Zobrazeno 1 - 10
of 7 857
pro vyhledávání: '"Gencer A"'
Autor:
Dumanlı Ahmet, Günay Ersin, Aydın Suphi, Çilekar Şule, Gencer Adem, Kurbaseviç Emira, Öz Gürhan, Çelik Sefa, Balcı Aydın, Özcan Mehmet, Karadağ Müjgan Ercan
Publikováno v:
Türk Biyokimya Dergisi, Vol 48, Iss 4, Pp 397-402 (2023)
We aimed to investigate the usability of pleural pyruvate kinase (PK), total antioxidant status (TAS), and total oxidant status (TOS) as an alternative to Light’s criteria in exudate-transudate differentiation.
Externí odkaz:
https://doaj.org/article/e67697f050754e61920d70587b5812ff
Autor:
Clasen, Kai Norman, Hackel, Leonard, Burgert, Tom, Sumbul, Gencer, Demir, Begüm, Markl, Volker
This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image analysis. The reBEN dataset consists of 549,488 pairs of Sentinel-1 a
Externí odkaz:
http://arxiv.org/abs/2407.03653
Deep metric learning (DML) has shown to be effective for content-based image retrieval (CBIR) in remote sensing (RS). Most of DML methods for CBIR rely on a high number of annotated images to accurately learn model parameters of deep neural networks
Externí odkaz:
http://arxiv.org/abs/2406.10107
We investigate intuitionistic modal logics with locally interpreted $\square$ and $\lozenge$. The basic logic LIK is stronger than constructive modal logic WK and incomparable with intuitionistic modal logic IK. We propose an axiomatization of LIK an
Externí odkaz:
http://arxiv.org/abs/2403.06772
Self-supervised learning through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) image representation learning, and thus embodies a significant potential for content-based image retrieval (CBIR) from ever-gro
Externí odkaz:
http://arxiv.org/abs/2401.07782
Federated learning (FL) enables the collaboration of multiple deep learning models to learn from decentralized data archives (i.e., clients) without accessing data on clients. Although FL offers ample opportunities in knowledge discovery from distrib
Externí odkaz:
http://arxiv.org/abs/2311.06141
Reading right books contributes to children's imagination and brain development, enhances their language and emotional comprehension abilities, and strengthens their relationships with others. Building upon the critical role of reading books in indiv
Externí odkaz:
http://arxiv.org/abs/2311.07591
We introduce FIK, a natural intuitionistic modal logic specified by Kripke models satisfying the condition of forward confluence. We give a complete Hilbert-style axiomatization of this logic and propose a bi-nested calculus for it. The calculus prov
Externí odkaz:
http://arxiv.org/abs/2309.06309
Deep metric learning (DML) based methods have been found very effective for content-based image retrieval (CBIR) in remote sensing (RS). For accurately learning the model parameters of deep neural networks, most of the DML methods require a high numb
Externí odkaz:
http://arxiv.org/abs/2306.11605
Autor:
Sumbul, Gencer, Demir, Begüm
Due to the publicly available thematic maps and crowd-sourced data, remote sensing (RS) image annotations can be gathered at zero cost for training deep neural networks (DNNs). However, such annotation sources may increase the risk of including noisy
Externí odkaz:
http://arxiv.org/abs/2306.08575