{"id":"https://openalex.org/W4403724012","doi":"https://doi.org/10.1109/dsaa61799.2024.10722839","title":"Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting","display_name":"Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting","publication_year":2024,"publication_date":"2024-10-06","ids":{"openalex":"https://openalex.org/W4403724012","doi":"https://doi.org/10.1109/dsaa61799.2024.10722839"},"language":"en","primary_location":{"id":"doi:10.1109/dsaa61799.2024.10722839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa61799.2024.10722839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)","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/A5042625709","display_name":"Chetraj Pandey","orcid":"https://orcid.org/0000-0002-4699-4050"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chetraj Pandey","raw_affiliation_strings":["Georgia State University,Dept. of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Dept. of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018886706","display_name":"Anli Ji","orcid":"https://orcid.org/0000-0002-1551-2370"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anli Ji","raw_affiliation_strings":["Georgia State University,Dept. of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Dept. of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029932684","display_name":"Jinsu Hong","orcid":"https://orcid.org/0009-0002-4383-1376"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinsu Hong","raw_affiliation_strings":["Georgia State University,Dept. of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Dept. of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009847987","display_name":"Rafal A. Angryk","orcid":"https://orcid.org/0000-0001-9598-8207"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rafal A. Angryk","raw_affiliation_strings":["Georgia State University,Dept. of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Dept. of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043887593","display_name":"Berkay Aydin","orcid":"https://orcid.org/0000-0002-9799-9265"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Berkay Aydin","raw_affiliation_strings":["Georgia State University,Dept. of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Dept. of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5042625709"],"corresponding_institution_ids":["https://openalex.org/I181565077"],"apc_list":null,"apc_paid":null,"fwci":0.4947,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.56567,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14224","display_name":"Oil, Gas, and Environmental Issues","score":0.9172999858856201,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14224","display_name":"Oil, Gas, and Environmental Issues","score":0.9172999858856201,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/solar-flare","display_name":"Solar flare","score":0.5753703713417053},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5544061660766602},{"id":"https://openalex.org/keywords/flare","display_name":"Flare","score":0.5507043600082397},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5347522497177124},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.47544169425964355},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.38987088203430176},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.377741277217865},{"id":"https://openalex.org/keywords/astrophysics","display_name":"Astrophysics","score":0.36253052949905396},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.34826648235321045},{"id":"https://openalex.org/keywords/astronomy","display_name":"Astronomy","score":0.32411256432533264},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21970710158348083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1445717215538025},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.06646472215652466}],"concepts":[{"id":"https://openalex.org/C185001636","wikidata":"https://www.wikidata.org/wiki/Q119830","display_name":"Solar flare","level":2,"score":0.5753703713417053},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5544061660766602},{"id":"https://openalex.org/C2779588948","wikidata":"https://www.wikidata.org/wiki/Q628261","display_name":"Flare","level":2,"score":0.5507043600082397},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5347522497177124},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.47544169425964355},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.38987088203430176},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.377741277217865},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.36253052949905396},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.34826648235321045},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.32411256432533264},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21970710158348083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1445717215538025},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.06646472215652466},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsaa61799.2024.10722839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsaa61799.2024.10722839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6700000166893005,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G4358589999","display_name":null,"funder_award_id":"2104004,1931555","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1958898028","https://openalex.org/W1966973332","https://openalex.org/W2012295549","https://openalex.org/W2033706575","https://openalex.org/W2122755404","https://openalex.org/W2129833860","https://openalex.org/W2165119014","https://openalex.org/W2194775991","https://openalex.org/W2332651617","https://openalex.org/W2883908877","https://openalex.org/W2905502273","https://openalex.org/W2963351448","https://openalex.org/W3007210650","https://openalex.org/W3046