{"id":"https://openalex.org/W7124457064","doi":"https://doi.org/10.1109/access.2026.3654434","title":"Multimodal Analysis for Depression Recognition Using Stacked Multilevel Deep Neural Networks","display_name":"Multimodal Analysis for Depression Recognition Using Stacked Multilevel Deep Neural Networks","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7124457064","doi":"https://doi.org/10.1109/access.2026.3654434"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3654434","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654434","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3654434","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010190570","display_name":"Filipe Fontinele de Almeida","orcid":null},"institutions":[{"id":"https://openalex.org/I47327664","display_name":"Universidade Federal do Maranh\u00e3o","ror":"https://ror.org/043fhe951","country_code":"BR","type":"education","lineage":["https://openalex.org/I47327664"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Filipe Fontinele De Almeida","raw_affiliation_strings":["Department of Computing, Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Federal University of Maranhão (UFMA), São Luís, Maranhão, Brazil","institution_ids":["https://openalex.org/I47327664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080600223","display_name":"Kelson Aires","orcid":"https://orcid.org/0000-0002-3938-5089"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Kelson R\u00f4mulo Teixeira Aires","raw_affiliation_strings":["Department of Computing, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088021525","display_name":"Andr\u00e9 Soares","orcid":"https://orcid.org/0000-0002-3909-8610"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Andr\u00e9 Castelo Branco Soares","raw_affiliation_strings":["Department of Computing, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011591106","display_name":"Laurindo de Sousa Britto Neto","orcid":"https://orcid.org/0000-0002-1484-4111"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Laurindo De Sousa Britto Neto","raw_affiliation_strings":["Department of Computing, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005236232","display_name":"Rodrigo Veras","orcid":"https://orcid.org/0000-0001-8180-4032"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rodrigo De Melo Souza Veras","raw_affiliation_strings":["Department of Computing, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Federal University of Piauí (UFPI), Teresina, Piauí, Brazil","institution_ids":["https://openalex.org/I3121799822"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5010190570"],"corresponding_institution_ids":["https://openalex.org/I47327664"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12212414,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"10033","last_page":"10052"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.8830000162124634,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.8830000162124634,"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/T12488","display_name":"Mental Health via Writing","score":0.03799999877810478,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social 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/T11519","display_name":"Digital Mental Health Interventions","score":0.019899999722838402,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5315999984741211},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48330000042915344},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4648999869823456},{"id":"https://openalex.org/keywords/suicidal-ideation","display_name":"Suicidal ideation","score":0.429500013589859},{"id":"https://openalex.org/keywords/depression","display_name":"Depression (economics)","score":0.4255000054836273},{"id":"https://openalex.org/keywords/psychosocial","display_name":"Psychosocial","score":0.39739999175071716},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.33230000734329224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7070000171661377},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5666999816894531},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48330000042915344},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4648999869823456},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45579999685287476},{"id":"https://openalex.org/C2776641880","wikidata":"https://www.wikidata.org/wiki/Q944142","display_name":"Suicidal ideation","level":4,"score":0.429500013589859},{"id":"https://openalex.org/C2776867660","wikidata":"https://www.wikidata.org/wiki/Q1814941","display_name":"Depression (economics)","level":2,"score":0.4255000054836273},{"id":"https://openalex.org/C150966472","wikidata":"https://www.wikidata.org/wiki/Q3410145","display_name":"Psychosocial","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.33230000734329224},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.31679999828338623},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2928999960422516},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.2757999897003174},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3654434","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654434","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3654434","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3654434","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6665068864822388,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":80,"referenced_works":["https://openalex.org/W1976066595","https://openalex.org/W1988790447","https://openalex.org/W2048533792","https://openalex.org/W2056403322","https://openalex.org/W2085662862