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Instillation checking using long short-term memories for ophthalmology patients., , , , и . Concurr. Comput. Pract. Exp., (2023)Discrimination Ability of Glaucoma via DCNNs Models from Ultra-Wide Angle Fundus Images Comparing Either Full or Confined to the Optic Disc., , , , и . ACCV Workshops, том 11367 из Lecture Notes in Computer Science, стр. 229-234. Springer, (2018)Ophthalmological Examination Determination Using Data Classification Based on Feedforward Neural Networks., , , , , , и . SMC, стр. 888-893. IEEE, (2018)On Biometric Identification Using Optical-Coherence-Tomography Data for Ophthalmic Patients., , , , и . SCIS&ISIS, стр. 1358-1363. IEEE, (2018)On Selection of Intraocular Power Formula Using Support Vector Machines and Genetic Algorithm., , , , , и . ISMVL, стр. 65-70. IEEE Computer Society, (2013)Data-Classification-Based Determination for Ophthalmological Examination Categories using Machine Learning., , , и . FLAP, 9 (3): 691-710 (2022)Formula selection for intraocular power calculation using support vector machines and self-organizing maps., , , , , и . SMC, стр. 1111-1116. IEEE, (2011)On expectation of binding time for ophthalmological outpatients., , , , и . SMC, стр. 3077-3082. IEEE, (2017)On Neural-Network-Based Detection for Hypertensive Subjects Using Classification of Retinal Fundus Photographs., , , и . ISMVL, стр. 42-47. IEEE, (2023)Instillation Checking Using Long Short-Term Memories for Ophthalmology Patients., , , , и . CANDAR (Workshops), стр. 152-158. IEEE, (2021)