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dc.contributor.authorAbed, Ali K.
dc.contributor.authorQahwaji, Rami S.R.
dc.date.accessioned2022-03-15T21:54:32Z
dc.date.accessioned2022-03-18T09:10:52Z
dc.date.available2022-03-15T21:54:32Z
dc.date.available2022-03-18T09:10:52Z
dc.date.issued2020
dc.identifier.citationAbed AK and Qahwaji RSR (2020) The Automated Solar Activity Prediction System (ASAP) Update Based on Optimization of a Machine Learning Approach. In: Arai K, Kapoor S and Bhatia R (Eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing. Vol 1230. Switzerland, Cham: Springer. 702-717.en_US
dc.identifier.urihttp://hdl.handle.net/10454/18789
dc.descriptionNoen_US
dc.description.abstractQuite recently, considerable attention has been paid to solar flare prediction because extreme solar eruptions could affect our daily life activities and on different technologies. Therefore, this paper presents a novel method of the development of improved second-generation of the Automated Solar Activity Prediction system (ASAP). The suggested algorithm improves the ASAP system by expanding a period of training vector and generating new machine learning rules to be more successful. Two neural networks are responsible for determining whether the sunspots group will release flare as well as determining if the flare is an M-class or X-class. Several measurement criteria are applied to determine the extent of system performance also all results are provided in this paper. Furthermore, the quadratic score (QR) is used as a metric criterion to compare between the prediction of the proposed algorithm with the Space Weather Prediction Center (SWPC) between 2012 and 2013. The results exhibit that the proposed algorithm outperforms the old ASAP system. Keywords: Solar flares, Machine Learning, Neural network, Space, Prediction, weather.en_US
dc.language.isoenen_US
dc.subjectAutomated solar activity predictionen_US
dc.subjectMcIntosh classificationsen_US
dc.subjectNeural networksen_US
dc.subjectSunspoten_US
dc.titleThe Automated Solar Activity Prediction System (ASAP) Update Based on Optimization of a Machine Learning Approachen_US
dc.status.refereedYesen_US
dc.date.Accepted2020
dc.date.application2020-07-04
dc.typeConference paperen_US
dc.type.versionNo full-text in the repositoryen_US
dc.identifier.doihttps://doi.org/10.1007/978-3-030-52243-8_53
dc.rights.licenseUnspecifieden_US
dc.date.updated2022-03-15T21:54:33Z
refterms.dateFOA2022-03-18T09:11:18Z
dc.openaccess.statusclosedAccessen_US


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