Yazar "Emiroglu, Bulent Gursel" seçeneğine göre listele

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    Integration search strategies in tree seed algorithm for high dimensional function optimization
    (SPRINGER HEIDELBERG, 2020) Gungor, Imral; Emiroglu, Bulent Gursel; Cinar, Ahmet Cevahir; Kiran, Mustafa Servet
    The tree-seed algorithm, TSA for short, is a new population-based intelligent optimization algorithm developed for solving continuous optimization problems by inspiring the relationship between trees and their seeds. The locations of trees and seeds correspond to the possible solutions of the optimization problem on the search space. By using this model, the continuous optimization problems with lower dimensions are solved effectively, but its performance dramatically decreases on solving higher dimensional optimization problems. In order to address this issue in the basic TSA, an integration of different solution update rules are proposed in this study for solving high dimensional continuous optimization problems. Based on the search tendency parameter, which is a peculiar control parameter of TSA, five update rules and a withering process are utilized for obtaining seeds for the trees. The performance of the proposed method is investigated on basic 30-dimensional twelve numerical benchmark functions and CEC (congress on evolutionary computation) 2015 test suite. The performance of the proposed approach is also compared with the artificial bee colony algorithm, particle swarm optimization algorithm, genetic algorithm, pure random search algorithm and differential evolution variants. Experimental comparisons show that the proposed method is better than the basic method in terms of solution quality, robustness and convergence characteristics.
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    Leveraging Deep Learning for Enhanced Detection of Alzheimer's Disease Through Morphometric Analysis of Brain Images
    (Int Information & Engineering Technology Assoc, 2023) Celebi, Selahattin Baris; Emiroglu, Bulent Gursel
    This study investigates the efficacy of tensor-based morphometry (TBM) in detecting Alzheimer's Disease (AD) using deep learning techniques. The primary focus is on discerning the volumetric variations in brain tissues characteristic of AD, Mild Cognitive Impairment (MCI), and cognitively normal (CN) conditions. TBM, as a measure of minute local volume differences, is employed as the distinguishing feature. The results are juxtaposed with those obtained from machine- learning-based methods, trained using a variety of medical images. Three unique models were developed for this purpose. The first model, trained using medial slices of the brain (train: 1622; test: 406), displayed an accuracy of less than 50%. The second model utilized axial brain slices procured at 5-pixel intervals, encompassing the hippocampus and the temporal lobe (train: 1632; test: 406), and demonstrated a significantly improved accuracy of 93%. The third model, fine-tuned with small kernel sizes to better extract localized changes from the image data used in the second model, achieved an accuracy of 92%. The findings suggest that the application of TBM and deep learning to medial slices alone is insufficient for an accurate diagnosis of AD. However, employing TBM with deep learning techniques to slices covering the hippocampus and temporal lobe can potentially offer a highly accurate approach for early AD detection. Notably, the use of small filters to extract detailed features from TBM did not enhance the model's performance. This research underscores the potential of deep learning in advancing the field of AD detection and diagnosis, providing crucial insights into the future development of diagnostic tools.
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    Use of Augmented Reality in Mobile Devices for Educational Purposes
    (Igi Global, 2017) Emiroglu, Bulent Gursel; Kurt, Adile Askim
    Use of technology in education has been widespread in the last decade, thanks to developments and improvements in information and communication technologies, especially in mobile devices. Among the fields in which mobile devices play important roles, education is one of the leading ones. Mobile devices help teachers and learners access educational resources when needed. To increase the reality of virtual learning environments on mobile devices, Augmented Reality (AR) technologies were introduced for mobile platforms, and the term Mobile Augmented Reality (MAR) arose. MAR opens a new door for educators and trainers to experience new methods of teaching for mobile learners. In this chapter, educational use of AR on mobile devices will be explained. Throughout the content of the chapter, readers will be informed about how AR applications changed people's teaching and learning styles.