TR-Dizin İndeksli Yayınlar Koleksiyonu

Bu koleksiyon için kalıcı URIhttps://hdl.handle.net/20.500.12573/396

Gözat

Filtreler

2012 - 20191192020 - 2026341

Ayarlar

Search Results

Şimdi gösteriliyor 1 - 10 / 460
  • Makale
    +CIl / +CUl Derivational Affix and the Word undefinedArchaicundefined in Turkey Turkish
    (2020) Kayasandık, Ahmet
    Türkçede yeni kelimeler yapmanın yollarından biri yapımekleriyle kelimeler türetmektir. Ancak bunun dilin kurallarınauygun olarak yapılması gerekir. Ek canlı olmalı, benzer örnekleri bulunmalı, kuruluşu tutarlı, yapısı doğru, anlamı açık olmalıdır. Eski kaynaklarda sınırlı örnekleri bulunan +CIl / +CUlekiyle düşkünlük, alışkınlık, benzerlik kavramı taşıyan kelimeler türetilmiştir. Son zamanlarda ekin asıl işlevine getirildiğiisme bağımlılık, yakınlık, isteklilik, sıralama gibi kavramlaryüklenmiş; sayıca az olsa da yine bu ekle bazı araç, gereç adları,bitki adları, oyun adları, terimler ve somut kelimeler yapılmıştır. Eskicil gibi birkaç örnekte ise bu eke, terim türetme ilkelerine pek de uygun olmadığı hâlde nispet eki işlevi yüklenmiştir.Bu çalışmada ekin kelimeye kattığı kavramlar göz önünde bulundurularak bu ekle türetilen bütün kelimeler gruplandırılmışve sözlük anlamları verilmiştir. Ayrıca arkaik yerine kullanılanterimlerden biri olan eskicil sözünün terim yapma yöntemlerineuygun olmadığı belirtilerek bu terim için arkaik veya eski dilögesi ya da eski unsur ifadeleri önerilmiştir.
  • Araştırma Projesi
    3 Hodgkin-Huxley Nöronlarında Ani Yükseliş ve Fırlama Dinamiklerinin Kontrolü
    ID: 116F049
    Borısenok, Sergey (Koordinatör) - Borısenok, Sergey (Yürütücü) - Borisenok, Sergey (Yürütücü)
    TÜBİTAK
    07.01.2016 - 01.01.2018
  • Makale
    3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging
    (Gazi Univ, Fac Engineering Architecture, 2025) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, Bulent
    This study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.
  • Makale
    A Case Study: Do Misconceptions Lead to Intergroup Conflicts at Workplaces?
    (2019) Süklün, Harika
    Bu çalışma, yanlış algıların çatışmalara yol açıp açmadığını incelemek ve daha fazla araştırma yapmak için bilim adamları arasında dikkati arttırmak amacı ile yapılmıştır. Çatışmaların köklerinden biri olarak yanlış algı, özellikle de uygulayıcılar tarafından kabul görse de, yanlış anlama ve çatışmalar arasında bir ilişki olup olmadığını araştırmak için pek çok deneye dayalı çalışma yoktur. Bu çalışma başlangıç noktası olarak küçük ölçekli olarak hazırlanmıştır. Konuyu daha iyi anlamaya çalışmak için olay inceleme metodu kullanılmıştır. Gruplar arasındaki durumu karşılaştırabilmek ve analiz edebilmek için iki farklı çalışma yapılmıştır. Biri grup içi, diğeri ise grup dışı olarak alınmıştır. Bu çalışmanın bulguları, yanlış algılamaların işyerlerinde çalışma ortamında çatışmaya yol açtığını ortaya koymuştur. Gruplar arasında \"Biz ve Onlar\" olgusunun yanı sıra gruplar arası çatışmalar gözlemlenmiştir. Çalışmanın sonuçları incelenmiştir.
  • Makale
    A Comparative Analysis of Convolutional Neural Network Architectures for Binary Image Classification: A Case Study in Skin Cancer Detection
    (2024) Kurban, Rifat; Korkut, Şerife Gül; Kocabaş, Hatice
    Bu çalışmada, ikili görüntü sınıflandırması için Evrişimsel Sinir Ağı (CNN) mimarilerinin kapsamlı bir karşılaştırmalı analizi sunulmuş ve transfer öğreniminin faydalarına vurgu yapılmıştır. MobileNetV3, VGG19, ResNet50 ve EfficientNetB0 gibi önde gelen CNN modellerinin ikili görüntülerden cilt kanseri sınıflandırmadaki performans ve doğruluğu değerlendirilmiştir. Önceden eğitilmiş bir yaklaşım kullanılarak, transfer öğreniminin bu mimarilerin etkinliği üzerindeki etkisi araştırılmış ve ikili görüntü sınıflandırması bağlamında güçlü ve zayıf yönleri belirlenmiştir. Bu makale, optimal CNN mimarisinin seçimi ve transfer öğreniminden yararlanarak ikili görüntü sınıflandırma uygulamalarında, özellikle tıbbi görüntü analiziyle ilgili olanlarda, üstün performans elde etme konusunda değerli içgörüler sağlamayı amaçlamaktadır.
