Türkmen, Güzin

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Name Variants
Tirkes G.
T.,Guzin
G.,Türkmen
T., Güzin
Güzin, Türkmen
G., Turkmen
Türkmen,G.
Tirkeş G.
Turkmen, Guzin
G.,Turkmen
T.,Güzin
Turkmen G.
Turkmen,G.
Guzin, Turkmen
Türkmen, Güzin
Türkmen G.
T., Guzin
Tirkes, Guzin
G., Türkmen
Job Title
Doktor Öğretim Üyesi
Email Address
guzin.turkmen@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Website
ORCID ID
0000-0003-0884-4876
Scopus Author ID
36020622300
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output

14

Articles

6

Citation Count

14

Supervised Theses

1

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2010 - 201942020 - 20221
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Author: Tirkes, Guzin

Scholarly Output Search Results

Now showing 1 - 5 of 5
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    Conference Object
    Citation - WoS: 1
    An Undergraduate Curriculum for Deep Learning
    (Ieee, 2018) Tirkes, Guzin; Ekin, Cansu Cigdem; Sengul, Gokhan; Bostan, Atila; Karakaya, Murat; Computer Engineering
    Deep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields.
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    Article
    Citation - WoS: 10
    Citation - Scopus: 24
    DEMAND FORECASTING: A COMPARISON BETWEEN THE HOLT-WINTERS, TREND ANALYSIS AND DECOMPOSITION MODELS
    (Univ Osijek, Tech Fac, 2017) Tirkes, Guzin; Guray, Cenk; Celebi, Nes'e; Computer Engineering; Department of Metallurgical and Materials Engineering; Industrial Engineering
    In food production industry, forecasting the timing of demands is crucial in planning production scheduling to satisfy customer needs on time. In the literature, several statistical models have been used in demand forecasting in Food and Beverage (F&B) industry and the choice of the most suitable forecasting model remains a central concern. In this context, this article aims to compare the performances between Trend Analysis, Decomposition and Holt-Winters (HW) models for the prediction of a time series formed by a group of jam and sherbet product demands. Data comprised the series of monthly sales from January 2013 to December 2014 obtained from a private company. As performance measures, metric analysis of the Mean Absolute Percentage Error (MAPE) is used. In this study, the HW and Decomposition models obtained better results regarding the performance metrics.
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    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Online Learning Perceptions Amid Covid-19 Pandemic: the Engineering Undergraduates' Perspective
    (Tempus Publications, 2022) Eryilmaz, Meltem; Kalem, Guler; Kilic, Hurevren; Tirkes, Guzin; Topalli, Damla; Turhan, Cigdem; Yazici, Ali; Information Systems Engineering; Computer Engineering; Software Engineering; Information Systems Engineering; Computer Engineering; Software Engineering
    The COVID-19 pandemic caused face-to-face education in just about all universities worldwide to shift to online education. For most students, this educational model was a compulsory first experience. In this study, the survey results are analyzed and discussed related to a group of students in the Engineering Faculty of a university in Turkey regarding their online education perceptions. Briefly summarized, the findings of the study indicate that: (a) most of the students still prefer face-to-face learning, which is also favored if accompanied by distance learning; (b) the concentration level of the students has dropped due to the concerns about the COVID-19 pandemic which affects their learning negatively; and (c) around half of the students participating in the study feel that the online exams conducted without a secure exam software, is considered unsafe. Additionally, the study's results were further extended to evaluate the questionnaire results and reported along with the suggestions of necessary actions in emergency online learning (EOL).
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    Conference Object
    Citation - WoS: 0
    Deep Learning and Current Trends in Machine Learning
    (Ieee, 2018) Bostan, Atila; Sengul, Gokhan; Tirkes, Guzin; Ekin, Cansu; Karakaya, Murat; Computer Engineering
    Academic interest and commercial attention can be used to identify how much potential a novel technology may have. Since the prospective advantages in it may help solving some problems that are not solved yet or improving the performance of readily available ones. In this study, we have investigated the Web of Science (WOS) indexing service database for the publications on Deep Learning (DL), Machine Learning (ML), Convolutional Neural Networks (CNN), and Image Processing to reveal out the current trend. The figures indicate the strong potential in DL approach especially in image processing domain.
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    Article
    Citation - WoS: 27
    Citation - Scopus: 29
    Open Source Learning Management Systems in Distance Learning
    (Turkish online Journal Educational Tech-tojet, 2010) Aydin, Cansu Cigdem; Tirkes, Guzin; Computer Engineering; Computer Engineering
    This paper presents the major findings from evaluation the most widely used open source learning management systems and identify the most suitable open source e-learning platform. In this study, some analyses and comparisons were made about open source learning management systems the outcome of which was that Moodle was found to be outstanding with many features more than other LMS since it aims to improve the educational quality and include the tools that an e-learning system should have.