{"id":"https://openalex.org/W4391793071","doi":"https://doi.org/10.1109/icceic60201.2023.10426733","title":"Analyzing and Comparing Results for Multiple Machine Learning Models","display_name":"Analyzing and Comparing Results for Multiple Machine Learning Models","publication_year":2023,"publication_date":"2023-10-20","ids":{"openalex":"https://openalex.org/W4391793071","doi":"https://doi.org/10.1109/icceic60201.2023.10426733"},"language":"en","primary_location":{"id":"doi:10.1109/icceic60201.2023.10426733","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icceic60201.2023.10426733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC)","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/A5110477778","display_name":"Jefferson Ng","orcid":null},"institutions":[{"id":"https://openalex.org/I204250578","display_name":"University of California, Irvine","ror":"https://ror.org/04gyf1771","country_code":"US","type":"education","lineage":["https://openalex.org/I204250578"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jefferson Ng","raw_affiliation_strings":["University of California,Department of Mechanical Engineering,Irvine,USA,92697"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California,Department of Mechanical Engineering,Irvine,USA,92697","institution_ids":["https://openalex.org/I204250578"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101800261","display_name":"Yijian Li","orcid":"https://orcid.org/0000-0002-3361-1786"},"institutions":[{"id":"https://openalex.org/I36672615","display_name":"Courant Institute of Mathematical Sciences","ror":"https://ror.org/037tm7f56","country_code":"US","type":"education","lineage":["https://openalex.org/I36672615","https://openalex.org/I57206974"]},{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yijian Li","raw_affiliation_strings":["Courant Institute of Mathematical Sciences, New York University,NY,USA,10012"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Courant Institute of Mathematical Sciences, New York University,NY,USA,10012","institution_ids":["https://openalex.org/I36672615","https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101304544","display_name":"Xuxiao Ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuxiao Ji","raw_affiliation_strings":["University of Waterloo,School of Computer Science,Waterloo,CA,N2L 3G1"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo,School of Computer Science,Waterloo,CA,N2L 3G1","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100458609","display_name":"Can Yang","orcid":"https://orcid.org/0000-0003-1950-114X"},"institutions":[{"id":"https://openalex.org/I131249849","display_name":"Oregon State University","ror":"https://ror.org/00ysfqy60","country_code":"US","type":"education","lineage":["https://openalex.org/I131249849"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Can Yang","raw_affiliation_strings":["Oregon State University,Department of Electrical Engineering and Computer Science,Corvallis,USA,97331"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oregon State University,Department of Electrical Engineering and Computer Science,Corvallis,USA,97331","institution_ids":["https://openalex.org/I131249849"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101304545","display_name":"Jiaqiao Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqiao Wan","raw_affiliation_strings":["Zhengzhou University,Software College,Zhengzhou,China,450000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou University,Software College,Zhengzhou,China,450000","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030485564","display_name":"Xingyu Lu","orcid":"https://orcid.org/0000-0002-8393-0582"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xingyu Lu","raw_affiliation_strings":["United World College Changshu China,Jiangsu,China,215500"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"United World College Changshu China,Jiangsu,China,215500","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":"163","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7772996425628662},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7563018798828125},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.7005161643028259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6559122204780579},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5636329650878906},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.551089882850647},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5133835077285767},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.5120289921760559},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.49440813064575195},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.42333346605300903},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3504166007041931},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34511756896972656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7772996425628662},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7563018798828125},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.7005161643028259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6559122204780579},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5636329650878906},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.551089882850647},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5133835077285767},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.5120289921760559},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.49440813064575195},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.42333346605300903},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3504166007041931},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34511756896972656},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icceic60201.2023.10426733","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icceic60201.2023.10426733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2896886654","https://openalex.org/W2915485951","https://openalex.org/W2954996726","https://openalex.org/W3040231701","https://openalex.org/W3153343195","https://openalex.org/W4224251796","https://openalex.org/W4225614409","https://openalex.org/W4297683907"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2989490741","https://openalex.org/W3092506759","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W2367545121","https://openalex.org/W4248881655","https://openalex.org/W2482165163","https://openalex.org/W3010890513"],"abstract_inverted_index":{"In":[0],"the":[1,7,23,47,57,62,85,126,141],"contemporary":[2],"landscape":[3],"of":[4,35,49,59,107,129],"atmospheric":[5],"sciences,":[6],"ability":[8],"to":[9,22,70,81],"recognize":[10],"specific":[11],"weather":[12,50,73,164],"phenomena":[13],"accurately":[14],"and":[15,41,53,118,134,154,161],"efficiently":[16],"has":[17],"become":[18],"paramount,":[19],"largely":[20],"due":[21],"exponential":[24],"growth":[25],"in":[26,75,159],"sensor-generated":[27],"data.":[28],"This":[29],"paper":[30],"undertakes":[31],"an":[32],"in-depth":[33],"analysis":[34],"advanced":[36],"algorithms,":[37],"namely":[38],"YOLOV8,":[39],"ResNet50,":[40],"Convolutional":[42],"Neural":[43],"Networks":[44],"(CNN),":[45],"for":[46,102],"purpose":[48],"pattern":[51],"identification":[52],"classification.":[54],"Building":[55],"on":[56,125,140],"foundation":[58],"deep":[60],"learning,":[61],"YOLOV8's":[63],"real-time":[64],"object":[65],"detection":[66],"capabilities":[67,158],"are":[68],"leveraged":[69],"discern":[71],"intricate":[72,163],"patterns":[74],"diverse":[76],"datasets,":[77],"from":[78],"meteorological":[79],"stations":[80],"satellite":[82],"imagery.":[83],"On":[84],"other":[86],"hand,":[87],"Resnet":[88],"50":[89],"which":[90],"is":[91,123],"another":[92],"CNN":[93,101,155],"based":[94],"model":[95,135],"was":[96],"also":[97],"tested":[98],"along":[99],"with":[100],"comparison.":[103],"An":[104],"exhaustive":[105],"evaluation":[106],"these":[108],"algorithms":[109],"covers":[110],"various":[111],"metrics,":[112],"including":[113],"accuracy,":[114],"precision,":[115],"computational":[116],"efficiency,":[117],"real-world":[119],"applicability.":[120],"Special":[121],"emphasis":[122],"placed":[124],"crucial":[127],"stages":[128],"data":[130],"preprocessing,":[131],"feature":[132],"extraction,":[133],"tuning,":[136],"highlighting":[137],"their":[138],"impact":[139],"algorithms'":[142],"overall":[143],"performance.":[144,171],"Our":[145],"findings":[146],"suggest":[147],"that,":[148],"when":[149],"appropriately":[150],"optimized,":[151],"both":[152],"YOLOV8":[153],"exhibit":[156],"exceptional":[157],"discerning":[160],"classifying":[162],"patterns,":[165],"whereas":[166],"ResNet50":[167],"exhibits":[168],"comparatively":[169],"less":[170]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