  • Makale
    A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms
    (2024) Akbaş, Ayhan; Dedeturk, Beyhan Adanur; Dedeturk, Bilge Kagan
    Forecasting tram passenger flow is an important part of the intelligent transportation system since it helps with resource allocation, network design, and frequency setting. Due to varying destinations and departure times, it is difficult to notice large fluctuations, non-linearity, and periodicity of tram passenger flows. In this paper, the first-order difference technique is used to eliminate seasonal structure from the time series data and the performance of different machine learning algorithms on passenger flow forecasting in trams is evaluated. Furthermore, the impact of the Covid-19 pandemic on forecasting success is examined. For this purpose, the tram data of Kayseri Transportation Inc. for the years 2018-2021 are used. Different estimation models including Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Network, Convolutional Neural Network, and LongTerm Short Memory are applied and the time series forecasting performances of the models are evaluated with MAPE and R2 metrics.
  • Makale
    Atıf - Scopus: 1
    A Comparative Study of Unet Variants for Low-Grade Glioma Segmentation in Magnetic Resonance Imaging
    (Inonu University, 2025) Guzel, Yasin; Aydin, Zafer
    Brain tumors originating from glial cells are pathological entities that significantly impact quality of life and are classified based on their malignancy into low-grade gliomas (LGGs) and high-grade gliomas (HGGs). While the more aggressive HGGs have been extensively studied, LGGs are of critical importance for early diagnosis due to their potential progression to HGGs if left untreated. This has driven researchers to develop methods for the rapid and consistent diagnosis of LGGs. In this study, three models—UNet, Transformer UNet, and Super Vision UNet—were comparatively evaluated for the automatic segmentation of LGGs using magnetic resonance imaging (MRI) data. Multimodal MRI scans from 110 patients, retrieved from The Cancer Imaging Archive (TCIA), were used to train the models. Performance was evaluated using Dice Coefficient, Tversky Index, and Intersection over Union (IoU) metrics. The Super Vision UNet achieves the highest Dice (0.9115) and Tversky (0.9154) scores, while the Transformer UNet attains the highest IoU (0.8789). Both advanced models demonstrate superior segmentation performance with lower loss values compared to the conventional UNet. Visual outputs indicate that the modern architectures delineate tumor contours with greater precision. These results highlight the effectiveness and reliability of contemporary UNet-based and Transformer-based architectures in segmenting complex tumor structures such as LGGs. Integrating these models into clinical decision support systems holds promise for enhancing the speed and accuracy of the diagnostic process. © 2025 Elsevier B.V., All rights reserved.
  • Makale
    Atıf - Scopus: 7
    A Comparative Study to Estimate the Mode I Fracture Toughness of Rocks Using Several Soft Computing Techniques
    (Murat Yakar, 2023) Köken, E.; Kadakci Koca, Tümay; Koca, Tümay Kadakci
    Fracture toughness is an important phenomenon to reveal the actual strength of fractured rock materials. It is, therefore, crucial to use the fracture toughness models principally for simulating the performance of fractured rock medium. In this study, the mode-I fracture toughness (KIC) was investigated using several soft computing techniques. For this purpose, an extensive literature survey was carried out to obtain a comprehensive database that includes simple and widely used mechanical rock parameters such as uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS). Several soft computing techniques such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), gene expression programming (GEP), and multivariate adaptive regression spline (MARS) were attempted to reveal the availability of these methods to estimate the KIC. Among these techniques, it was determined that ANN presents the best prediction capability. The correlation of determination value (R2) for the proposed ANN model is 0.90, showing its relative success. In this manner, the present study can be declared a case study, indicating the applicability of several soft computing techniques for the evaluation of KIC. However, the number of samples for different rock types should be increased to improve the established predictive models in future studies. © 2023 Elsevier B.V., All rights reserved.
