{"id":"https://openalex.org/W4387702018","doi":"https://doi.org/10.3390/rs15204997","title":"Novel Hybrid Model to Estimate Leaf Carotenoids Using Multilayer Perceptron and PROSPECT Simulations","display_name":"Novel Hybrid Model to Estimate Leaf Carotenoids Using Multilayer Perceptron and PROSPECT Simulations","publication_year":2023,"publication_date":"2023-10-17","ids":{"openalex":"https://openalex.org/W4387702018","doi":"https://doi.org/10.3390/rs15204997"},"language":"en","primary_location":{"id":"doi:10.3390/rs15204997","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15204997","pdf_url":"https://www.mdpi.com/2072-4292/15/20/4997/pdf?version=1697547301","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/20/4997/pdf?version=1697547301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002350481","display_name":"Weilin Hao","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weilin Hao","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing 100871, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025217236","display_name":"Jia Sun","orcid":"https://orcid.org/0000-0002-3438-364X"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jia Sun","raw_affiliation_strings":["School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"],"affiliations":[{"raw_affiliation_string":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101854253","display_name":"Zichao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zichao Zhang","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing 100871, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100693841","display_name":"Kan Zhang","orcid":"https://orcid.org/0000-0001-7656-8952"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kan Zhang","raw_affiliation_strings":["Suzhou Enterprise Credit Service Co., Ltd., Suzhou 215000, China"],"affiliations":[{"raw_affiliation_string":"Suzhou Enterprise Credit Service Co., Ltd., Suzhou 215000, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036064800","display_name":"Feng Qiu","orcid":"https://orcid.org/0000-0002-0422-3493"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Qiu","raw_affiliation_strings":["International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"],"affiliations":[{"raw_affiliation_string":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059434416","display_name":"Jin Xu","orcid":"https://orcid.org/0000-0002-6377-1190"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Xu","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing 100871, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5025217236"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":3.1124,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.91417058,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"15","issue":"20","first_page":"4997","last_page":"4997"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T14365","display_name":"Leaf Properties and Growth Measurement","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T10616","display_name":"Smart Agriculture and AI","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6447768211364746},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5902374982833862},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5893535614013672},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5136188864707947},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.49287986755371094},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4893590807914734},{"id":"https://openalex.org/keywords/lookup-table","display_name":"Lookup table","score":0.43963316082954407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40170592069625854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3505047559738159},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33257856965065},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.30134740471839905},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23968756198883057},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10793432593345642},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09726855158805847}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6447768211364746},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5902374982833862},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5893535614013672},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5136188864707947},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.49287986755371094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4893590807914734},{"id":"https://openalex.org/C134835016","wikidata":"https://www.wikidata.org/wiki/Q690265","display_name":"Lookup table","level":2,"score":0.43963316082954407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40170592069625854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3505047559738159},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33257856965065},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30134740471839905},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23968756198883057},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10793432593345642},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09726855158805847},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs15204997","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15204997","pdf_url":"https://www.mdpi.com/2072-4292/15/20/4997/pdf?version=1697547301","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:16297814e6824331a6545f95b431f05a","is_oa":true,"landing_page_url":"https://doaj.org/article/16297814e6824331a6545f95b431f05a","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":"Remote Sensing, Vol 15, Iss 20, p 4997 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs15204997","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15204997","pdf_url":"https://www.mdpi.com/2072-4292/15/20/4997/pdf?version=1697547301","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.7699999809265137}],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G135951260","display_name":null,"funder_award_id":"62272009","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G1923979980","display_name":null,"funder_award_id":"62172014","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G2082318887","display_name":null,"funder_award_id":"42001314","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3092615922","display_name":null,"funder_award_id":"6227200","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4412229723","display_name":null,"funder_award_id":"62272009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4964365975","display_name":null,"funder_award_id":"62172014","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5561738421","display_name":null,"funder_award_id":"2019YFA0706401","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G585525231","display_name":null,"funder_award_id":"62002002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5996605579","display