{"id":"https://openalex.org/W2947230502","doi":"https://doi.org/10.1109/iciinfs.2018.8721374","title":"Performance Comparison of Machine Learning and Deep Learning While Classifying Driver\u2019s Cognitive State","display_name":"Performance Comparison of Machine Learning and Deep Learning While Classifying Driver\u2019s Cognitive State","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2947230502","doi":"https://doi.org/10.1109/iciinfs.2018.8721374","mag":"2947230502"},"language":"en","primary_location":{"id":"doi:10.1109/iciinfs.2018.8721374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iciinfs.2018.8721374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101696595","display_name":"Rahul Bhardwaj","orcid":"https://orcid.org/0000-0003-4854-988X"},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rahul Bhardwaj","raw_affiliation_strings":["Department of Engineering Design, IIT Madras, RBG Labs, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering Design, IIT Madras, RBG Labs, Chennai, India","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076190140","display_name":"Swathy Parameswaran","orcid":null},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Swathy Parameswaran","raw_affiliation_strings":["Department of Engineering Design, IIT Madras, RBG Labs, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering Design, IIT Madras, RBG Labs, Chennai, India","institution_ids":["https://openalex.org/I24676775"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023711130","display_name":"Venkatesh Balasubramanian","orcid":"https://orcid.org/0000-0002-8491-4947"},"institutions":[{"id":"https://openalex.org/I24676775","display_name":"Indian Institute of Technology Madras","ror":"https://ror.org/03v0r5n49","country_code":"IN","type":"education","lineage":["https://openalex.org/I24676775"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Venkatesh Balasubramanian","raw_affiliation_strings":["Department of Engineering Design, IIT Madras, RBG Labs, Chennai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering Design, IIT Madras, RBG Labs, Chennai, India","institution_ids":["https://openalex.org/I24676775"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24676775"],"apc_list":null,"apc_paid":null,"fwci":0.8659,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.77725564,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"89","last_page":"93"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9994000196456909,"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":0.9994000196456909,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7656306624412537},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7427204847335815},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6892206072807312},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6610684394836426},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.649314820766449},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6472179293632507},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5658695101737976},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.44271060824394226},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.345057874917984}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7656306624412537},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7427204847335815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6892206072807312},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6610684394836426},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.649314820766449},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6472179293632507},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5658695101737976},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.44271060824394226},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.345057874917984},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iciinfs.2018.8721374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iciinfs.2018.8721374","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1526657239","https://openalex.org/W1903029394","https://openalex.org/W1978602582","https://openalex.org/W2008060633","https://openalex.org/W2014815679","https://openalex.org/W2043133488","https://openalex.org/W2058569601","https://openalex.org/W2094726855","https://openalex.org/W2100495367","https://openalex.org/W2147558499","https://openalex.org/W2236429807","https://openalex.org/W2395515569","https://openalex.org/W2743916180","https://openalex.org/W2884843234","https://openalex.org/W2914212892","https://openalex.org/W4241615709","https://openalex.org/W6759331637"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000","https://openalex.org/W4224922629"],"abstract_inverted_index":{"Driver":[0],"fatigue":[1,44,97,128,137,235],"is":[2,36,45],"a":[3,39,53,96,112,122],"major":[4],"cause":[5],"of":[6,23,46,56,85,136,155,182,240],"the":[7,13,32,51,72,134,152,180,187,190,204,224],"road":[8,139],"accidents":[9,140],"that":[10,19,38,151],"occur":[11],"throughout":[12],"globe.":[14],"It":[15],"has":[16],"been":[17,59],"observed":[18],"among":[20,63],"total":[21],"number":[22,55],"accidents,":[24],"20%":[25],"are":[26,211,221],"contributed":[27],"from":[28],"driver":[29,43,127,165,183,234],"fatigue.":[30,166,184],"Acknowledging":[31],"existing":[33],"data":[34,117],"it":[35],"clear":[37],"notification":[40],"system":[41,130],"for":[42,213,233],"at":[47],"most":[48,73],"importance.":[49],"Over":[50],"past":[52],"large":[54],"strategies":[57],"have":[58,68,110],"tested":[60],"out":[61],"and":[62,75,144,159,172,198,216,219,229,242],"them":[64],"EEG":[65,89,116,125],"based":[66,126],"systems":[67],"shown":[69,111],"to":[70,77,88,164,203],"be":[71],"accurate":[74],"reliable":[76],"estimate":[78],"driver's":[79],"cognitive":[80],"state.":[81],"The":[82,148,167],"direct":[83],"relation":[84],"brain":[86],"activity":[87],"signal":[90],"explains":[91],"its":[92],"high":[93],"accuracy":[94,206,239],"in":[95,102,115,179],"detection":[98],"system.":[99],"Current":[100],"researches":[101],"machine":[103,142],"learning":[104,109,143,146,228],"as":[105,107,223],"well":[106,176],"deep":[108,145],"new":[113],"perspective":[114],"analysis.":[118],"This":[119],"work":[120,215],"proposed":[121],"highly":[123],"accurate,":[124],"classification":[129],"which":[131],"can":[132],"reduce":[133],"rate":[135],"related":[138],"using":[141,174],"algorithms.":[147],"results":[149],"showed":[150,161],"relative":[153],"power":[154],"theta,":[156],"alpha,":[157],"beta":[158],"delta":[160],"significant":[162],"correlation":[163],"selected":[168],"features":[169],"were":[170,201],"trained":[171],"evaluated":[173],"20":[175],"established":[177],"classifiers":[178,188],"field":[181],"Among":[185],"all":[186],"tested,":[189],"Fine":[191,195],"Tree,":[192],"Subspace":[193],"KNN,":[194],"Gaussian":[196],"SVM,":[197],"Weighted":[199],"KNN":[200,220],"performed":[202],"highest":[205],"levels.":[207],"Different":[208],"performance":[209],"metrics":[210],"used":[212],"this":[214],"Deep":[217,227],"Autoencoder":[218],"identified":[222],"best":[225],"suitable":[226],"Machine":[230],"Learning":[231],"Algorithms":[232],"prediction":[236],"with":[237],"an":[238],"99.7%":[241],"99.6":[243],"%":[244],"respectively.":[245]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}