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Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/1338

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Examining Tongue Movement Intentions in EEG-Based BCI With Machine and Deep Learning: An Approach for Dysphagia Rehabilitation
(Sciendo, 2024) Aslan, Sevgi Gokce; Yilmaz, Bulent; 0000-0001-9425-1916; AGÜ; Aslan, Sevgi Gökçe; 01. Abdullah Gül University
Dysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, and Kernel were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN (79.4%) and SVM (63.4%) exhibited lower accuracy rates compared to ensemble methods like AdaBoost, Bagging, and Random Forest, all achieving high accuracy rates of 99.8%. These ensemble techniques proved to be highly effective in handling complex EEG datasets, particularly in distinguishing between rest and imagination phases. Furthermore, the deep learning approach, utilizing CNN and Continuous Wavelet Transform (CWT), achieved an accuracy of 83%, highlighting its potential in analyzing motor imagery data. Overall, this study demonstrates the promising role of BCI technologies and advanced machine learning techniques, especially ensemble and deep learning methods, in improving outcomes for dysphagia rehabilitation.
Citation - WoS: 3
Citation - Scopus: 10
Raising Awareness of Sustainable Development Goals in Higher Education Institutions
(Turkish Educational Admin Research & Development Assoc, 2024) Suklun, Harika; Bengu, Elif; 0000-0003-1016-268X; 0000-0001-9817-7207; AGÜ; Bengu, Elif; 01. Abdullah Gül University; 10. Rektörlük
Higher education institutions play a crucial role in advancing sustainable development goals. They bear the responsibility of informing and encouraging all stakeholders, including faculty members, students, and industry partners, to collaborate towards achieving these goals. While many universities are integrating Sustainable Development Goals into their operations and educational programs, there is an increasing need to establish collaborative platforms with private sectors and nongovernmental organizations to further champion this agenda. Educating the future workforce is a key responsibility of these institutions, and they should actively raise students' awareness of these goals, enabling them to develop competencies related to sustainability. This study aims to explore how higher education institutions can effectively raise awareness of sustainable development goals. In addition, the research contributes to the literature by presenting a curriculum designed in a Turkish higher education institution to foster awareness of sustainable development goals. The findings hold the potential to significantly enrich existing literature on awarenessraising practices and the promotion of sustainability strategies, extending beyond higher education institutions to organizations at large.
Citation - Scopus: 13
Staging of the Liver Fibrosis From CT Images Using Texture Features
(2012) Kayaaltı, Ömer; Aksebzeci, Bekir Hakan; Karahan, Ökkeş Ibrahim; Deniz, Kemal; Öztürk, Menmet; Yilmaz, Bulent; Asyali, Musa Hakan; 0000-0003-2954-1217; 0000-0001-7476-8141; AGÜ; Aksebzeci, Bekir Hakan; Yilmaz, Bülent; Asyali, Musa Hakan; 01. Abdullah Gül University
Even though liver biopsy is critical for evaluating chronic hepatitis and fibrosis, it is an invasive, costly, and difficult to standardize approach. The developments in medical image processing and artificial intelligence methods have advanced the potential of using computer-aided diagnosis techniques in the classification of liver tissues. The aim of this study was to develop a non-invasive, cost-effective, and fast approach to specify fibrosis stage using the texture properties of computed tomography images of liver. Gray level co-occurrence matrix, discrete wavelet transform, and discrete Fourier transform were the image analysis tools in the feature extraction phase. Following dimension reduction of the texture features support vector machines and k-nearest neighbor methods were used in the classification phase of this study. Our results showed that our approach is feasible in fibrosis staging especially in pairwise stage comparisons with success rate of approximately 90%. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.
Citation - WoS: 1
Citation - Scopus: 2
Vision-Based Autonomous Aerial Refueling
(Amer Inst Aeronautics & Astronautics, 2022) Erkin, Tevfik; Abdo, Omer; Sanli, Yilmaz; Celik, Harun; Isci, Hasan; AGÜ; Sanli, Yilmaz; 01. Abdullah Gül University
Aerial refueling tasks are very challenging due to the high risk of aircraft close proximity. Currently, within the drogue-probe method, the receiver aircraft pilot manages the refueling task in accordance with the tanker aircraft pilot. Therefore, autonomous aerial refueling is still an unaccomplished task for aircrafts. In this paper, a fully automated aerial refueling procedure based on digital visual inspection is proposed. A nonlinear dynamic model of receiver aircraft is derived to track the motion of drogue. In order to control the receiver aircraft affected by tanker aircraft vortex during approach, and ensure the receiver aircraft to automatically track and dock the tanker aircraft, an autopilot system that considers visual sensing of drogue motion is designed. The receiver aircraft is controlled by the autopilot system via translational motion of tanker aircraft projected by a cameramounted on the receiver aircraft. Thanks to this vision-based controllers, the need of tanker aircraft positioning is denied since camera projection has the capability of perception of three-dimensional direction of tanker aircraft. In order to test the autopilots include vision-based controllers and algorithms, the vision-based autonomous aerial refueling is operated under presence of turbulence and vortex. Finally, the simulation results demonstrate that the proposed guidance-navigation-control system achieve aerial refueling autonomously, and make it feasible and realizable for aircrafts.

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