{"id":"https://openalex.org/W4415398692","doi":"https://doi.org/10.1109/spml66318.2025.11199840","title":"A Pigment Concentration Prediction Model Based on an Improved Random Forest","display_name":"A Pigment Concentration Prediction Model Based on an Improved Random Forest","publication_year":2025,"publication_date":"2025-07-15","ids":{"openalex":"https://openalex.org/W4415398692","doi":"https://doi.org/10.1109/spml66318.2025.11199840"},"language":null,"primary_location":{"id":"doi:10.1109/spml66318.2025.11199840","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spml66318.2025.11199840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 8th International Conference on Signal Processing and Machine Learning (SPML)","raw_type":"proceedings-article"},"type":"article","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/A5113373278","display_name":"Yufei Lu","orcid":"https://orcid.org/0009-0007-2169-6261"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangu Lu","raw_affiliation_strings":["School of International Education, Guangdong University of Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of International Education, Guangdong University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenshu Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenshu Huang","raw_affiliation_strings":["School of International Education, Guangdong University of Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of International Education, Guangdong University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040238827","display_name":"Kaiquan Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiquan Fang","raw_affiliation_strings":["School of International Education, Guangdong University of Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of International Education, Guangdong University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110157524","display_name":"Xuan Xiong","orcid":null},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiong Xuan","raw_affiliation_strings":["School of International Education, Guangdong University of Technology,Guangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of International Education, Guangdong University of Technology,Guangzhou,China","institution_ids":["https://openalex.org/I139024713"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30792422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"497","last_page":"502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.7559999823570251,"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"}},"topics":[{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.7559999823570251,"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/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.7357000112533569,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing 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/random-forest","display_name":"Random forest","score":0.75},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5049999952316284},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4812999963760376},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4742000102996826},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4348999857902527},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.42289999127388},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.38440001010894775},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.38089999556541443}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.75},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5049999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49480000138282776},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4867999851703644},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4812999963760376},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4742000102996826},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.42289999127388},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38440001010894775},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.38089999556541443},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3555000126361847},{"id":"https://openalex.org/C73782692","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Random error","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33570000529289246},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.3167000114917755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31540000438690186},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3142000138759613},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.28540000319480896},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C48548287","wikidata":"https://www.wikidata.org/wiki/Q6803557","display_name":"Mean-shift","level":3,"score":0.28130000829696655},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2612000107765198}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/spml66318.2025.11199840","is_oa":false,"landing_page_url":"https://doi.org/10.1109/spml66318.2025.11199840","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 8th International Conference on Signal Processing and Machine Learning (SPML)","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":15,"referenced_works":["https://openalex.org/W3096717952","https://openalex.org/W3103897629","https://openalex.org/W3116250684","https://openalex.org/W3123908207","https://openalex.org/W3200110661","https://openalex.org/W4200160957","https://openalex.org/W4220934548","https://openalex.org/W4225392871","https://openalex.org/W4281490773","https://openalex.org/W4290735133","https://openalex.org/W4293581639","https://openalex.org/W4296551003","https://openalex.org/W4309576537","https://openalex.org/W4320180452","https://openalex.org/W4386857857"],"related_works":[],"abstract_inverted_index":{"Industrial":[0],"color":[1,27,72,89,164],"matching":[2,28,90,165],"plays":[3],"a":[4,46,75,86,98,105,113],"critical":[5],"role":[6],"in":[7,37,128,161],"applications":[8],"such":[9],"as":[10],"paints,":[11],"textiles,":[12],"and":[13,41,74,112,155],"plastics.":[14],"Achieving":[15],"accurate":[16],"pigment":[17,47],"concentration":[18,48],"prediction":[19,49],"is":[20],"essential":[21],"for":[22,64,70,158],"the":[23,26,32,94,132,141,148,153],"automation":[24],"of":[25,34,39,103,110,115,120,144,152],"process.":[29],"To":[30],"address":[31],"limitations":[33],"traditional":[35],"methods":[36],"terms":[38],"accuracy":[40,129],"generalization,":[42],"this":[43],"paper":[44],"presents":[45],"model":[50,60,96,154],"based":[51],"on":[52,85],"an":[53,124],"enhanced":[54,95],"Random":[55,134],"Forest":[56,135],"algorithm.":[57],"The":[58],"proposed":[59],"incorporates":[61],"wavelet":[62],"transform":[63],"noise":[65],"suppression,":[66],"embedding-based":[67],"feature":[68],"enhancement":[69],"improved":[71],"representation,":[73],"clustering":[76],"mechanism":[77],"to":[78,131],"strengthen":[79],"local":[80],"pattern":[81],"learning.":[82],"Experiments":[83],"conducted":[84],"self-constructed":[87],"industrial":[88,163],"dataset":[91],"demonstrate":[92],"that":[93],"achieves":[97],"mean":[99,106],"absolute":[100],"error":[101,108],"(MAE)":[102],"0.44,":[104],"squared":[107],"(MSE)":[109],"0.92,":[111],"coefficient":[114],"determination":[116],"<tex":[117],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[118],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\left(\\mathrm{R}^{\\mathrm{2}}\\right)$</tex>":[119],"0.99":[121],"\u2014representing":[122],"over":[123],"80":[125],"%":[126],"improvement":[127],"compared":[130],"baseline":[133],"model.":[136],"Ablation":[137],"studies":[138],"further":[139],"confirm":[140],"individual":[142],"contributions":[143],"each":[145],"component,":[146],"highlighting":[147],"strong":[149],"predictive":[150],"performance":[151],"its":[156],"potential":[157],"practical":[159],"application":[160],"intelligent":[162],"systems.":[166]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-22T00:00:00"}