_name":null,"funder_award_id":"62002002","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6224804700","display_name":null,"funder_award_id":"2019YFA0706401","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6681604972","display_name":null,"funder_award_id":"62172015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6971149800","display_name":null,"funder_award_id":"62172015","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8785782320","display_name":null,"funder_award_id":"42001314","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387702018.pdf"},"referenced_works_count":77,"referenced_works":["https://openalex.org/W248389711","https://openalex.org/W633320881","https://openalex.org/W1498436455","https://openalex.org/W1532517383","https://openalex.org/W1836465849","https://openalex.org/W1972539809","https://openalex.org/W1977177161","https://openalex.org/W1978160572","https://openalex.org/W1981590796","https://openalex.org/W1998718573","https://openalex.org/W2030274299","https://openalex.org/W2038137144","https://openalex.org/W2051128904","https://openalex.org/W2063907334","https://openalex.org/W2066612219","https://openalex.org/W2076063813","https://openalex.org/W2081887174","https://openalex.org/W2108275917","https://openalex.org/W2118791227","https://openalex.org/W2121025745","https://openalex.org/W2121102297","https://openalex.org/W2124937378","https://openalex.org/W2125230412","https://openalex.org/W2125257725","https://openalex.org/W2137983211","https://openalex.org/W2138399273","https://openalex.org/W2152164823","https://openalex.org/W2152424523","https://openalex.org/W2167881994","https://openalex.org/W2243927384","https://openalex.org/W2308983515","https://openalex.org/W2413379912","https://openalex.org/W2461497717","https://openalex.org/W2596051487","https://openalex.org/W2770333615","https://openalex.org/W2775006744","https://openalex.org/W2794163091","https://openalex.org/W2800158423","https://openalex.org/W2804032941","https://openalex.org/W2804241367","https://openalex.org/W2901420733","https://openalex.org/W2904894076","https://openalex.org/W2906508587","https://openalex.org/W2911886196","https://openalex.org/W2913323966","https://openalex.org/W2919115771","https://openalex.org/W2955723217","https://openalex.org/W2975790795","https://openalex.org/W2989585671","https://openalex.org/W2995089028","https://openalex.org/W3002090371","https://openalex.org/W3010212250","https://openalex.org/W3011511010","https://openalex.org/W3012125688","https://openalex.org/W3044137096","https://openalex.org/W3046161533","https://openalex.org/W3073865269","https://openalex.org/W3090525814","https://openalex.org/W3105998209","https://openalex.org/W3123813572","https://openalex.org/W3124990706","https://openalex.org/W3125537303","https://openalex.org/W3128164063","https://openalex.org/W3199988365","https://openalex.org/W3208500438","https://openalex.org/W4200412506","https://openalex.org/W4205686602","https://openalex.org/W4205947740","https://openalex.org/W4212929620","https://openalex.org/W4294643443","https://openalex.org/W4297989294","https://openalex.org/W4313889909","https://openalex.org/W4316506822","https://openalex.org/W4384938791","https://openalex.org/W6739879593","https://openalex.org/W6808577523","https://openalex.org/W6842890020"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2922073769","https://openalex.org/W4322009192","https://openalex.org/W4320483926","https://openalex.org/W2559793074","https://openalex.org/W3203207972","https://openalex.org/W4384209958","https://openalex.org/W4327814505","https://openalex.org/W4312532313"],"abstract_inverted_index":{"Leaf":[0],"carotenoids":[1],"(Cxc)":[2],"play":[3],"a":[4,25,78,111,263],"crucial":[5],"role":[6],"in":[7,64,88,222],"vegetation":[8,69,275],"as":[9,31,262],"essential":[10],"pigments":[11],"responsible":[12],"for":[13,180,266],"capturing":[14],"sunlight":[15],"and":[16,29,55,86,158,177,190,211,238,277],"protecting":[17],"leaf":[18,44,181],"tissues.":[19],"They":[20],"provide":[21],"vital":[22],"insights":[23,273],"into":[24,274],"plant":[26,35],"physiological":[27],"status":[28],"serve":[30],"sensitive":[32],"indicators":[33],"of":[34,40,62,133,167,187,192,225,244,256],"stress.":[36],"However,":[37],"remote":[38],"sensing":[39],"Cxc":[41,53,182,268],"at":[42],"the":[43,51,65,118,134,142,151,159,164,171,233,242,245,254,257],"level":[45],"has":[46],"been":[47,72],"challenging":[48],"due":[49],"to":[50,60,127],"low":[52],"content":[54,183],"weaker":[56],"absorption":[57],"features":[58],"compared":[59],"those":[61],"chlorophylls":[63],"visible":[66],"domain.":[67],"Existing":[68],"indices":[70],"have":[71],"widely":[73],"applied":[74],"but":[75],"often":[76],"lack":[77],"solid":[79],"physical":[80,92],"foundation,":[81],"which":[82],"limits":[83],"their":[84],"applicability":[85],"robustness":[87,176],"characterizing":[89],"Cxc.":[90,130],"Yet,":[91],"models":[93],"can":[94],"confront":[95],"this":[96,106,108],"ill-posed":[97],"problem,":[98],"though":[99],"with":[100,123,141,185,229],"high":[101],"operational":[102],"costs.":[103],"To":[104],"address":[105],"issue,":[107],"study":[109],"presents":[110],"novel":[112],"hybrid":[113,156,161,260],"inversion":[114,146],"method":[115,136,173,247,261],"that":[116],"combines":[117],"multilayer":[119],"perceptron":[120],"(MLP)":[121],"algorithm":[122],"PROSPECT":[124,144],"model":[125,145],"simulations":[126],"accurately":[128],"retrieve":[129],"The":[131,194],"effectiveness":[132],"MLP":[135,172,246,259],"was":[137],"investigated":[138],"through":[139],"comparisons":[140],"classical":[143],"(look-up":[147],"table":[148],"[LUT]":[149],"method),":[150],"convolutional":[152],"neural":[153],"network":[154],"(CNN)":[155],"model,":[157],"Transformer":[160,195],"model.":[162],"In":[163],"pooled":[165],"results":[166],"six":[168],"experimental":[169],"datasets,":[170],"exhibited":[174],"its":[175],"generalization":[178],"capabilities":[179],"estimation,":[184],"RMSE":[186],"3.12":[188],"\u03bcg/cm2":[189],"R2":[191,200,208,217],"0.52.":[193],"(RMSE":[196,204,213],"=":[197,201,205,209,214,218],"3.14":[198],"\u03bcg/cm2,":[199,207,216],"0.46),":[202],"CNN":[203],"3.42":[206],"0.28),":[210],"LUT":[212],"3.82":[215],"0.24)":[219],"methods":[220],"followed":[221],"descending":[223],"order":[224],"accuracy.":[226],"A":[227],"comparison":[228],"previous":[230],"studies":[231],"using":[232],"same":[234],"public":[235],"datasets":[236],"(ANGERS":[237],"LOPEX)":[239],"also":[240],"demonstrated":[241],"performance":[243],"from":[248],"another":[249],"perspective.":[250],"These":[251],"findings":[252],"underscore":[253],"potential":[255],"proposed":[258],"powerful":[264],"tool":[265],"accurate":[267],"retrieval":[269],"applications,":[270],"providing":[271],"valuable":[272],"health":[276],"stress":[278],"response.":[279]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}