dc.creatorAhumada-García, Roberto
dc.creatorMorán Faúndez, Esteban
dc.creatorZabala-Blanco, David
dc.creatorLópez-Cortés, Xaviera A.
dc.creatorRivelli Malco, Juan Pablo
dc.creatorSoto, Ismael
dc.date2024-01-16T17:40:04Z
dc.date2024-01-16T17:40:04Z
dc.date2023
dc.date.accessioned2024-05-02T20:32:03Z
dc.date.available2024-05-02T20:32:03Z
dc.identifierhttp://repositorio.ucm.cl/handle/ucm/5180
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9275365
dc.descriptionThe protection of planet Earth, its inhabitants, and all living beings requires the identification of potentially dangerous objects, the simulation of impacts with Earth, and the mitigation of such threats. This research proposes the use of ELMs to distinguish between potentially dangerous objects and those that are not. The ELMs applied in this study include the standard ELM, the Regularized ELM, and the Weighted ELM (in versions W 1 and W 2 ). For the training and validation of the hazard object classification models, the “Nearest Earth Objects” database from NASA, available on Kaggle, was used. From this database, five features of the objects and a binary output indicating the danger or not towards Earth were used. The models were evaluated based on accuracy, geometric mean, and training time. According to the results, the Weighted ELM in its W 1 version offers the best performance, as it is capable of more effectively classifying dangerous and non-dangerous objects for Earth, with an accuracy of 0.8, a geometric mean of 0.7, and a training time of 1.8 seconds. Based on the results obtained, the viability of classifying whether objects are potentially dangerous for Earth is confirmed. However, to increase the performance of the models, it is recommended to continue exploring other types of ELMs.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceProceedings - International Conference of the Chilean Computer Science Society, SCCC, 2023, 1-6
dc.subjectEarth
dc.subjectTraining
dc.subjectComputer science
dc.subjectDatabases
dc.subjectExtreme learning machines
dc.subjectComputational modeling
dc.subjectNASA
dc.titleExtreme learning machine (ELM) for detection of hazardous near Earth objects
dc.typeArticle


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