Visual Task Classification using Classic Machine Learning and CNNs

Devangi Vilas Chinchankarame; Noha Elfiky; Nada Attar

doi:10.11159/mhci22.110

Visual Task Classification using Classic Machine Learning and CNNs

Devangi Vilas Chinchankarame
, Noha Elfiky
, Nada Attar

Saint Mary's College of California

San Jose State University

Producción científica

Idioma original	English
Título de la publicación alojada	Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022
Editores	Luigi Benedicenti, Zheng Liu
Editorial	Avestia Publishing
ISBN (versión impresa)	9781990800092
DOI	https://doi.org/10.11159/mhci22.110
Estado	Published - 2022
Evento	8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022 - Prague Duración: jul 28 2022 → jul 30 2022

Serie de la publicación

Nombre	Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science
ISSN (versión digital)	2369-811X

Conference

Conference	8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022
País/Territorio	Czech Republic
Ciudad	Prague
Período	7/28/22 → 7/30/22

ASJC Scopus Subject Areas

Computer Networks and Communications
Information Systems
Artificial Intelligence
Human-Computer Interaction
Electrical and Electronic Engineering
Biomedical Engineering

Acceder al documento

10.11159/mhci22.110

Otros archivos y enlaces

Citar esto

Chinchankarame, D. V., Elfiky, N., & Attar, N. (2022). Visual Task Classification using Classic Machine Learning and CNNs. En L. Benedicenti, & Z. Liu (Eds.), Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022 Artículo MHCI 110 (Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science). Avestia Publishing. https://doi.org/10.11159/mhci22.110

Chinchankarame, Devangi Vilas, Noha Elfiky y Nada Attar. "Visual Task Classification using Classic Machine Learning and CNNs." Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022, edited by Luigi Benedicenti y Zheng Liu, Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science., Avestia Publishing, 2022. https://doi.org/10.11159/mhci22.110

Visual Task Classification using Classic Machine Learning and CNNs. / Chinchankarame, Devangi Vilas; Elfiky, Noha; Attar, Nada.
Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022. ed. / Luigi Benedicenti; Zheng Liu. Avestia Publishing, 2022. MHCI 110 (Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science).

Producción científica

Chinchankarame, DV, Elfiky, N & Attar, N 2022, Visual Task Classification using Classic Machine Learning and CNNs. En L Benedicenti & Z Liu (eds.), Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022., MHCI 110, Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science, Avestia Publishing, 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022, Prague, Czech Republic, 7/28/22. https://doi.org/10.11159/mhci22.110

Chinchankarame DV, Elfiky N, Attar N. Visual Task Classification using Classic Machine Learning and CNNs. En Benedicenti L, Liu Z, editores, Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022. Avestia Publishing. 2022. MHCI 110. (Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science). doi: 10.11159/mhci22.110

Chinchankarame, Devangi Vilas ; Elfiky, Noha ; Attar, Nada. / Visual Task Classification using Classic Machine Learning and CNNs. Proceedings of the 8th World Congress on Electrical Engineering and Computer Systems and Science, EECSS 2022. editor / Luigi Benedicenti ; Zheng Liu. Avestia Publishing, 2022. (Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science).

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