832359","https://openalex.org/W4205653606","https://openalex.org/W4212924687","https://openalex.org/W4226111913","https://openalex.org/W4243123773","https://openalex.org/W4291109640","https://openalex.org/W4291162302","https://openalex.org/W4312891673","https://openalex.org/W4381854179","https://openalex.org/W4385701252","https://openalex.org/W4387430300","https://openalex.org/W4388426030","https://openalex.org/W4390679445","https://openalex.org/W4391093559","https://openalex.org/W4391929877","https://openalex.org/W4392943649","https://openalex.org/W4392945891","https://openalex.org/W4402100980","https://openalex.org/W6630789969","https://openalex.org/W6654625237","https://openalex.org/W6659245018","https://openalex.org/W6683552499","https://openalex.org/W6730586566","https://openalex.org/W6740295617","https://openalex.org/W6749509129","https://openalex.org/W6750757783","https://openalex.org/W6774899255","https://openalex.org/W6784333009","https://openalex.org/W6785805631","https://openalex.org/W6791809772","https://openalex.org/W6795442437","https://openalex.org/W6850305021"],"related_works":["https://openalex.org/W2069584417","https://openalex.org/W2172141389","https://openalex.org/W2064117586","https://openalex.org/W2964166047","https://openalex.org/W2104143565","https://openalex.org/W1529285236","https://openalex.org/W2495253737","https://openalex.org/W3104157634","https://openalex.org/W1946485059","https://openalex.org/W4391503899"],"abstract_inverted_index":{"Several":[0],"natural":[1],"phenomena,":[2],"such":[3],"as":[4,69,76,204,215,222,252],"floods,":[5],"earth-quakes,":[6],"volcanic":[7],"eruptions,":[8],"or":[9,24,80,92],"extreme":[10],"space":[11],"weather":[12],"events":[13,33,75],"often":[14],"come":[15],"with":[16,54,151,178,307,338,360],"severity":[17],"indexes.":[18],"While":[19],"these":[20,32],"indexes,":[21],"whether":[22],"linear":[23],"logarithmic":[25],"are":[26,251],"vital,":[27],"data-driven":[28,52],"predictive":[29],"models":[30,371],"for":[31,285,320,350,372],"rather":[34],"use":[35,209],"a":[36,70,85,117,175,210,257,264,361],"fixed":[37],"threshold.":[38],"In":[39,112],"this":[40,45,95,113,249],"paper,":[41,114],"we":[42,115,173,342],"explore":[43],"encoding":[44],"ordinality":[46,101,262],"to":[47,147,181,200,238,260,271,346,358,385],"enhance":[48,277],"the":[49,99,103,124,131,137,149,156,160,164,168,187,223,227,233,289,308,344,367,378],"performance":[50,166],"of":[51,63,159,167,190,192,226,248,315,328,334,369,380],"models,":[53],"specific":[55],"application":[56,270],"in":[57,332,352],"solar":[58,64,202,272,278,291,329,373,381],"flare":[59,126,134,273,279,283,348,374,382],"forecasting.":[60],"The":[61,245],"prediction":[62,127,388],"flares":[65,184],"is":[66,145,220],"commonly":[67],"approached":[68],"binary":[71,96,107,125,138,265],"forecasting":[72,280],"problem,":[73],"categorizing":[74],"either":[77],"Flare":[78],"(FL)":[79],"No-Flare":[81],"(NF)":[82],"based":[83,154],"on":[84,155],"chosen":[86],"threshold":[87],"(e.g.,":[88],">C-class,":[89],">":[90],"M-class,":[91],">X-class).":[93],"However,":[94],"formulation":[97],"overlooks":[98],"inherent":[100],"between":[102,356],"sub-classes":[104],"within":[105,323],"each":[106,286],"class":[108],"(FL":[109],"and":[110,162,232,240,297,299,318,326,364],"NF).":[111],"propose":[116],"novel":[118,258],"loss":[119,141,266,310],"function":[120,267],"aimed":[121],"at":[122],"optimizing":[123],"problem":[128],"by":[129,185,281],"embedding":[130],"intrinsic":[132],"ordinal":[133,157],"characteristics":[135,158],"into":[136,263],"cross-entropy":[139],"(BCE)":[140],"function.":[142],"This":[143,376],"modification":[144],"intended":[146],"provide":[148],"model":[150,177],"better":[152],"guidance":[153],"data":[161],"improve":[163],"overall":[165],"models.":[169],"For":[170],"our":[171,205,216,242],"experiments,":[172],"employ":[174],"ResNet34-based":[176],"transfer":[179],"learning":[180],"predict":[182],"2:M-class":[183],"utilizing":[186],"shape-based":[188],"features":[189],"magnetograms":[191],"active":[193],"region":[194],"(AR)":[195],"patches":[196,322],"spanning":[197],"from":[198],"-90\u00b0":[199],"+90\u00b0of":[201],"longitude":[203],"input":[206],"data.":[207],"We":[208,255,276],"composite":[211],"skill":[212],"score":[213],"(CSS)":[214],"evaluation":[217],"metric,":[218],"which":[219],"calculated":[221],"geometric":[224],"mean":[225],"True":[228],"Skill":[229,235],"Score":[230,236],"(TSS)":[231],"Heidke":[234],"(HSS)":[237],"rank":[239],"compare":[241,300],"models'":[243],"performance.":[244,301],"primary":[246],"contributions":[247],"work":[250],"follows:":[253],"(i)":[254],"introduce":[256],"approach":[259],"encode":[261],"showing":[268],"an":[269,313],"prediction,":[274],"(ii)":[275],"enabling":[282],"predictions":[284],"AR":[287,321],"across":[288],"entire":[290],"disk,":[292],"without":[293],"any":[294],"longitudinal":[295],"restrictions,":[296],"evaluate":[298],"(iii)":[302],"Our":[303],"candidate":[304],"model,":[305],"optimized":[306],"proposed":[309],"function,":[311],"shows":[312],"improvement":[314],"(~17%,":[316],"(~14%,":[317],"(~13%":[319],"\u00b130\u00b0,":[324],"\u00b160\u00b0,":[325],"\u00b190\u00b0":[327],"longitude,":[330],"respectively":[331],"terms":[333],"CSS,":[335],"when":[336],"compared":[337],"standard":[339],"BCE.":[340],"Additionally,":[341],"demonstrate":[343],"ability":[345],"issue":[347],"forecasts":[349],"ARs":[351],"near-limb":[353],"regions":[354],"(regions":[355],"\u00b160\u00b0":[357],"\u00b190\u00b0)":[359],"CSS=0.34":[362],"(TSS=0.50":[363],"HSS=0.23),":[365],"expanding":[366],"scope":[368],"AR-based":[370],"prediction.":[375],"advances":[377],"reliability":[379],"forecasts,":[383],"leading":[384],"more":[386],"effective":[387],"capabilities.":[389]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}