المؤلفون: Christian Arthur, Novanto Yudistira, Candra Dewi

المصدر: IEEE Access, Vol 12, Pp 14014-14026 (2024)

مصطلحات موضوعية: Autocorrelation, cosine cyclic learning rate, deep transformer, optimizer, time series, variance, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10410839/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/7246ce2472de420082571fa741168415

المؤلفون: Mariem Bounabi, Rida Azmi, Jérôme Chenal, El Bachir Diop, Seyid Abdellahi Ebnou Abdem, Meriem Adraoui, Mohammed Hlal, Imane Serbouti

المصدر: Technologies, Vol 12, Iss 10, p 192 (2024)

مصطلحات موضوعية: Urban 4.0, predictive maintenance, deep transformer, computer vision, anomalies detection, software application, Technology

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2227-7080/12/10/192; https://doaj.org/toc/2227-7080

URL الوصول: https://doaj.org/article/d6cd8f12ccea44b39e47698fa4576b00

المؤلفون: Türk, Ömer, Şimşek Bağcı, Reyhan, Acar, Emrullah

مصطلحات موضوعية: Crop identification, Deep transformer encoder, Landsat-8, Sentinel-1

وصف الملف: application/pdf

Relation: Computers and Electronics in Agriculture; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; Bağcı, R. Ş., Acar, E., & Türk, Ö. (2023). Identification of cotton and corn plant areas by employing deep transformer encoder approach and different time series satellite images: A case study in Diyarbakir, Turkey. Computers and Electronics in Agriculture, 209, 107838.; https://doi.org/10.1016/j.compag.2023.107838; https://www.scopus.com/record/display.uri?eid=2-s2.0-85152632329&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=2823a35be21843e193e9f80528d81b55; https://hdl.handle.net/20.500.12514/3554

الاتاحة: https://hdl.handle.net/20.500.12514/3554
https://doi.org/10.1016/j.compag.2023.107838
https://www.scopus.com/record/display.uri?eid=2-s2.0-85152632329&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=2823a35be21843e193e9f80528d81b55

المؤلفون: Ahmed, Muhammed

المساهمون: Georgia Institute of Technology. Machine Learning, Mailchimp

مصطلحات موضوعية: Deep transformer models, Natural language processing (NLP)

وصف الملف: 42:39 minutes; video/mp4; text/html; text/plain; image/jpeg

Relation: Machine Learning @ Georgia Tech (ML@GT) Seminar Series; http://hdl.handle.net/1853/62006

الاتاحة: http://hdl.handle.net/1853/62006