Contributors

Autor: Antonin Abherve, Benny Akesson, Mehmet Akşit, Wilbert Alberts, Önder Babur, Alessandra Bagnato, Konstantinos Barmpis, Cagatay Catal, Michel R.V. Chaudron, Loek Cleophas, Antonio García-Domínguez, Regina Hebig, Jozef Hooman, Truong Ho-Quang, Aydin Kaya, Ali Seydi Keceli, Timo Kehrer, Michael Nieke, Güner Orhan, Christopher Pietsch, Eric J. Rapos, Gregorio Robles, Ramon Schiffelers, Christoph Seidl, Jack Sleuters, Matthew Stephan, Harald Störrle, Aishwarya Suresh, Bedir Tekinerdogan, Burak Uzun, Mark van den Brand, Adrian Yankov

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::692e4be2a46e1969cba6b72e924facc4
https://doi.org/10.1016/b978-0-12-816649-9.00005-3

Sensor failure tolerable machine learning-based food quality prediction model

Autor: Ali Seydi Keceli, Bedir Tekinerdogan, Aydin Kaya, Cagatay Catal

Publikováno v: Sensors (Switzerland), 20(11)
Sensors
Volume 20
Issue 11
Sensors (Switzerland) 20 (2020) 11
Sensors (Basel, Switzerland)
Sensors, Vol 20, Iss 3173, p 3173 (2020)

For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the qual

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf422935ddc24ede320bee29572fdffd
https://research.wur.nl/en/publications/sensor-failure-tolerable-machine-learning-based-food-quality-pred

Viewpoint projection based deep feature learning for single and dyadic action recognition

Autor: Ali Seydi Keceli

Publikováno v: Expert Systems with Applications. 104:235-243

Usage of depth data in various computer vision applications gains popularity with cheap depth sensors available on the market. In this study, a method that utilizes Deep Learning is proposed for single human actions and dyadic actions. Depth data of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::afe67df119c7909d4a8f8b5e89eecbb3
https://doi.org/10.1016/j.eswa.2018.03.047

Depth features to recognise dyadic interactions

Autor: Ali Seydi Keceli, Ahmet Burak Can, Aydin Kaya

Publikováno v: IET Computer Vision. 12:331-339

Usage of depth sensors in activity recognition is an emerging technology in human-computer interaction. This study presents an approach to recognise human-to-human interactions by using depth information. Both hand-crafted features and deep features

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b93ad8993ab3acccf964acd708240927
https://doi.org/10.1049/iet-cvi.2017.0204

Analysis of transfer learning for deep neural network based plant classification models

Autor: Huseyin Temucin, Ali Seydi Keceli, Aydin Kaya, Cagatay Catal, Hamdi Yalin Yalic, Bedir Tekinerdogan

Publikováno v: Computers and Electronics in Agriculture, 158, 20-29
Computers and Electronics in Agriculture 158 (2019)

Plant species classification is crucial for biodiversity protection and conservation. Manual classification is time-consuming, expensive, and requires experienced experts who are often limited available. To cope with these issues, various machine lea

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c37783039927171e5c773c93466c3c5d
https://research.wur.nl/en/publications/analysis-of-transfer-learning-for-deep-neural-network-based-plant