","https://openalex.org/W2132322340","https://openalex.org/W2158698691","https://openalex.org/W2191779130","https://openalex.org/W2239141610","https://openalex.org/W2295598076","https://openalex.org/W2346454595","https://openalex.org/W2386192529","https://openalex.org/W2395639500","https://openalex.org/W2478051194","https://openalex.org/W2517194566","https://openalex.org/W2531271152","https://openalex.org/W2584561145","https://openalex.org/W2618530766","https://openalex.org/W2767030624","https://openalex.org/W2796806385","https://openalex.org/W2889056793","https://openalex.org/W2891155597","https://openalex.org/W2903942159","https://openalex.org/W2935705121","https://openalex.org/W2936503027","https://openalex.org/W2972853497","https://openalex.org/W2978855205","https://openalex.org/W2981677410","https://openalex.org/W2998993395","https://openalex.org/W3006923237","https://openalex.org/W3034599802","https://openalex.org/W3040622027","https://openalex.org/W3043084043","https://openalex.org/W3046220160","https://openalex.org/W3088806210","https://openalex.org/W3103685439","https://openalex.org/W3145157056","https://openalex.org/W3161724753","https://openalex.org/W3199562679","https://openalex.org/W4214576234","https://openalex.org/W4220887861","https://openalex.org/W4221055733","https://openalex.org/W4224247713","https://openalex.org/W4283219344","https://openalex.org/W4283732207","https://openalex.org/W4283815599","https://openalex.org/W4307703018","https://openalex.org/W4309573071","https://openalex.org/W4310333988","https://openalex.org/W4312308342","https://openalex.org/W4313443450","https://openalex.org/W4315487826","https://openalex.org/W4321353437","https://openalex.org/W4324387123","https://openalex.org/W4360999323","https://openalex.org/W4377292557","https://openalex.org/W4379382378","https://openalex.org/W4381302628","https://openalex.org/W4385245566","https://openalex.org/W4388833473","https://openalex.org/W4390605218","https://openalex.org/W4391547665","https://openalex.org/W4392054338","https://openalex.org/W4392504912","https://openalex.org/W4392904055","https://openalex.org/W4392963607","https://openalex.org/W4400762160","https://openalex.org/W4400927401","https://openalex.org/W4404290502","https://openalex.org/W4404498187","https://openalex.org/W4405686157","https://openalex.org/W4405988167","https://openalex.org/W4406237488","https://openalex.org/W4406321704","https://openalex.org/W4406339771","https://openalex.org/W4408352824","https://openalex.org/W4408971007","https://openalex.org/W4410086599","https://openalex.org/W4413274215","https://openalex.org/W4413451230"],"related_works":[],"abstract_inverted_index":{"Depression":[0],"is":[1,7,34],"a":[2,10,73,94],"severe":[3,22],"mental":[4],"disorder":[5],"that":[6,46],"associated":[8,128],"with":[9,129],"persistent":[11],"state":[12],"of":[13,50,97,114,165,206],"sadness,":[14],"anhedonia,":[15],"and":[16,30,56,108,140,158,170,183,187,191,194,196],"psychosocial":[17],"dysfunction.":[18],"In":[19,41,116,152,173],"its":[20,213],"most":[21],"form,":[23],"it":[24],"may":[25],"culminate":[26],"in":[27,85,101,178,220,225],"suicidal":[28],"ideation":[29],"behavior.":[31],"Early":[32],"diagnosis":[33],"crucial":[35],"to":[36,82,123,149,203,212],"attenuate":[37],"adverse":[38,126],"clinical":[39],"outcomes.":[40],"this":[42,67],"regard,":[43],"recent":[44],"procedures":[45],"include":[47],"the":[48,118,125,145,166,175,179,204,207,216,221,226],"analyses":[49],"speech":[51],"acoustic":[52],"parameters,":[53],"facial":[54],"features,":[55],"transcribed":[57],"linguistic":[58],"content":[59],"have":[60],"emerged":[61],"as":[62,135],"essential":[63],"alternatives":[64],"for":[65,105],"identifying":[66],"disorder.":[68],"The":[69],"present":[70],"study":[71],"introduces":[72],"Multi-modal":[74],"Stacked":[75],"Multilevel":[76],"Deep":[77],"Neural":[78],"Network":[79],"model":[80],"applied":[81],"depression":[83],"recognition":[84],"predisposed":[86],"patients.":[87],"This":[88],"approach":[89],"integrates":[90],"base":[91],"models":[92,146],"into":[93],"metamodel,":[95],"all":[96],"which":[98,177],"are":[99],"grounded":[100],"deep":[102],"learning":[103],"architectures":[104],"processing":[106],"audiovisual":[107],"audio-textual":[109],"information":[110],"at":[111],"multiple":[112],"levels":[113],"representation.":[115],"addition,":[117],"solution":[119],"incorporates":[120],"specific":[121],"strategies":[122],"ease":[124],"effects":[127],"integrating":[130],"various":[131],"data":[132],"sources":[133],"such":[134],"class":[136],"imbalance,":[137],"high":[138],"variance,":[139],"elevated":[141],"dimensionality,":[142],"thereby":[143],"making":[144],"more":[147],"resilient":[148],"these":[150],"challenges.":[151],"total,":[153],"11":[154],"metamodels":[155],"were":[156,161],"evaluated,":[157],"their":[159],"results":[160],"highlighted,":[162],"including":[163],"those":[164],"BiLSTM,":[167,176],"DCNN,":[168],"Transformer,":[169],"TCN":[171],"architectures.":[172],"particular,":[174],"multimodal":[180],"datasets":[181],"D-Vlog":[182,227],"EDAIC":[184],"achieved":[185],"75.0%":[186],"60.2%":[188],"Precision,":[189],"82.9%":[190],"70.6%":[192],"Recall,":[193],"78.7%":[195],"64.8%":[197],"F1-score,":[198],"respectively,":[199],"attesting":[200],"not":[201],"only":[202],"effectiveness":[205],"proposed":[208],"methodology,":[209],"but":[210],"also":[211],"superiority":[214],"over":[215],"main":[217],"approaches":[218],"reported":[219],"literature,":[222],"statistically":[223],"confirmed":[224],"dataset.":[228]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-01-17T00:00:00"}