{"id":"https://openalex.org/W7151382694","doi":"https://doi.org/10.1109/icmla66185.2025.00206","title":"A Multimodal Machine Learning Approach for Solar Flare Prediction Using Polarity Inversion Lines Data with Low-Rank Fusion","display_name":"A Multimodal Machine Learning Approach for Solar Flare Prediction Using Polarity Inversion Lines Data with Low-Rank Fusion","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W7151382694","doi":"https://doi.org/10.1109/icmla66185.2025.00206"},"language":null,"primary_location":{"id":"doi:10.1109/icmla66185.2025.00206","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00206","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","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/A5098789088","display_name":"Ziba Khani","orcid":"https://orcid.org/0000-0003-2058-7008"},"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":"Ziba Khani","raw_affiliation_strings":["Georgia State University,Department of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Department of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087160893","display_name":"Reza Mansouri","orcid":"https://orcid.org/0000-0001-6007-6566"},"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":"Reza Mansouri","raw_affiliation_strings":["Georgia State University,Department of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Department 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,Department of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Department of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133103765","display_name":"Berkay Aydin","orcid":null},"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,Department of Computer Science,Atlanta,GA,USA"],"affiliations":[{"raw_affiliation_string":"Georgia State University,Department of Computer Science,Atlanta,GA,USA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5098789088"],"corresponding_institution_ids":["https://openalex.org/I181565077"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76994539,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1346","last_page":"1353"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10251","display_name":"Solar and Space Plasma Dynamics","score":0.7651000022888184,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10251","display_name":"Solar and Space Plasma Dynamics","score":0.7651000022888184,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.08940000087022781,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12424","display_name":"Earthquake Detection and Analysis","score":0.010400000028312206,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"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/inversion","display_name":"Inversion (geology)","score":0.5817000269889832},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.47119998931884766},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4120999872684479},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3896999955177307},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.3828999996185303},{"id":"https://openalex.org/keywords/flare","display_name":"Flare","score":0.38019999861717224}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6097000241279602},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.5817000269889832},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4291999936103821},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3978999853134155},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C2779588948","wikidata":"https://www.wikidata.org/wiki/Q628261","display_name":"Flare","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C2777361361","wikidata":"https://www.wikidata.org/wiki/Q1112585","display_name":"Polarity (international relations)","level":3,"score":0.3368000090122223},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.29989999532699585},{"id":"https://openalex.org/C185001636","wikidata":"https://www.wikidata.org/wiki/Q119830","display_name":"Solar flare","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2930000126361847},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2621999979019165}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla66185.2025.00206","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00206","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.44227761030197144,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"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":16,"referenced_works":["https://openalex.org/W2034895394","https://openalex.org/W2097117768","https://openalex.org/W2793171700","https://openalex.org/W2800761015","https://openalex.org/W2905502273","https://openalex.org/W2963446712","https://openalex.org/W2964010806","https://openalex.org/W2964346351","https://openalex.org/W3008201674","https://openalex.org/W3041430681","https://openalex.org/W3112722658","https://openalex.org/W3157917947","https://openalex.org/W4283022822","https://openalex.org/W4323565727","https://openalex.org/W4404612058","https://openalex.org/W7115566228"],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"solar":[1,261],"flares":[2,105],"is":[3],"a":[4,35,40,78,91,139,147,157,191,235],"central":[5],"problem":[6],"in":[7,20,38,138],"space":[8],"weather":[9],"forecasting":[10,263],"that":[11,96,185,248],"has":[12],"drawn":[13],"increased":[14],"research":[15],"interest":[16],"due":[17],"to":[18,98,161],"advances":[19],"data-driven":[21],"learning":[22],"models":[23],"and":[24,56,65,84,124,142,176,244],"novel":[25],"datasets":[26],"integrated":[27],"from":[28,50,135,228],"heterogeneous":[29],"data":[30,123,221],"sources.":[31],"This":[32,111,254],"paper":[33],"marks":[34],"pioneering":[36],"effort":[37],"leveraging":[39],"rich":[41],"dataset":[42],"of":[43,63,69,82,87,102,166,196,202,214,234],"magnetic":[44,224],"polarity":[45,225],"inversion":[46,226],"lines":[47,227],"(MPILs)":[48],"derived":[49],"the":[51,66,100,107,120,130,152,163,170,186,208,241,257],"Solar":[52],"Dynamics":[53],"Observatory\u2019s":[54],"Helioseismic":[55],"Magnetic":[57],"Imager":[58],"(SDO/HMI).":[59],"We":[60,89,155],"utilize":[61],"rasters":[62],"MPILs":[64,70],"convex":[67],"hull":[68],"as":[71],"two":[72],"distinct":[73],"spatial":[74,131],"modalities,":[75],"along":[76],"with":[77],"multivariate":[79],"time":[80,121],"series":[81,122],"physical":[83],"shape-based":[85],"characteristics":[86],"MPILs.":[88],"introduce":[90],"new":[92],"multimodal":[93,188,220,236,249,265],"prediction":[94,153,238],"model":[95,112,189,239],"aims":[97],"predict":[99],"occurrence":[101],"\u2265M1.0":[103],"class":[104],"within":[106],"next":[108],"24":[109],"hours.":[110],"utilizes":[113],"Long":[114],"Short-Term":[115],"Memory":[116],"(LSTM)":[117],"networks":[118],"for":[119,129,151,259],"Convolutional":[125],"Neural":[126],"Networks":[127],"(CNN)":[128],"data,":[132],"extracting":[133],"features":[134],"each":[136],"modality":[137],"self-supervised":[140],"manner":[141],"combining":[143],"them":[144],"by":[145],"employing":[146],"low-rank":[148],"fusion":[149],"method":[150],"task.":[154],"employ":[156],"chronological":[158],"partitioning":[159],"strategy":[160],"evaluate":[162],"predictive":[164,252],"skill":[165],"our":[167],"model,":[168],"using":[169,223],"established":[171],"True":[172,192],"Skill":[173,178,193,199],"Statistic":[174,194],"(TSS)":[175,195],"Heidke":[177,198],"Score":[179,200],"(HSS)":[180,201],"metrics.":[181],"Evaluation":[182],"results":[183],"show":[184],"proposed":[187],"achieves":[190],"0.44,":[197],"0.30,":[203],"outperforming":[204],"unimodal":[205],"baselines":[206],"on":[207],"same":[209],"dataset.":[210],"The":[211],"main":[212],"contributions":[213],"this":[215],"study":[216],"include:":[217],"(1)":[218],"innovative":[219],"integration":[222,250],"active":[229],"regions,":[230],"(2)":[231],"effective":[232],"implementation":[233],"flare":[237,262],"utilizing":[240],"MPIL":[242],"dataset,":[243],"(3)":[245],"empirical":[246],"validation":[247],"enhances":[251],"performance.":[253],"work":[255],"lays":[256],"groundwork":[258],"advancing":[260],"through":[264],"machine":[266],"learning.":[267]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2026-04-08T00:00:00"}