{"id":"https://openalex.org/W7140209398","doi":"https://doi.org/10.1016/j.procs.2026.03.096","title":"Comparative analysis of machine learning algorithms for stress detection","display_name":"Comparative analysis of machine learning algorithms for stress detection","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140209398","doi":"https://doi.org/10.1016/j.procs.2026.03.096"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2026.03.096","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.096","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2026.03.096","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ant\u00f3nio Oseas Pataca","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ant\u00f3nio Oseas Pataca","raw_affiliation_strings":["Instituto de Telecomunica\u00e7\u00f5es, Escola Superior de Tecnologia e Gest\u00e3o de \u00c1gueda, Universidade de Aveiro, \u00c1gueda, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto de Telecomunica\u00e7\u00f5es, Escola Superior de Tecnologia e Gest\u00e3o de \u00c1gueda, Universidade de Aveiro, \u00c1gueda, Portugal","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Ivan Miguel Pires","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ivan Miguel Pires","raw_affiliation_strings":["Instituto de Telecomunica\u00e7\u00f5es, Escola Superior de Tecnologia e Gest\u00e3o de \u00c1gueda, Universidade de Aveiro, \u00c1gueda, Portugal"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Instituto de Telecomunica\u00e7\u00f5es, Escola Superior de Tecnologia e Gest\u00e3o de \u00c1gueda, Universidade de Aveiro, \u00c1gueda, Portugal","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.63435297,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"278","issue":null,"first_page":"1161","last_page":"1169"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.3684000074863434,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.3684000074863434,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.21960000693798065,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.020899999886751175,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7268999814987183},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7175999879837036},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5724999904632568},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5551000237464905},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4832000136375427},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.45019999146461487},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4465000033378601},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4422999918460846}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8831999897956848},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7986000180244446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7433000206947327},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7268999814987183},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7175999879837036},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5724999904632568},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5551000237464905},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4887999892234802},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.45019999146461487},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4465000033378601},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4422999918460846},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4296000003814697},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.4027000069618225},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3855000138282776},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.3353999853134155},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C125168437","wikidata":"https://www.wikidata.org/wiki/Q7625184","display_name":"Structured support vector machine","level":3,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2026.03.096","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.096","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2026.03.096","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2026.03.096","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334779","display_name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","ror":"https://ror.org/00snfqn58"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2156331665","https://openalex.org/W2894771803","https://openalex.org/W2964024268","https://openalex.org/W3030356194","https://openalex.org/W3082076610","https://openalex.org/W3154846076","https://openalex.org/W3191514085","https://openalex.org/W4283690093","https://openalex.org/W4293450877","https://openalex.org/W4360910592","https://openalex.org/W4367320958","https://openalex.org/W4379058548","https://openalex.org/W4393372864","https://openalex.org/W4402633634","https://openalex.org/W4404238796","https://openalex.org/W4406028247"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"evaluates":[2],"five":[3],"machine":[4],"learning":[5],"models,":[6],"such":[7],"as":[8],"AutoKeras,":[9],"Bayesian":[10,108],"Neural":[11],"Network":[12],"(BNN),":[13],"Multilayer":[14],"Perceptron":[15],"(MLP),":[16],"Random":[17,51],"Forest,":[18,105],"and":[19,40,50,91,96,107,119],"Support":[20],"Vector":[21],"Machine":[22],"(SVM),":[23],"on":[24],"the":[25,45,69,76,114],"publicly":[26],"available":[27],"WESAD":[28],"dataset.":[29],"The":[30],"models":[31],"are":[32],"tested":[33],"under":[34],"three":[35],"sensor":[36],"configurations:":[37],"chest-only,":[38],"wrist-only,":[39],"combined.":[41],"While":[42],"MLP":[43],"achieved":[44],"highest":[46],"accuracy":[47],"(83.17%),":[48],"SVM":[49],"Forest":[52],"delivered":[53],"comparable":[54],"performance":[55],"with":[56],"much":[57],"lower":[58],"computational":[59],"requirements,":[60],"making":[61],"them":[62],"suitable":[63],"for":[64],"wearable":[65],"deployment.":[66],"BNN":[67],"offered":[68],"advantage":[70],"of":[71,78],"uncertainty":[72,109],"estimation,":[73],"though":[74],"at":[75],"cost":[77],"significantly":[79],"increased":[80],"resource":[81],"usage.":[82],"In":[83],"addition":[84],"to":[85],"classification":[86],"accuracy,":[87,117],"we":[88],"report":[89],"training":[90],"inference":[92],"times,":[93],"memory":[94],"usage,":[95],"provide":[97],"preliminary":[98],"explainability":[99,120],"results":[100],"via":[101],"feature":[102],"importance":[103],"(Random":[104],"SVM)":[106],"estimation.":[110],"These":[111],"findings":[112],"underscore":[113],"trade-offs":[115],"between":[116],"efficiency,":[118],"in":[121],"stress":[122],"detection":[123],"systems.":[124]},"counts_by_year":[],"updated_date":"2026-04-25T08:17:42.794288","created_date":"2026-03-25T00:00:00"}
