Tecim, Vahap

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https://gcris.yasar.edu.tr/handle/123456789/13097
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01.01.12.03. Yönetim Bilişim Sistemleri Bölümü
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0000-0001-5319-5241
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6th International Symposium on Multidisciplinary Studies and Innovative Technologies ISMSIT 20221
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  • Thumbnail Image
    Conference Object
    Use of Artificial Intelligence Techniques in Project Production Systems, Proje Üretim Sistemlerinde Yapay Zeka Tekniklerinin Kullanymy
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmet Selcuk Ozgur; Cigdem Tarhan; Murat Komesli; Vahap Tecim; Tarhan, Cigdem; Ozgur, Ahmet Selcuk; Komesli, Murat; Tecim, Vahap
    Among the reasons for the failure in the project management process the lack of realistic and clear objectives the failure to assign competent people to the steps expected to be realized in the project the inability to determine the time required for the fulfillment of the defined tasks the selection of people who cannot meet the expectations for the project team and the deficiencies in the project planning are important. In projects that are expected to be realized in institutions and organizations effective methods should be used to achieve success in the foresight stages necessary for the formation of the project team and the success of the planned project. The system model proposed within the scope of the study supports the decision maker by using artificial intelligence techniques in the effective evaluation of the cognitive competencies of human resources in the team building process in the projects to be developed the creation of the project team in line with the determined purpose and the determination of possible alternatives the prediction of the success of the project and its sub-components. As an example of the project sub-components of the proposed system model with the study carried out the machine learning model was established by evaluating the success of the graduate students in the doctorate program in the same field in line with the defined criteria in the projects they took in the graduate courses. High classification success has been achieved with Decision Trees with 0.97 accuracy from Support Vector Machines Decision Trees and K-Nearest Neighbors models. With the operation and joint use of the realized machine learning models the decision maker was supported in the evaluation of general and sub-objectives. © 2022 Elsevier B.V. All rights reserved.