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
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09.02. Internal Sciences
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Current Staff
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1NO POVERTY
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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Documents
8
Citations
45
h-index
4
Documents
10
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27
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10
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3
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34/0
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WoS Citation Count
24
Scopus Citation Count
48
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WoS Citations per Publication
2.40
Scopus Citations per Publication
4.80
Open Access Source
1
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0
| Journal | Count |
|---|---|
| 11th IFAC Conference on Manufacturing Modelling, Management and Control (MIM) -- Jun 30-Jul 03, 2025 -- Trondheim, Norway | 1 |
| 2022 30th Signal Processing and Communications Applications Conference, SIU 2022 | 1 |
| 2022 Medıcal Technologıes Congress (Tıptekno'22) | 1 |
| Acta Psychologica | 1 |
| European Signal Processing Conference | 1 |
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Now showing 1 - 10 of 10
Conference Object Classification of Dementia Eeg Based on Sub-Bands Using Time-Frequency Approaches(Institute of Electrical and Electronics Engineers Inc., 2022-05-15) Cura O.K.; Yilmaz G.C.; Ture H.S.; Akan A.; Yilmaz, Gulce Cosku; Ture, Hatice Sabiha; Cura, Ozlem Karabiber; Akan, AydinAlzheimer's dementia is a highly prevalent disorder among all neurological disorders. In this study, a new method based on time-Frequency (TF) representations such as Short Time Fourier Transform (STFT) and Synchrosqueezing Transform (SST) is proposed to classify EEG segments of AD patients and control subjects. Previous studies have shown that there are distinctive differences in the EEG signals of control subjects and AD patients in the low-frequency EEG subbands. Hence, in the proposed method TF representations of all EEG subbands are used for feature calculation separately. TF energy distributions obtained by SST and STFT approaches are used to calculate 13 TF features to gather distinctive information between EEG segments of control subjects and AD patients. Various classification techniques are utilized to distinguish feature sets of two the groups. Simulation results demonstrate that the proposed method achieve outstanding validation accuracy rates. © 2022 IEEE.Article Citation - WoS: 14Citation - Scopus: 21Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods(World Scientific Publ Co Pte Ltd, 2022-08-09) 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.Article Citation - Scopus: 2Evaluation of the Relationships Between Psychiatric Comorbidity and Seizure Semiology in Psychogenic Non-Epileptic Seizure Patients(Elsevier B.V., 2025-02) 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 AuthorsConference Object Citation - WoS: 2Citation - Scopus: 6Deep Time-Frequency Feature Extraction for Alzheimer's Dementia Eeg Classification(IEEE, 2022-10-31) 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, 2021-11-04) 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(2010-09-07) 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.Conference Object Citation - WoS: 3Citation - Scopus: 4Classification of Psychogenic Non-Epileptic Seizures Using Synchrosqueezing Transform of Eeg Signals(European Signal Processing Conference, EUSIPCO, 2021-08-23) Cura O.K.; Yilmaz G.C.; Türe H.S.; Akan A.; Yilmaz, Gülce Cosku; Türe, Hatice Sabiha; Cura, Ozlem Karabiber; Akan, AydinPsychogenic non-epileptic seizures (PNES) are mostly associated with psychogenic factors, where the symptoms are often confused with epilepsy. Since electroencephalography (EEG) signals maintain their normal state in PNES cases, it is not possible to diagnose using the EEG recordings alone. Therefore, long-term video EEG records and detailed patient history are needed for reliable diagnosis and correct treatment. However, the video EEG recording method is more expensive than the classical EEG. Therefore, it has great importance to distinguish PNES signals from normal epileptic seizure (ES) signals using only the EEG recordings. In the proposed study, using the Synchrosqueezed Transform (SST) that gives high-resolution time-frequency representations (TFR), inter-PNES, PNES, and Epileptic seizure EEG classification is introduced. 17 joint TF features are calculated from the TFRs, and various classifiers are used for classification processes. Classification problems with three classes (inter-PNES, PNES, and ES) and two classes (inter-PNES and PNES) are considered. Experimental results indicated that both three-class and two-class classification approaches achieved encouraging validation performances (three-class problem: 95.8% ACC, 86.9% SEN, 91.4% PRE, and 8.6% FDR; two-class problem: 96.4% ACC, 96.8% SEN, 97.3% PRE, and FDR lower than 10%). © 2021 European Signal Processing Conference. All rights reserved.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, 2024-08-13) 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(2010-09) 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.
