{"id":"https://openalex.org/W4407131119","doi":"https://doi.org/10.1109/imcom64595.2025.10857575","title":"Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction","display_name":"Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction","publication_year":2025,"publication_date":"2025-01-03","ids":{"openalex":"https://openalex.org/W4407131119","doi":"https://doi.org/10.1109/imcom64595.2025.10857575"},"language":"en","primary_location":{"id":"doi:10.1109/imcom64595.2025.10857575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/imcom64595.2025.10857575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM)","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/A5060696016","display_name":"Muhammad Omer","orcid":"https://orcid.org/0000-0001-5796-364X"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Omer","raw_affiliation_strings":["Sungkyunkwan University,Dept. of Computer Science and Engineering,Suwon,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Dept. of Computer Science and Engineering,Suwon,Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111345100","display_name":"Sardar Jaffar Ali","orcid":"https://orcid.org/0009-0002-5485-7598"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sardar Jaffar Ali","raw_affiliation_strings":["Sungkyunkwan University,Dept. of AI Systems Engineering,Suwon,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Dept. of AI Systems Engineering,Suwon,Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022697983","display_name":"Syed M. Raza","orcid":"https://orcid.org/0000-0001-6580-3232"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Syed M. Raza","raw_affiliation_strings":["James Watt School of Engineering, University of Glasgow,Glasgow,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"James Watt School of Engineering, University of Glasgow,Glasgow,United Kingdom","institution_ids":["https://openalex.org/I7882870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041230893","display_name":"Duc-Tai Le","orcid":"https://orcid.org/0000-0002-5286-6629"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Duc-Tai Le","raw_affiliation_strings":["Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080942297","display_name":"Hyunseung Choo","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunseung Choo","raw_affiliation_strings":["Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan University,Dept. of Electrical and Computer Engineering,Suwon,Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.2944,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.95132953,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.9144999980926514,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9129999876022339,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7367401123046875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5977796316146851},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5618464350700378},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.560486912727356},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5073656439781189},{"id":"https://openalex.org/keywords/air-quality-index","display_name":"Air quality index","score":0.46403881907463074},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.05540004372596741}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7367401123046875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5977796316146851},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5618464350700378},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.560486912727356},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5073656439781189},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.46403881907463074},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.05540004372596741},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/imcom64595.2025.10857575","is_oa":false,"landing_page_url":"https://doi.org/10.1109/imcom64595.2025.10857575","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1964357740","https://openalex.org/W2030964520","https://openalex.org/W2064675550","https://openalex.org/W2103496339","https://openalex.org/W4220682616","https://openalex.org/W4252684946","https://openalex.org/W4400762160","https://openalex.org/W6684191040","https://openalex.org/W6713134421"],"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":{"Air":[0,19],"pollution":[1],"remains":[2],"a":[3,54],"critical":[4],"issue,":[5],"adversely":[6],"affecting":[7],"public":[8],"health":[9],"and":[10,28,41,65,79,84,96,140],"the":[11,18,111,115,125,155],"environment.":[12],"In":[13],"this":[14,47],"study,":[15],"we":[16,49],"utilize":[17],"Quality":[20],"index":[21],"dataset":[22],"from":[23],"Kaggle":[24],"to":[25],"analyze":[26],"temporal":[27],"seasonal":[29],"variations":[30],"of":[31,132,158],"key":[32],"pollutants,":[33],"specifically":[34,161],"Carbon":[35],"Monoxide":[36],"($CO$),":[37],"Nitrogen":[38],"Oxides":[39],"($NOx$),":[40],"Benzene":[42],"($\\mathrm{C}_6":[43],"\\mathrm{H}_6$).":[44],"Building":[45],"upon":[46],"analysis,":[48],"predict":[50],"Absolute":[51,118],"Humidity":[52],"(AH),":[53],"vital":[55],"meteorological":[56],"factor":[57],"influencing":[58],"pollutant":[59],"dispersion,":[60],"using":[61],"Machine":[62],"Learning":[63,67],"(ML)":[64],"Deep":[66],"(DL)":[68],"techniques.":[69],"Three":[70],"ML":[71,122,164],"techniques,":[72,87],"Linear":[73],"Regression":[74,82],"(LR),":[75],"Random":[76],"Forest":[77],"(RF),":[78],"Support":[80],"Vector":[81],"(SVR),":[83],"three":[85],"DL":[86,159],"Artificial":[88],"Neural":[89,93],"Network":[90,94],"(ANN),":[91],"Convolutional":[92],"(CNN),":[95],"Long":[97],"Short-Term":[98],"Memory":[99],"(LSTM),":[100],"are":[101],"employed":[102],"for":[103],"predicting":[104,167],"AH.":[105,168],"The":[106],"results":[107],"indicate":[108],"that":[109],"while":[110],"RF":[112,148],"model":[113],"achieved":[114],"lowest":[116],"Mean":[117],"Error":[119],"(MAE)":[120],"among":[121],"methods":[123,165],"(0.02),":[124],"CNN":[126],"model,":[127],"despite":[128],"having":[129],"an":[130],"MAE":[131],"0.04,":[133],"demonstrated":[134],"statistical":[135,156],"superiority":[136],"through":[137],"paired":[138],"t-tests":[139],"Wilcoxon":[141],"signed-rank":[142],"tests":[143],"(p":[144,149],"<":[145,150],"0.005),":[146],"outperforming":[147],"0.03).":[151],"These":[152],"findings":[153],"highlight":[154],"significance":[157],"methods,":[160],"CNN,":[162],"over":[163],"in":[166]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}