Yılmaz Çakan, Gülce Coşku
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Yilmaz, G.
Yilmaz, Gülce Cosku
Yilmaz, Gulce Cosku
Yilmaz, Gulce C.
Yilmaz, G. Cosku
Yılmaz, Gülce Coşku
Yilmaz, Gülce Cosku
Yilmaz, Gulce Cosku
Yilmaz, Gulce C.
Yilmaz, G. Cosku
Yılmaz, Gülce Coşku
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gulce.cakan@ieu.edu.tr
Main Affiliation
09.02. Internal Sciences
Status
Current Staff
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Sustainable Development Goals
Documents
8
Citations
45
h-index
4
Documents
10
Citations
27
Scholarly Output
8
Articles
3
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0/0
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
21
Scopus Citation Count
44
WoS h-index
2
Scopus h-index
4
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0
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0
WoS Citations per Publication
2.63
Scopus Citations per Publication
5.50
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1
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0
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8 results
Scholarly Output Search Results
Now showing 1 - 8 of 8
Conference Object Citation - WoS: 2Citation - Scopus: 6Deep Time-Frequency Feature Extraction for Alzheimer's Dementia Eeg Classification(IEEE, 2022) Cura, Ozlem Karabiber; Yilmaz, Gulce C.; Ture, H. Sabiha; Akan, AydinAlzheimer's Dementia (AD), one of the age-related neurological disorders, causes loss of cognitive functions and seriously affects the daily life of patients. Electroencephalogram (EEG) is one of the most frequently used clinical tools to investigate the effects of AD on the brain. In the proposed study, a time-frequency representation and deep feature extraction based model is introduced to distinguish EEG segments of control subjects and AD patients. TF representations of EEG segments are obtained using high-resolution SynchroSqueezing Transform (SST), and conventional short-time Fourier transform (STFT) methods. The magnitudes of SST and STFT are used for deep feature extraction. Various classifiers are used to classify the extracted features to distinguish the EEG segments of control subjects and AD patients. STFT based deep feature extraction approach yielded better classification results than that of the SST method.Conference Object Citation - Scopus: 5Classification of Alzheimers' Dementia by Using Various Signal Decomposition Methods(IEEE, 2021) Cura, Ozlem Karabiber; Yilmaz, Gulce Cosku; Ture, Hatice Sabiha; Akan, AydinNeurological disorders may spring from any disorder in the brain or the central and autonomic nervous systems. Among the neurological disorders, while Alzheimer's disease and other dementias are the fourth-largest contributors of disabilityadjusted life years, they are the second largest contributor of deaths. In the proposed study, various signal decomposition methods such as EMD, EEMD, and DWT are presented to classify EEG segments of control subjects and Alzheimer' dementia patients. Time-domain features are calculated using selected 7 IMFs and 5 detail and approximation coefficients of DWT. Various classification techniques namely Decision Tree (DT), Support Vector Machine (SVM), k- Nearest Neighbor (kNN), and Random Forest (RF) are utilized to distinguish two groups. Simulation results demonstrate that the proposed approaches achieve outstanding validation accuracy rates.Conference Object Citation - Scopus: 1Implementation and Performance Evaluation of the Rsep Protocol on Arm and Intel Platforms(2010) Kondakçı, Süleyman; Yilmaz G.We present performance evaluation of two low level implementations of the RSEP protocol. The implementations are realized both in Java and C++ languages, while the test and evaluations are performed on two different CPU architectures, ARM and Intel ®. RSEP [21] is a security evaluation protocol used to assess security of remote systems over open and insecure networks such as the Internet. RSEP protocol provides an alternative approach to security test and evaluation, which mainly consists of a secure communication protocol, back-end services, and a variety of remote evaluation agents. Secure evaluation of remote entities/assets is a challenging issue with several important requirements such as interoperability, security, robustness, time-efficiency, and ease of applicability. Mobile agents running on hand-held ARM devices and Intel platforms perform the remote evaluation independent of time and location. Copyright 2010 ACM.Article Citation - Scopus: 2Evaluation of the Relationships Between Psychiatric Comorbidity and Seizure Semiology in Psychogenic Non-Epileptic Seizure Patients(Elsevier B.V., 2025) Yılmaz, G.C.; Türe, H.S.; Kılıçaslan, E.E.; Akhan, G.Psychogenic non-epileptic seizures (PNES) are episodic events that bear a resemblance to epileptic seizures (ES) in their outward manifestations, yet they lack pathological electroencephalographic (EEG) activity during the ictal phase. In the Diagnostic and Statistical Manual 5th Edition (DSM-5), PNES is designated as "Functional Neurological Symptom Disorder with seizures". Individuals diagnosed with PNES commonly present with concurrent psychiatric disorders, notably depression, panic disorder, and chronic anxiety. This phenomenon renders PNES a shared affliction within the domains of neurology and psychiatry, thereby mandating the implementation of diverse therapeutic approaches in the management of the condition. Indeed, identifying the