],"process,":[76],"they're":[78],"alert":[79],"sleepy.":[81],"In":[82],"study,":[84],"A":[85],"high-precision":[86],"artificial":[87],"intelligence":[88],"model":[89,107,121,200],"was":[90,108],"developed":[91],"using":[92,110,159,252],"deep":[93],"learning":[94],"driver":[97,245],"drowsiness":[98,135],"during":[99,289],"enhance":[102],"safety":[104],"statistics.":[105],"The":[106,120,137,172,192,206,224,273],"trained":[109],"a":[111],"dataset":[112],"containing":[113],"pictures":[114],"people":[116],"blinking.":[119],"then":[123],"integrated":[124],"with":[125,132,240],"mobile":[126],"applications":[127],"for":[128,134],"ease":[129],"smartphones":[133],"detection.":[136],"application":[138,235,262],"includes":[139],"authentication,":[140],"camera":[141,187],"stream,":[142],"trip":[143,145],"history,":[144],"statistics,":[146],"alarm":[147],"notification,":[149],"emergency":[151],"modules.":[152],"Similar":[153],"proposed":[155,274],"by":[156,285,300],"prior":[157],"researchers":[158],"hardware":[160],"devices":[161],"such":[162],"as":[163,238],"webcam,":[164],"smartwatch,":[165],"many":[167],"more":[168],"predict":[170],"drowsiness.":[171,246],"results":[173,225],"achieved":[174],"model's":[175],"accuracy":[176,242],"ranges":[177],"62-87%,":[179],"depending":[180],"on":[181],"frames":[183],"input":[184],"stream":[188],"device":[190],"performance.":[191],"mAP":[193],"average":[196],"recall":[197],"were":[201],"0.55":[202],"0.662,":[204],"respectively.":[205],"testing":[207,228,231,249],"evaluation":[209],"phase":[210],"divided":[212],"into":[213],"three":[214],"parts:":[215],"unit":[216,227],"testing,":[217,219],"integration":[218,230],"user":[221],"acceptance":[222,248],"testing.":[223],"indicate":[232],"working":[237],"intended,":[239],"high":[241],"detecting":[244,286],"User":[247],"done":[251],"System":[254],"Usability":[255],"Scale":[256],"questionnaire,":[257],"which":[258],"shows":[259],"perceived":[264],"be":[266],"easy":[267],"use,":[269],"effective,":[270],"satisfactory.":[272],"system":[275],"expected":[277],"reduce":[279],"statistics":[281],"accidents":[284],"their":[290],"trips,":[292],"it":[294],"widely":[296],"easily":[298],"used":[299],"drivers.":[302]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}