{"id":"https://openalex.org/W4405850931","doi":"https://doi.org/10.61822/amcs-2024-0038","title":"A deep learning based hybrid model for maternal health risk detection and multifaceted emotion analysis in social networks","display_name":"A deep learning based hybrid model for maternal health risk detection and multifaceted emotion analysis in social networks","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4405850931","doi":"https://doi.org/10.61822/amcs-2024-0038"},"language":"en","primary_location":{"id":"doi:10.61822/amcs-2024-0038","is_oa":true,"landing_page_url":"https://doi.org/10.61822/amcs-2024-0038","pdf_url":"https://reference-global.com/2/v2/download/article/10.61822/amcs-2024-0038.pdf","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"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":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://reference-global.com/2/v2/download/article/10.61822/amcs-2024-0038.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024701134","display_name":"R. Geethanjali","orcid":null},"institutions":[{"id":"https://openalex.org/I33585257","display_name":"Anna University, Chennai","ror":"https://ror.org/01qhf1r47","country_code":"IN","type":"education","lineage":["https://openalex.org/I33585257"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"R. Geethanjali","raw_affiliation_strings":["Faculty of Information and Communication Engineering Anna University"],"affiliations":[{"raw_affiliation_string":"Faculty of Information and Communication Engineering Anna University","institution_ids":["https://openalex.org/I33585257"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043232548","display_name":"A. Valarmathi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106463","display_name":"System Science Applications (United States)","ror":"https://ror.org/01gg29e32","country_code":"US","type":"company","lineage":["https://openalex.org/I4210106463"]},{"id":"https://openalex.org/I4210130392","display_name":"Research Applications (United States)","ror":"https://ror.org/02q2v3574","country_code":"US","type":"company","lineage":["https://openalex.org/I4210130392"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Valarmathi","raw_affiliation_strings":["Department of Computer Applications Anna University"],"affiliations":[{"raw_affiliation_string":"Department of Computer Applications Anna University","institution_ids":["https://openalex.org/I4210130392","https://openalex.org/I4210106463"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024701134"],"corresponding_institution_ids":["https://openalex.org/I33585257"],"apc_list":{"value":4080,"currency":"PLN","value_usd":1100},"apc_paid":{"value":4080,"currency":"PLN","value_usd":1100},"fwci":1.8432,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.86883769,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"34","issue":"4","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.828499972820282,"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"}},"topics":[{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.828499972820282,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.8205000162124634,"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/computer-science","display_name":"Computer science","score":0.5247631072998047},{"id":"https://openalex.org/keywords/social-network-analysis","display_name":"Social network analysis","score":0.4841978847980499},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41484907269477844},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4091032147407532},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3885580003261566},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.33524584770202637},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.11033695936203003},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.06800800561904907}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5247631072998047},{"id":"https://openalex.org/C114713312","wikidata":"https://www.wikidata.org/wiki/Q7551269","display_name":"Social network analysis","level":3,"score":0.4841978847980499},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41484907269477844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4091032147407532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3885580003261566},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.33524584770202637},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.11033695936203003},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.06800800561904907}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.61822/amcs-2024-0038","is_oa":true,"landing_page_url":"https://doi.org/10.61822/amcs-2024-0038","pdf_url":"https://reference-global.com/2/v2/download/article/10.61822/amcs-2024-0038.pdf","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"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":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f1b719e51d3b4cf894f4ebda25bf6e2b","is_oa":true,"landing_page_url":"https://doaj.org/article/f1b719e51d3b4cf894f4ebda25bf6e2b","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"International Journal of Applied Mathematics and Computer Science, Vol 34, Iss 4, Pp 565-577 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.61822/amcs-2024-0038","is_oa":true,"landing_page_url":"https://doi.org/10.61822/amcs-2024-0038","pdf_url":"https://reference-global.com/2/v2/download/article/10.61822/amcs-2024-0038.pdf","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"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":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323425","display_name":"Anna University","ror":"https://ror.org/01qhf1r47"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4405850931.pdf","grobid_xml":"https://content.openalex.org/works/W4405850931.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4387369504","https://openalex.org/W3046775127"],"abstract_inverted_index":{"<abstract":[0],"xmlns=\"http://www.w3.org/1999/xhtml\">":[1],"In":[2],"the":[3,24,37,75,139],"field":[4],"of":[5,26,102,117,121,125,131],"public":[6],"health,":[7,73,85],"accurately":[8],"identifying":[9],"maternal":[10,38,72,103,168],"health":[11,39,104,169],"risks":[12],"through":[13],"social":[14],"network":[15],"data":[16],"is":[17],"both":[18],"vital":[19],"and":[20,60,68,86,94,127,142,171,176],"challenging":[21],"due":[22],"to":[23],"complexities":[25],"multimodal":[27,90,110],"sentiment":[28,66,111],"analysis.":[29],"Our":[30,106],"study":[31],"addresses":[32],"this":[33],"challenge":[34],"by":[35],"introducing":[36],"risk":[40,69],"factor":[41],"detection":[42,70],"using":[43],"deep":[44],"learning":[45],"approach":[46,64],"(MHRFD-DLA),":[47],"a":[48,99,119,123,164],"novel":[49],"framework":[50],"that":[51],"integrates":[52],"convolutional":[53],"neural":[54],"networks,":[55,59],"long":[56],"short-term":[57],"memory":[58],"attention":[61],"mechanisms.":[62],"This":[63],"enhances":[65],"analysis":[67,112],"in":[71,157,178],"with":[74],"focus":[76],"on":[77],"critical":[78],"areas":[79],"such":[80,137],"as":[81,138],"prenatal":[82],"care,":[83],"mental":[84],"nutrition.":[87],"MHRFD-DLA":[88,150],"utilizes":[89],"data,":[91],"including":[92],"text":[93],"electrocardiogram":[95],"(ECG)":[96],"signals,":[97],"offering":[98],"comprehensive":[100],"assessment":[101],"risks.":[105],"model":[107,151],"outperforms":[108],"existing":[109],"models,":[113],"achieving":[114],"an":[115,128],"accuracy":[116],"98.4%,":[118],"precision":[120],"97.6%,":[122],"recall":[124],"95.6%,":[126],"F1":[129],"score":[130],"98.4%.":[132],"Through":[133],"performance":[134],"evaluations,":[135],"visualizations":[136],"confusion":[140],"matrix":[141],"class":[143],"distributions":[144],"further":[145],"validate":[146],"its":[147,174],"robustness.":[148],"The":[149],"not":[152],"only":[153],"bridges":[154],"significant":[155],"gaps":[156],"current":[158],"methodologies,":[159],"but":[160],"it":[161],"also":[162],"sets":[163],"new":[165],"benchmark":[166],"for":[167],"surveillance":[170],"intervention,":[172],"demonstrating":[173],"practicality":[175],"effectiveness":[177],"real-world":[179],"applications.":[180],"</abstract>":[181]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2024-12-28T00:00:00"}