[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120460-en":3,"doc-seo-120460-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120460,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","High-Fidelity Machine Learning Techniques for Driver Drowsiness Detection - Research article","Driver drowsiness contributes to a growing level of road crashes worldwide, motivating the development of robust safety solutions. The study analyzes drowsiness detection through a behavioral-based pipeline that emphasizes implementation cost, execution time, and classification accuracy. It leverages image pixels derived from facial geometry instead of relying only on recorded EAR/MOR values. Three ML classifiers (SVM, Naive Bayes, Random Forest) are trained and tested on 1448 images, and Random Forest achieves 92.41% accuracy. A VGG16 deep neural network reaches 97.20%, outperforming traditional models.","Southern University and A&M College  \nDigital Commons @ Southern University and A&M College  \n\n| Faculty Publications | Office of Sponsored Programs |\n| --- | --- |\n| 9-20-2024\u003Cbr>High-Fidelity Machine Learning Techniques for Driver Drowsiness Detection\u003Cbr>Yasser Ismail\u003Cbr>Southern University and A&M College Ebenezer Essel\u003Cbr>Abeer Abdelhamid\u003Cbr>Mahmoud Darwich Fahmi Khalifa\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://digitalcommons.subr.edu/osp_facpubs](https://digitalcommons.subr.edu/osp_facpubs)\u003Cbr> Part of the Engineering Commons |  |\n\nRecommended Citation  \nIsmail, Yasser; Essel, Ebenezer; Abdelhamid, Abeer; Darwich, Mahmoud; Khalifa, Fahmi; and Lacy, Fred,\"High-Fidelity Machine Learning Techniques for Driver Drowsiness Detection\" (2024) . Faculty Publications. 24.  \n[https://digitalcommons.subr.edu/osp_facpubs/24](https://digitalcommons.subr.edu/osp_facpubs/24)  \nThis Article is brought to you for free and open access by the Office of Sponsored Programs at Digital Commons @ Southern University and A&M College. It has been accepted for inclusion in Faculty Publications by an authorized administrator of Digital Commons @ Southern University and A&M College. For more information, please contact [maletta_payne@subr.edu](maletta_payne@subr.edu).  \nAuthors  \nYasser Ismail, Ebenezer Essel, Abeer Abdelhamid, Mahmoud Darwich, Fahmi Khalifa, and Fred Lacy  \nThis article is available at Digital Commons @ Southern University and A&M College:  \n[https://digitalcommons.subr.edu/osp_facpubs/24](https://digitalcommons.subr.edu/osp_facpubs/24)  \nInternational Journal of Computing and Digital Systems  \nISSN (2210-142X)  \nInt. J. Com. Dig. Sys. 16, No.1 (Sep-2024)  \n\n| [http:](http://dx.doi.org/10.12785/ijcds/1601106)[//](http://dx.doi.org/10.12785/ijcds/1601106)[dx.doi.org](http://dx.doi.org/10.12785/ijcds/1601106)[/](http://dx.doi.org/10.12785/ijcds/1601106)[10.12785](http://dx.doi.org/10.12785/ijcds/1601106)[/](http://dx.doi.org/10.12785/ijcds/1601106)[ijcds](http://dx.doi.org/10.12785/ijcds/1601106)[/](http://dx.doi.org/10.12785/ijcds/1601106)[1601106](http://dx.doi.org/10.12785/ijcds/1601106)\u003Cbr>High-Fidelity Machine Learning Techniques for Driver\u003Cbr>Drowsiness Detection\u003Cbr>Ebenezer Essel 1 , Abeer Abdelhamid2 , Mahmoud Darwich3 , Fahmi Khalifa4 , Fred Lacy5 and Yasser\u003Cbr>Ismail5\u003Cbr>1 Department of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA\u003Cbr>2 Electronics and Communications Engineering Dept., Mansoura University, Mansoura 35516, Egypt\u003Cbr>3 Department of Mathematics and Computer Science, University of Mount Union, Alliance, Ohio 44601, USA\u003Cbr>4 Department of Electrical and Computer Engineering, Morgan State University, Baltimore MD 21251, USA\u003Cbr>5 Department of Electrical Engineering, Southern University and A &M College, Baton Rouge, LA 70813, USA\u003Cbr>Received 16 Mar. 2024, Revised 10 Jun. 2024, Accepted 11 Jun. 2024, Published 20 Sep. 2024 |  |\n| --- | --- |\n| Abstract: It is devastating that daily, there is an ample number of car crashes that cause damage to automobiles, onboard passengers get injured, and others tend to lose their lives. Road crashes are fast rising across the globe and have drawn many road safety commissions and concerned individuals to discuss ways to reduce this menacing situation drastically. With the introduction of artificial intelligence and technological advancement, the government and state commissions have beckoned on the various universities and research institutions to develop methods to curb the rise of automobile crashes. Some causes of these crashes include drunk driving and drowsiness, the latter is most prevalent as it occurs to all and sundry. Drowsiness detection can be categorized into three main techniques; behavioral-based, vehicular-based, and physiological-based. In this research, the behavioral-based approach was studied, with significant consideration being the cost of implementation, execution tim","cbCaihFgubBZzUFM","https://ap.wps.com/l/cbCaihFgubBZzUFM","pdf",5372605,1,14,"English","en",105,"# Abstract\n## 1. Introduction\n## Classification Approaches\n## Experimental Setup and Results","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses driver drowsiness detection to reduce automobile crashes and improve road safety outcomes.\"},{\"question\":\"How does the proposed method differ from earlier studies?\",\"answer\":\"It directly uses image pixels from facial geometry to enhance classification accuracy rather than depending only on EAR and MOR values.\"},{\"question\":\"Which models were evaluated and what were the main results?\",\"answer\":\"Support Vector Machine, Naive Bayes, and Random Forest were compared using 1448 images, with Random Forest achieving 92.41% accuracy. VGG16 deep neural network achieved 97.20% accuracy, outperforming the traditional models.\"}]","High-Fidelity Machine Learning Techniques for Driver Drowsiness Detection - Research article | PDF",1785730212,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"high-fidelity-machine-learning-techniques-for-driver-drowsiness-detection-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/high-fidelity-machine-learning-techniques-for-driver-drowsiness-detection-research-article/120460/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses driver drowsiness detection to reduce automobile crashes and improve road safety outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method differ from earlier studies?",{"text":80,"@type":76},"It directly uses image pixels from facial geometry to enhance classification accuracy rather than depending only on EAR and MOR values.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models were evaluated and what were the main results?",{"text":84,"@type":76},"Support Vector Machine, Naive Bayes, and Random Forest were compared using 1448 images, with Random Forest achieving 92.41% accuracy. 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