Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Mucahit Cevik"'
Publikováno v:
Applied Intelligence.
Generative Adversarial Networks (GANs) have gained significant attention in several computer vision tasks for generating high-quality synthetic data. Various medical applications including diagnostic imaging and radiation therapy can benefit greatly
Publikováno v:
Applied Intelligence. 53:69-95
Generative adversarial networks (GANs) are one of the most widely used generative models. GANs can learn complex multi-modal distributions, and generate real-like samples. Despite the major success of GANs in generating synthetic data, they might suf
Autor:
Ozan Ozyegen, Hadi Jahanshahi, Mucahit Cevik, Beste Bulut, Deniz Yigit, Fahrettin F. Gonen, Ayşe Başar
Publikováno v:
Journal of Data, Information and Management. 4:71-85
Publikováno v:
INFOR: Information Systems and Operational Research. 60:133-164
Multistage stochastic programming provides a modeling framework for sequential decision-making problems that involve uncertainty. One typically overlooked aspect of this methodology is how uncertainty is incorporated into modeling. Traditionally, sta
Publikováno v:
Applied Intelligence
Being able to interpret a model’s predictions is a crucial task in many machine learning applications. Specifically, local interpretability is important in determining why a model makes particular predictions. Despite the recent focus on interpreta
Autor:
Mehmetcan Tutuncu, Aysun Bozanta, Eray Mert Kavuk, Bilgin Kosucu, Mucahit Cevik, Ayse Basar, Ayse Tosun, Sibel B. Sonuc
Publikováno v:
Applied Intelligence. 52:4274-4299
This paper proposes a real-life application of deep reinforcement learning to address the order dispatching problem of a Turkish ultra-fast delivery company, Getir. Before applying off-the-shelf reinforcement learning methods, we define the specific
Publikováno v:
Proceedings of the Canadian Conference on Artificial Intelligence.
Publikováno v:
Proceedings of the Canadian Conference on Artificial Intelligence.
Autor:
Hadi Jahanshahi, Mucahit Cevik
Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd66db6fec92968b413a29215ee9204e
http://arxiv.org/abs/2204.05972
http://arxiv.org/abs/2204.05972
Autor:
Emir Kavurmacioglu, Sanaz Mohammadjafari, Mucahit Cevik, Ozan Ozyegen, Jonathan Ethier, Ayse Basar
Publikováno v:
Neural Computing and Applications. 33:11309-11323
In this paper, we investigate the capability of generative adversarial networks, including conditional and conditional convolutional generative adversarial networks, in generating electromagnetic engineered surfaces (EES). Generative models such as g