Yazar "Ukelge, Mulayim Ongun" seçeneğine göre listele

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    A novel approach for predicting global innovation index scores
    (Inderscience Enterprises Ltd, 2024) Yildirim, Rabia Sultan; Ukelge, Mulayim Ongun; Essiz, Esra Sarac; Oturakci, Murat
    Innovation has great importance in growth models in today's economy. In the globalising world, countries that renew their product and service range are at the forefront. The way to manage innovation is to measure it. Therefore, to have measurable information, the Global Innovation Index (GII) identifies inputs and outputs that are indicators of innovation. The GII provides a global ranking for countries according to their innovation capacity. In this study, GII scores of 125 countries between the years 2013 and 2020 were estimated using the artificial neural network (ANN). Before the estimation, feature selection was performed from 61 common indicator parameters. 27 parameters that best explain the GII score were selected and used in the ANN. According to the estimated GII scores, the selected 27 parameters are sufficient to calculate the GII score and has been observed that the ANN model is sufficient to determine the approximate GII score of the countries.
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    Öğe
    Finite Mixture Model-Based Analysis of Yarn Quality Parameters
    (MDPI, 2025) Karakas, Esra; Koyuncu, Melik; Ukelge, Mulayim Ongun
    This study investigates the applicability of finite mixture models (FMMs) for accurately modeling yarn quality parameters in 28/1 Ne ring-spun polyester/viscose yarns, focusing on both yarn imperfections and mechanical properties. The research addresses the need for advanced statistical modeling techniques to better capture the inherent heterogeneity in textile production data. To this end, the Poisson mixture model is employed to represent count-based defects, such as thin places, thick places, and neps, while the gamma mixture model is used to model continuous variables, such as tenacity and elongation. Model parameters are estimated using the expectation-maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). The results reveal that thin places are optimally modeled using a two-component Poisson mixture distribution, whereas thick places and neps require three components to reflect their variability. Similarly, a two-component gamma mixture distribution best describes the distributions of tenacity and elongation. These findings highlight the robustness of FMMs in capturing complex distributional patterns in yarn data, demonstrating their potential in enhancing quality assessment and control processes in the textile industry.