  • Makale
    A Comparison of Ensemble and Base Learner Algorithms for the Prediction of Machining Induced Residual Stresses in the Turning of Aerospace Materials
    (2022) Buyrukoglu, Selim; Kesriklioglu, Sinan
    The estimation of residual stresses is essential to prevent the catastrophic failures of the components used in the aerospace industry. The objective of this work is to predict the machining induced residual stresses with bagging, boosting, and single-based machine learning models based on the design and cutting parameters used in the turning of Inconel 718 and Ti6Al4V alloys. Experimentally measured residual stress data of these two materials was compiled from the literature, including the surface material of the cutting tools, cooling conditions, rake angles, as well as the cutting speed, feed, and width of cut to show the robustness of the models. These variables were also grouped into different combinations to clearly show the contribution and necessity of each element. Various predictive models in machine learning (AdaBoost, Random Forest, Artificial Neural Network, K-Neighbors Regressor, Linear Regressor) were then applied to estimate the residual stresses on the machined surfaces for the classified groups using the generated data. It was found that the AdaBoost algorithm was able to predict the machining induced residual stresses with a mean absolute error of 18.1 MPa for the IN718 alloy and 31.3 MPa for Ti6Al4V by taking into account all the variables, while the artificial neural network provides the lowest mean absolute errors for the Ti6Al4V alloy. On the other hand, the linear regression model gives poor agreement with the experimental data. All the analyses showed that AdaBoost (boosting) ensemble learning and artificial neural network models can be used for the prediction of the machining induced residual stresses with the small datasets of the IN718 and Ti6Al4V materials.
  • Makale
    Atıf - Scopus: 1
    A Conceptual Process Model for the Sustainability of a Healthy Building
    (Istanbul Teknik Universitesi, Faculty of Architecture itudergisi@itu.edu.tr, 2016) Sarp Yalçin, Asutan; Balanlı, Ayşe; Yalçın, Asutan Sarp
    Healthy buildings may lose their healthy characteristics over time and due to changing circumstances during their usage phase, which may lead to biological and psychological health problems for their users. Thus a process model is required for buildings which ensures that the initial healthy environment is retained. This study therefore introduces a conceptual Process Model for the Sustainability of a Healthy Building. In the study, the sustainability of the healthy building and its criteria were determined by the help of the definition of sustainability and associated criteria. Then, the changes which prevent the building from sustaining its healthy status were introduced. Based on the events that lead to the loss of healthy building performance, the actions and functioning of the process model were created. Action steps were then composed and the relationship between these steps were established. As a result, the principal components of this model are determined as Achievement of a Healthy Building, Sustainability Assessment of a Healthy Building, and Sustainability Management of a Healthy Building. Application of the procedures in the proposed model can help maintain the healthy characteristics of buildings throughout their operational lifespan, thereby preserving the basic requirement for a healthy environment. © 2020 Elsevier B.V., All rights reserved.