A Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All_Sky Imagers

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dc.contributor.authorDaniel Okoh
dc.contributor.authorClaudio Cesaroni
dc.contributor.authorBabatunde Rabiu
dc.contributor.authorKazuo Shiokawa
dc.contributor.authorYuichi Otsuka
dc.contributor.authorSamuel Ogunjo
dc.contributor.authorAderonke Akerele
dc.contributor.authorJohn Bosco Habarulema
dc.contributor.authorBruno Nava
dc.contributor.authorYenca Migoya_Oru�
dc.contributor.authorPunyawi Jamjareegulgarn
dc.contributor.authorAdeniran Seun
dc.contributor.authorOgechi Adama
dc.contributor.authorGeorge Ochieng
dc.contributor.authorJames Ameh
dc.contributor.authorAdero Awuor
dc.contributor.authorPaul Baki
dc.date.accessioned2025-07-21T06:12:58Z
dc.date.issued2025-06-01
dc.identifier.doi10.1029/2024ea004117
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14442
dc.subjectBootstrapping (finance)
dc.subject.classificationAtmospheric and Environmental Gas Dynamics
dc.titleA Bootstrapping Convolutional Neural Network Technique for Optimizing Automated Detection of Equatorial Plasma Bubbles by Optical All_Sky Imagers
dc.typeArticle

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