presence of concurrent psychiatric disorders in a patient with PNES during the early stages is crucial for devising an appropriate treatment plan. In this study, an assessment was conducted to examine the correlation between PNES semiology and psychiatric disorder comorbidity, to elucidate whether semiological characteristics serve as predictors for the presence of comorbid psychiatric disorders. The PNES patients enrolled were divided into two subgroups based on the presence or absence of accompanying psychiatric disorders (onlyPNES and PNES+). The study assessed disparities in semiological characteristics between the two subgroups of PNES and the results obtained indicate that individual variations in semiotic features are not influenced by the presence of psychiatric comorbidity. © 2024 The AuthorsArticle Citation - WoS: 14Citation - Scopus: 21Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods(World Scientific Publ Co Pte Ltd, 2022) Cura, Ozlem Karabiber; Akan, Aydin; Yilmaz, Gulce Cosku; Ture, Hatice SabihaDementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer's dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.Conference Object Flexible Production Planning MILP Model Including Shift and Overtime Decisions(Elsevier, 2025) Ozel, Oyku; Yilmaz, GorkemThis study introduces a production planning model designed to address the complexities of shift and overtime scheduling while minimizing frequent changes in production settings over short intervals. By incorporating flexible shift and overtime scheduling, the model enables companies to efficiently manage production, inventory, and backlogs while remaining responsive to fluctuating customer demands. The proposed mixed-integer linear programming model (MILP) optimizes production, inventory, and backorder costs through product-production line allocation, shift, and overtime decisions. The objective function minimizes total costs, including fixed and variable production costs, inventory holding costs, backorder costs, shift and overtime transition costs, and idle capacity penalties. Computational experiments validate the effectiveness of the model, demonstrating its ability to improve production efficiency, reduce operational costs, and adapt to dynamic demand conditions. The findings highlight the potential of the proposed approach to support efficient and flexible production planning in dynamic manufacturing environments. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Article Citation - WoS: 5Citation - Scopus: 7Classification of Alzheimer’s Dementia Eeg Signals Using Deep Learning(SAGE Publications Ltd, 2025) Sen, S.Y.; Cura, O.K.; Yilmaz, G.C.; Akan, A.Alzheimer’s dementia (AD) is a predominant neurological disorder arising from corruptions in brain functions and is characterized by a chronic or progressive nature. While the precise etiology of dementia remains incompletely elucidated, its manifestation is frequently associated with discernible structural and chemical alterations in the brain. Living with dementia significantly impacts individuals’ daily lives due to the resultant loss of cognitive functions. This study presents a novel method to monitor and detect AD using advanced signal processing applied to electroencephalography (EEG) signals. The intrinsic time-scale decomposition (ITD) algorithm is employed to extract proper rotation components (PRCs) from EEG signals, utilizing a 5-second EEG segment duration. The proposed method is compared with the detection of 5-second raw EEG segments using a custom one-dimensional convolutional neural network (1D CNN). Additionally, four different quartiles (Quartile 1 (Q1), Q2, Q3, and Q4) of EEG signals are considered to identify the most significant contributor to AD. Experimental results demonstrate that the ITD-based approach yields better detection performance compared to using raw EEG signals. The most promising result is achieved by the EEG-PRCs method in Q1, with an accuracy of 94.00%, sensitivity of 93.50%, and specificity of 93.90%. In contrast, the highest-performing result of the raw EEG segments method is in Q2, with an accuracy of 88.40%, sensitivity of 89.10%, and specificity of 87.60% in terms of detecting AD. © The Author(s) 2024.Conference Object Citation - Scopus: 2Ubiquitous Monitoring System for Critical Rescue Operations(2010) Kondakçı, Süleyman; Yilmaz G.; Kocabıyık, Emre; Coskuner F.; Akçöltekin A.; Yüksel M.S.Real world sensor network deployments and prototype implementations are still a challenging research and development area for scientists and engineers. We present a prototype implementation of a ubiquitous monitoring system (UBIMOS) applying wireless sensor networks. The monitoring system is designed for use by various operation teams, especially by critical rescue and communication teams. With its light weight and secure communication abilities, UBIMOS can be used in a variety of critical operations, ranging from disaster recovery to anti - terror operations. A UBIMOS agent gathers wearable sensor data from individuals, physical locations, and operation team members, and distributes the data to critical decision making and emergency response locations. The distribution of the sensor data is performed in two modes, secure and insecure (but reliable). Therefore, the communication protocol of UBIMOS is implemented to fully support secure node-authentications and secure data exchange operations, as well as insecure but fast data exchange operations. © 2010 IEEE.
