Yazar "Bulut, Faruk" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    Enhancement of eye socket recognition performance using inverse histogram fusion images and the Gabor transform
    (Wiley, 2025) Shehu, Harisu Abdullahi; Ince, Ibrahim Furkan; Bulut, Faruk
    The eye socket is a cavity in the skull that encloses the eyeball and its surrounding muscles. It has unique shapes in individuals. This study proposes a new recognition method that relies on the eye socket shape and region. This method involves the utilization of an inverse histogram fusion image to generate Gabor features from the identified eye socket regions. These Gabor features are subsequently transformed into Gabor images and employed for recognition by utilizing both traditional methods and deep-learning models. Four distinct benchmark datasets (Flickr30, BioID, Masked AT & T, and CK+) were used to evaluate the method's performance. These datasets encompass a range of perspectives, including variations in eye shape, covering, and angles. Experimental results and comparative studies indicate that the proposed method achieved a significantly (p<0.001) higher accuracy (average value greater than 92.18%) than that of the relevant identity recognition method and state-of-the-art deep networks (average value less than 78%). We conclude that this improved generalization has significant implications for advancing the methodologies employed for identity recognition.
  • [ X ]
    Öğe
    MACHINE LEARNING FOR ENHANCED CLASSROOM HOMOGENEITY IN PRIMARY EDUCATION
    (2024) Bulut, Faruk; Donmez, Ilknur; Ince, Ibrahim Furkan; Petrov, Pavel
    A homogeneous distribution of students in a class is accepted as a key factor for overall success in primary education. A class of students with similar attributes normally increases academic success. It is also a fact that general academic success might be lower in some classes where students have different intelligence and academic levels. In this study, a class distribution model is proposed by using some data science algorithms over a small number of students’ dataset. With unsupervised and semi supervised learning methods in machine learning and data mining, a group of students is equally distributed to classes, taking into account some criteria. This model divides a group of students into clusters by the considering students’ different qualitative and quantitative characteristics. A draft study is carried out by predicting the effectiveness and efficiency of the presented approaches. In addition, some process elements such as quantitative and qualitative characteristics of a student, data acquisition style, digitalization of attributes, and creating a future prediction are also included in this study. Satisfactory and promising experimental results are received using a set of algorithms over collected datasets for classroom scenarios. As expected, a clear and concrete evaluation between balanced and unbalanced class distributions cannot be performed since these two scenarios for the class distributions cannot be applicable at the same time.