{"id":"https://openalex.org/W4404031004","doi":"https://doi.org/10.1109/icccnt61001.2024.10723985","title":"Improving the Accuracy by Comparing with CatBoost Classifier Algorithm over Multilayer Perceptron Classifier Algorithm for Predicting the Air Pollution","display_name":"Improving the Accuracy by Comparing with CatBoost Classifier Algorithm over Multilayer Perceptron Classifier Algorithm for Predicting the Air Pollution","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4404031004","doi":"https://doi.org/10.1109/icccnt61001.2024.10723985"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt61001.2024.10723985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt61001.2024.10723985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5062731087","display_name":"Y. Pavan Kumar Naidu","orcid":null},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Y. Pavan Kumar Naidu","raw_affiliation_strings":["Saveetha Institute of Medical andTechnical Sciences,Saveetha School of Engineering,Department of Computer Science and Engineering,Tamilnadu,Indiaa"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Saveetha Institute of Medical andTechnical Sciences,Saveetha School of Engineering,Department of Computer Science and Engineering,Tamilnadu,Indiaa","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023824061","display_name":"Muhammad Amanullah","orcid":null},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Amanullah","raw_affiliation_strings":["Saveetha Institute of Medical andTechnical Sciences, Saveetha Univerity,Saveetha School of Engineering,Department of Computer Science and Engineering,Tamilnadu,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Saveetha Institute of Medical andTechnical Sciences, Saveetha Univerity,Saveetha School of Engineering,Department of Computer Science and Engineering,Tamilnadu,India","institution_ids":["https://openalex.org/I85461943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I85461943"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.8845999836921692,"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":0.8845999836921692,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6982164978981018},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6980459094047546},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6239380240440369},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.573066234588623},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5594891309738159},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5077112913131714},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4873407781124115},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.44410350918769836},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3640996813774109},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2455451488494873}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6982164978981018},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6980459094047546},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6239380240440369},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.573066234588623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5594891309738159},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5077112913131714},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4873407781124115},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.44410350918769836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3640996813774109},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2455451488494873}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt61001.2024.10723985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt61001.2024.10723985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2922446486","https://openalex.org/W2955092811","https://openalex.org/W2973494134","https://openalex.org/W2997493149","https://openalex.org/W3024586220","https://openalex.org/W3038462551","https://openalex.org/W3113701344","https://openalex.org/W3131333586","https://openalex.org/W3183461092","https://openalex.org/W4225871724","https://openalex.org/W4386041549","https://openalex.org/W4386736729","https://openalex.org/W4387490344"],"related_works":["https://openalex.org/W2076543106","https://openalex.org/W2523437662","https://openalex.org/W89844371","https://openalex.org/W2019891950","https://openalex.org/W2085842814","https://openalex.org/W4286643620","https://openalex.org/W4387048144","https://openalex.org/W4280611221","https://openalex.org/W2492135063","https://openalex.org/W2362514456"],"abstract_inverted_index":{"Aim:":[0],"This":[1,179],"effort":[2],"aims":[3],"to":[4,48,132],"determine":[5],"the":[6,17,22,33,50,55,63,74,114,120,127,140,154,182,201],"type":[7],"of":[8,16,150],"air":[9,38,56],"pollution":[10,57],"quality.":[11],"Through":[12],"a":[13,160],"comparison":[14],"study":[15],"catboost":[18],"Classifier":[19,144,156,169],"procedure":[20],"and":[21,41,68,78,90,153],"MLP":[23,155,202],"classifier":[24,195,203],"method,":[25],"it":[26],"has":[27,147,159,197],"been":[28],"found":[29],"which":[30,146,158],"algorithm":[31,67,196,204],"is":[32,46,76,117],"most":[34],"accurate":[35],"in":[36,95,205],"forecasting":[37],"pollution.":[39],"Materials":[40],"methods:":[42],"The":[43,60,84,122,164],"Kaggle":[44],"website":[45],"used":[47],"obtain":[49],"dataset":[51,75],"required":[52],"for":[53,62,113],"finding":[54],"quality":[58],"type.":[59],"algorithms":[61,184],"Novel":[64,141,166],"Cat":[65,142,193],"boost":[66],"Multilayer":[69],"perceptron":[70],"are":[71,102,124],"tested":[72],"when":[73],"imported":[77],"data":[79],"mining":[80],"can":[81],"be":[82],"done.":[83],"G":[85],"power":[86,91],"test":[87],"with":[88,173],"$\\alpha=0.05$":[89],"$=0.85$":[92],"was":[93,171],"employed":[94],"this":[96,206],"study.":[97,207],"For":[98],"two":[99,103,183],"algorithms,":[100],"there":[101],"groups.":[104],"Each":[105],"group":[106],"sample":[107],"size":[108],"iteration":[109],"10.":[110],"Results:":[111],"Accuracy":[112],"supplied":[115],"inputs":[116],"acquired":[118],"as":[119],"results.":[121],"results":[123],"obtained":[125],"using":[126],"IBM":[128],"SPSS":[129],"software.":[130],"According":[131],"findings,":[133],"A":[134],"statistically":[135,186],"significant":[136,172],"distinction":[137],"exists":[138],"between":[139],"Boost":[143,194],"Procedure,":[145],"an":[148],"accuracy":[149,199],"83.11":[151],"%,":[152],"Algorithm,":[157],"$77.76":[161],"\\%$":[162],"accuracy.":[163],"Statistically":[165],"Gradient":[167],"Boosting":[168],"Algorithm":[170],"p=0.002(p<0.05)":[174],"by":[175],"Independent":[176],"T-test":[177],"analysis.":[178],"suggests":[180],"that":[181],"differ":[185],"significantly":[187],"from":[188],"one":[189],"another.":[190],"Conclusion:":[191],"Innovative":[192],"more":[198],"than":[200]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}