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    Applying the Fuzzy PERT Method in Project Management: A Real-Life Case Study
    (Prof.Dr. İskender AKKURT, 2023) Kenar, E.; Ipek, M.; Dügenci, M.; Korkmaz, Ö.A.
    Time is the most precious resource for any business simply because no one can bring time back. Therefore, there is a huge need to be able to control time accurately. Also, the competitiveness between companies increased the value of time. Many investments were allocated to build methodologies that help to use time effectively. Because it became a fact that the speed of work accomplishment is the main factor for being a considerable competitor. Planning for future projects usually relays on non-fixed but mostly expected data. It is more reliable to use fuzzy methods for this situation. In this study, Fuzzy PERT (FPERT) was used as a project management technique to plan the construction of a marble factory from the establishment phase to the plan of the machines that will be used later on. FPERT showed more realistic results than any classical method would. Out of 16 routes, the final result of the completion time was (90.9,122.1,151.9) days which was determined after triangular fuzzy numbers for the activity times in the entire project. This article aims to prove the efficiency of fuzzy methodologies especially FPERT to be conducted in future planned projects through this real-life case study. © IJCESEN.
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    Face recognition by distance and slope between facial landmarks
    (Institute of Electrical and Electronics Engineers Inc., 2017) Özseven, T.; Dügenci, M.
    Facial landmark is to be determined point by point of areas such as eyes, nose, mouth, eyebrows on the face. Facial recognition studies can be generally categorized into two categories, local and global. All of the faces are used in the global face recognition while the face domain is divided into subspaces in the local face recognition. In face recognition studies facial landmarks are used for facial recognition with detection of regions located at the face and image processing methods. In this study, face recognition has been successfully analyzed using distance and slope between facial landmarks. Analyzes were performed with both statistical and classifiers. According to the results obtained, the distance and slope between the 14 landmarks used in the study were found to be statistically significant in the facial recognition. In addition, the classification was performed with the help of these features and the highest success was found with 94.60% with MLP classifier. Obtained findings show the usability of the distance and slope between the landmarks in facial recognition. © 2017 IEEE.