Výsledky vyhledávání - "Ahmet Üstündağ"

Akademický článek

First-grade students, their parents, and teachers met distance education (DE) and remote literacy instruction (RLI) as a result of the quarantine measures and rigorous regulations implemented to stop the spread of COVID-19. The aim of this study was to investigate first-grade teachers’ experiences and opinions on RLI in DE during the COVID-19 pandemic. Qualitative research paradigm and case study design were used to gather the data. Focus group interviews using a semi-structured interview form were carried out as a data collection method. The study group included 15 first-grade teachers working in public primary schools in Istanbul. Data were collected online, and content analysis was applied. The findings revealed that the teachers perceived DE as a crisis because they were more fatigued mentally. They could not communicate with their parents properly. They could not teach values. They could not manage the classroom. DE was also an opportunity because the teachers were less fatigued physically. They could develop professionally. While teaching online, teachers used various materials. They could apply different activities and instructional methods. They achieved student-student interaction as well as student-teacher interaction. Teachers could assess both reading comprehension and writing not only during the course but also after the course. They had some expectations and plans about school life after DE although they predicted to have some problems. They thought they could teach literacy online if they had to. The results highlighted the need for a sense of belonging, collaboration, and positive relationship among teachers, students and parents in RLI.

Autor: Bora Korkmazer, Ahmet Kürşat Karaman, Ahmet Üstündağ, Kanullah Süleyman, Serdar Arslan, Osman Kızılkılıç, Naci Koçer, Civan Islak

Publikováno v: Cerrahpaşa Medical Journal, Vol 48, Iss 1, Pp 1-7 (2024)

Purpose: The aim of this study was to investigate the clinical and imaging features of intracranial mycotic aneurysms (MAs) in addition to endovascular treatment (EVT) results with different techniques. Methods: Patients who underwent EVT for MAs

Externí odkaz: https://doaj.org/article/2ad7b8bc4055424895fabffc3d54ee2d

Zobrazit plný text záznamu

Akademický článek

Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study

Autor: Omer Bagcilar, Deniz Alis, Ceren Alis, Mustafa Ege Seker, Mert Yergin, Ahmet Ustundag, Emil Hikmet, Alperen Tezcan, Gokhan Polat, Ahmet Tugrul Akkus, Fatih Alper, Murat Velioglu, Omer Yildiz, Hakan Hatem Selcuk, Ilkay Oksuz, Osman Kizilkilic, Ercan Karaarslan

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)

Abstract The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object

Externí odkaz: https://doaj.org/article/1bde78bbbdf84660812bf903593a17c4

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT

Autor: Deniz Alis, Ceren Alis, Mert Yergin, Cagdas Topel, Ozan Asmakutlu, Omer Bagcilar, Yeseren Deniz Senli, Ahmet Ustundag, Vefa Salt, Sebahat Nacar Dogan, Murat Velioglu, Hakan Hatem Selcuk, Batuhan Kara, Caner Ozer, Ilkay Oksuz, Osman Kizilkilic, Ercan Karaarslan

Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)

Abstract To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test it

Externí odkaz: https://doaj.org/article/40d9024177f54153a115a4f0937e7097

Zobrazit plný text záznamu

Plný text ve formátu HTML

Akademický článek

Inter-vendor performance of deep learning in segmenting acute ischemic lesions on diffusion-weighted imaging: a multicenter study

Autor: Deniz Alis, Mert Yergin, Ceren Alis, Cagdas Topel, Ozan Asmakutlu, Omer Bagcilar, Yeseren Deniz Senli, Ahmet Ustundag, Vefa Salt, Sebahat Nacar Dogan, Murat Velioglu, Hakan Hatem Selcuk, Batuhan Kara, Ilkay Oksuz, Osman Kizilkilic, Ercan Karaarslan

Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)

Abstract There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospect

Externí odkaz: https://doaj.org/article/897683b2837b4094b80466289ed7a30d

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání