Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/3

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Citation - WoS: 5
Citation - Scopus: 4
12th June 2017 Offshore Karaburun-Lesvos Island Earthquake Coseismic Deformation Analysis Using Continuous Gps and Seismological Data
(SCIENTIFIC TECHNICAL RESEARCH COUNCIL TURKEY-TUBITAK, 2021) Yıldız, Hasan; Çırmık, Ayça; Pamukçu, Oya; Özdağ, Özkan Cevdet; Gönenç, Tolga; Kahveci, Muzaffer
Understanding the tectonic mechanism generated by the earthquakes and faults is possible only if the preseismic, coseismic and postseismic crustal deformation related to the earthquakes is determined properly. By the analysis of continuous GPS (CGPS) coordinate time series, it is possible to estimate the crustal deformation. Besides, accelerometer records at strong motion stations (SMSs) may support the CGPS-based estimates. In this study, CGPS coordinate time series were analyzed in comparison with the accelerometer records for clarifying the coseismic deformation caused by the earthquake occurred in the surrounding of Lesvos fault located in the northern part of Karaburun within the active mechanism that controls the area where the earthquakes occurred during June 2017 on the offshore Karaburun. The activity of this fault continued throughout June 2017 until the time when the main shock (12th June 2017, M-W = 6.2) occurred. We analyzed CGPS coordinate time series of AYVL and CESM and DEUG stations to determine the coseismic deformation due to the offshore Karaburun-Lesvos Island earthquake using the empirical mode decomposition (EMD) method. Besides, the EMD method results were compared with the accelerometer records obtained from the SMSs close to the CGPS stations and CGPS-based results were found to be consistent with the accelerometer records. Additionally, the horizontal displacements were calculated by Coulomb 3.3 software using different focal plane solutions and compared with CGPS-based results. Consequently, it is suggested an integrated use of CGPS and strong motion accelerometer networks for the joint assessment of the crustal deformation and for the cost-effective use of existing observation networks as well as for the establishment of future observation networks at lower cost.
Citation - WoS: 1
Citation - Scopus: 2
Adaptive State Feedback Control Method Based on Recursive Least Squares
(Kauno Technologijos Universitetas, 2022) Levent, Mehmet Latif; Aydoğdu, Ömer
This study revealed an adaptive state feedback control method based on recursive least squares (RLS) that is introduced for a time-varying system to work with high efficiency. Firstly, a system identification block was created that gives the mathematical model of the time-varying system using the input/output data packets of the controller system. Thanks to this block, the system is constantly monitored to update the parameters of the system, which change over time. Linear quadratic regulator (LQR) is renewed according to these updated parameters, and self-adjustment of the system is provided according to the changed system parameters. The Matlab/Simulink state-space model of the variable loaded servo (VLS) system module was obtained for the simulation experiments in this study; then the system was controlled. Moreover, experiments were carried out on the servo control experimental equipment of the virtual simulation laboratories (VSIMLABS). The effectiveness of the proposed new method was observed taking the performance indexes as a reference to obtain the results of the practical application of the proposed method. Regarding the analysis of simulation and experimental results, the proposed approach minimizes the load effect and noise and the system works at high efficiency. © 2022 Kauno Technologijos Universitetas. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 2
Adaptive Traffic Management Model for Signalised Intersections
(Kauno Technologijos Universitetas, 2024) Yalcinli, F.; Akdemir, B.; Durdu, A.
As population increases, one of the factors affecting life is traffic. Efficient traffic management has a direct positive impact on issues such as time, carbon dioxide emissions, and fuel consumption. Today, an important parameter under the heading of traffic is the signalling systems for intersections, which are operated with fixed-time, semi-actuated, fully actuated, and fully adaptive control methods. In this study, an adaptive traffic management model is developed for signalised intersections. The adaptive traffic management model developed includes phase extension with minimum and maximum time intervals dependent on density and phase skip features. Additionally, the most distinctive feature of the model is its flexible phase structure rather than a sequential phase. The Heybe intersection, located within the boundaries of Antalya province, is modelled one-to-one in the simulation of urban mobility (SUMO) simulation programme with real intersection data. The developed adaptive traffic management model is applied to the Heybe intersection, and the effects of the model are revealed. Improvements obtained from the SUMO simulation programme were verified through visual inspection, and high-accuracy results were determined. As a result of the studies, it was found that the application of the adaptive traffic management model developed at Heybe intersection, which has approximately 50,000 vehicles passing daily, resulted in a 27.2% improvement in the average delay per vehicle parameter, a 32.4% improvement in the average waiting time per vehicle parameter, and a 16.7 % improvement in the average speed per vehicle parameter. © 2024 Kauno Technologijos Universitetas. All rights reserved.
Analysis and Design of a Permanent Magnet Linear Synchronous Motor Based on Inductance Calculation
(Polska Akad Nauk, Polish Acad Sciences, 2025) Yucel, Enes; Mutluer, Mumtaz; Cunkas, Mehmet
This paper presents a comprehensive design and analysis methodology for a Permanent Magnet Linear Synchronous Motor (PMLSM), with a focus on evaluating different inductance modeling approaches. The motor design begins with analytical dimensioning based on defined design parameters. A two-dimensional finite element analysis follows this in ANSYS Maxwell to verify magnetic saturation, back-EMF, flux linkage, and electromagnetic performance under full load conditions. The inductance parameters are calculated using both conventional and look-up table (LUT) based models. In the conventional model, seven different methods are tested under static and dynamic conditions, as well as in non-salient and salient scenarios, and their results are compared. In the LUT model, current-dependent inductance values are extracted from flux linkage maps. The motor designed in Maxwell, along with the calculated inductance data, is integrated into a dynamic cooperative simulation (co-sim) model controlled by an inverter in Simplorer to analyze the thrust force. The results show that the LUT model provides outputs that are closer to the co-sim reference than the traditional model. Furthermore, performance curves based on the Maximum Torque Per Ampere strategy are generated, and the force-speed and power-speed characteristics derived from both inductance models are compared. The findings emphasize the importance of accurate inductance modeling in capturing the actual electromagnetic behaviour of PMLSM under dynamic operating conditions.
Citation - WoS: 2
Citation - Scopus: 2
An Approach To Determine Pathological Breast Tissue Samples With Free-Space Measurement Method at 24 Ghz
(WILEY, 2024) Toprak, Rabia; Gultekin, Seyfettin Sinan; Kayabasi, Ahmet; Çelik, Zeliha Esin; Tekin, Fatma Hicret; Uzer, Dilek
Pathology is an important branch of science in the diagnosis and treatment of several diseases. In cancer diseases, serious investigations have been made about the course of the diseases. A report that is essential for both the patient and the doctor is prepared by the pathologists as a result of a detailed cellular examination. These reports contain information about the disease. Access duration to these reports, which affects the form and duration of the treatment, is extremely important today. It is possible to shorten this period with systems using antenna technologies. The pathological breast tissue samples have been examined by using horn antenna structures with high gain in this study. Dual identical horn antennas have been placed opposite each other as receivers and transmitters in the measurement setup at 24 GHz. Measurements of normal and cancerous breast tissues have been made, and the normalization process has been applied to the measured scattering parameters. The different values between normal and cancerous breast tissues have been shown with this process. The normalized values are compared with other analyzed values. According to the results obtained, the percentage of normalized values for transmission is much more effective and meaningful than other results.
Citation - WoS: 3
Citation - Scopus: 4
Approaches To Automated Land Subdivision Using Binary Search Algorithm in Zoning Applications
(Ice Publishing, 2022) Koç, İsmail; Çay, Tayfun; Babaoğlu, İsmail
The planned development of urban areas depends on zoning applications. Although zoning practices are performed using different techniques, the parcelling operations that shape the future view of the city are the same. Preparing the parcelling plans is an important step that has a direct impact on ownership structure and reallocation. Parcelling operations are traditionally handled manually by a technician. This is a serious problem in terms of time and cost. In this study, by taking the zoning legislation, the production of a pre-land subdivision plan has been automatically performed for a region of Konya, which is one of the major cities in Turkey. The parcelling processes have been performed in three different ways: the first parcelling technique is parcelling with edge values, the second is parcelling with area values and the third is parcelling using both edge and area values together. For the entire parcelling process, the area of the parcel has been calculated using the Gauss method. Moreover, to effectively determine the boundaries and to calculate the parcel area in the parcelling process, the binary search technique has been used in all the methods. The experimental results show that the parcelling operations were carried out very quickly and successfully.
Citation - WoS: 24
Citation - Scopus: 23
Artificial Bee Colony Algorithm for Solving Multi-Objective Distributed Fuzzy Permutation Flow Shop Problem
(IOS Press BV, 2022) Baysal, Mehmet Emin; Sarucan, Ahmet; Büyüközkan, Kadir; Engin, Orhan
The distributed permutation flow shop scheduling (DPFSS) is a permutation flow shop scheduling problem including the multi-factory environment. The processing times of the jobs in a real life scheduling problem cannot be precisely know because of the human factor. In this study, the process times and due dates of the jobs are considered triangular and trapezoidal fuzzy numbers for DPFSS environment. An artificial bee colony (ABC) algorithm is developed to solve the multi-objective distributed fuzzy permutation flow shop (DFPFS) problem. First, the proposed ABC algorithm is calibrated with the well-known DPFSS instances in the literature. Then, the DPFSS instances are fuzzified and solved with the algorithm. According to the results, the proposed ABC algorithm performs well to solve the DFPFS problems. © 2022 - IOS Press. All rights reserved.
Citation - Scopus: 4
Artificial Bee Colony Algorithm for Solving Multi-Objective Distributed Fuzzy Permutation Flow Shop Problem [2]
(IOS Press BV, 2021) Baysal, M.E.; Sarucan, A.; Büyüközkan, K.; Engin, O.
The distributed permutation flow shop scheduling (DPFSS) is a permutation flow shop scheduling problem including the multi-factory environment. The processing times of the jobs in a real life scheduling problem cannot be precisely know because of the human factor. In this study, the process times and due dates of the jobs are considered triangular and trapezoidal fuzzy numbers for DPFSS environment. An artificial bee colony (ABC) algorithm is developed to solve the multi-objective distributed fuzzy permutation flow shop (DFPFS) problem. First, the proposed ABC algorithm is calibrated with the well-known DPFSS instances in the literature. Then, the DPFSS instances are fuzzified and solved with the algorithm. According to the results, the proposed ABC algorithm performs well to solve the DFPFS problems. © 2022 - IOS Press. All rights reserved.
Automatic Phase Reversal Detection in Routine Eeg
(CHURCHILL LIVINGSTONE, 2020) Yıldırım, Sema; Koçer, Hasan Erdinç; Ekmekçi, Ahmet Hakan
Electroencephalograph (EEG), a valuable tool in the clinical evaluation, is readily available, safe and provides information about brain function. EEG interpretation is important for the diagnosis of neurological disorders. The long-term EEG data may be required to document and study neurosciences that include many epileptic activities and phase reversal (PR) etc. However, analyze of the long-term EEG done by an expert neurologist is much time consuming and quite difficult. Therefore, an automatic PR determination method for analyzing of long-term EEG is described in this study. The presented technique was applied to the pathological EEG recordings that were obtained from two different datasets gathered as a retrospective in Selcuk University Hospital (SUH) and Boston Children's Hospital (BCH). With this method, PR in the dataset was determined and then compared with the ones detected by the specialist doctor. Two tests were carried out in the SUH dataset and the classification success of the method was 83.22% for test 1 and 85.19% for test 2. On the other hand, three tests were carried out for two different position values for BCH dataset. The highest classification success of the six tests was 75% for test 5, while the lowest classification success appeared as 58.33% for test 6. As a result, the overall success in the detection of PR with the conducted method is 84.20% for SUH and 66.7% for BCH. According to these results, the determination of PR that is known to be indicative of neurological disorders and presenting them to expert information will accelerate the interpretation of long-term EEG recordings.
Citation - Scopus: 1
Automatic Sleep Stage Classification for the Obstructive Sleep Apnea
(Trans Tech Publications Ltd, 2023) Özsen, Seral; Koca, Yasin; Tezel, Gülay Tezel; Solak, Fatma Zehra; Vatansev, Hulya; Kucukturk, Serkan
Automatic sleep scoring systems have been much more attention in the last decades. Whereas a wide variety of studies have been used in this subject area, the accuracies are still under acceptable limits to apply these methods to real-life data. One can find many high-accuracy studies in literature using a standard database but when it comes to using real data reaching such high performance is not straightforward. In this study, five distinct datasets were prepared using 124 persons including 93 unhealthy and 31 healthy persons. These datasets consist of time-, nonlinear-, welch-, discrete wavelet transform- and Hilbert-Huang transform features. By applying k-NN, Decision Trees, ANN, SVM, and Bagged Tree classifiers to these feature sets in various manners by using feature-selection highest classification accuracy was searched. The maximum classification accuracy was detected in the case of the Bagged Tree classifier as 95.06% with the use of 14 features among a total of 136 features. This accuracy is relatively high compared with the literature for a real-data application.
Citation - WoS: 2
Citation - Scopus: 2
Autonomously Simultaneous Localization and Mapping Based on Line Tracking in a Factory-Like Environment
(VSB-TECHNICAL UNIV OSTRAVA, 2019) Durdu, Akif; Korkmaz, Mehmet
This study is related to SLAM, also known simultaneous localization and mapping which is highly important and an indispensable issue for autonomous mobile robots. Both an environment mapping and an agent's localization are provided with SLAM systems. However, while performing SLAM for an unknown environment, the robot is navigated by three different ways: a user guidance, random movements on an exploration mode or exploration algorithms. A user guidance or random exploration methods have some drawbacks that a user may not be able to observe the agent or random process may take a long time. In order to answer these problems, it is searched for a new and autonomous exploration algorithm for SLAM systems. In this manner, a new kind of left-orientated autonomous exploration algorithm for SLAM systems has been improved. To show the algorithm effectiveness, a factorylike environment is made up on the ROS (Robot Operating System) platform and navigation of the agent is observed. The result of the study demonstrates that it is possible to perform SLAM autonomously in any similar environment without the need of the user interference.
Citation - WoS: 5
Citation - Scopus: 5
The Backstage of Twice-Exceptionality: a Systematic Review of the Movies
(Routledge Journals, Taylor & Francis Ltd, 2022) Atmaca, Furkan; Yağbasanlar, Osman; Yıldız, Erol; Göncü, Ahmet; Baloğlu, Mustafa
Twice-exceptional individuals (2e) are highly gifted/talented or creative but have a disability in at least in one developmental area. In order to reveal more about the condition we systematically reviewed movies that depict 2e individuals to reveal how they are portrayed. Eight movies were analyzed in depth. The selected movies were independently watched and encoded. As a result, a total of 54 codes were generated, which were combined under six themes, most themes having two categories (i.e., positive versus negative or strengths versus weaknesses). Despite being perceived more positively on the cognitive themes, they are portrayed mostly negatively on the socioemotional and behavioral themes. The movies conveyed significant messages about the educational lives and familial difficulties of these individuals.
Citation - WoS: 4
Citation - Scopus: 5
The Classification of Eye Diseases From Fundus Images Based on Cnn and Pretrained Models
(Czech Technical University in Prague, 2024) Benbakreti, S.; Benbakreti, S.; Ozkaya, U.
Visual impairment affects more than a billion people worldwide due to insufficient care or inadequate vision screening. Computer-aided diagnosis using deep neural networks is a promising approach, it can analyse and process retinal fundus images, providing valuable reference data for doctors in clinical diagnosis or screening. This study aims to achieve an accurate classification of fundus images, including images of healthy patients as well as those with diabetic retinopathy, cataracts, and glaucoma, using a convolutional neural network (CNN) architecture and several pretrained models (AlexNet, GoogleNet, ResNet18, ResNet50, YOLOv3, and VGG 19). To enhance the training process, a mirror effect technique was applied to augment the volume of data. The experimental study resulted in very satisfactory outcomes, with the GoogleNet model paired with the SGDM optimiser achieving the highest accuracy (92.7 %). © 2024 The Author(s).
Citation - WoS: 5
Citation - Scopus: 5
Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms
(INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC, 2020) Örnek, Ahmet Haydar; Ervural, Saim; Ceylan, Murat; Konak, Murat; Soylu, Hanifi; Savaşçı, Duygu
Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.
Citation - WoS: 9
Citation - Scopus: 15
Clustering-Based Plane Refitting of Non-Planar Patches for Voxel-Based 3d Point Cloud Segmentation Using K-Means Clustering
(INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC, 2020) Sağlam, Ali; Makineci, Hasan Bilgehan; Baykan, Ömer Kaan; Baykan, Nurdan Akhan
Point cloud processing is a struggled field because the points in the clouds are three-dimensional and irregular distributed signals. For this reason, the points in the point clouds are mostly sampled into regularly distributed voxels in the literature. Voxelization as a pretreatment significantly accelerates the process of segmenting surfaces. The geometric cues such as plane directions (normals) in the voxels are mostly used to segment the local surfaces. However, the sampling process may include a non-planar point group (patch), which is mostly on the edges and corners, in a voxel. These voxels can cause misleading the segmentation process. In this paper, we separate the non-planar patches into planar sub-patches using k-means clustering. The largest one among the planar sub-patches replaces the normal and barycenter properties of the voxel with those of itself. We have tested this process in a successful point cloud segmentation method and measure the effects of the proposed method on two point cloud segmentation datasets (Mosque and Train Station). The method increases the accuracy success of the Mosque dataset from 83.84% to 87.86% and that of the Train Station dataset from 85.36% to 87.07%.
Çocuk Hekimlerinin Antibiyotik Seçiminde Çoklu Faktörlerin Değerlendirilmesi
(Bilimsel Tip Yayinevi, 2025) Sarucan, Ahmet; Büyükdogan, Bırol; Çiftci, Fatma; Afşar, Bilge; Dişci, Işık
Giriş: Pediyatri bölümü sıklıkla antibiyotik kullanmaktadır. Antibiyotik reçetelemeyi etkileyen faktörlerin anlaşılması tedavi başarısını artırabilir. Bu çalışma, antibiyotik seçimi ve genel tedavi etkinliğindeki rollerini vurgulayarak bu faktörleri araştırmayı amaçlamıştır. Gereç ve Yöntemler: Çalışmada birincil veriler kullanılmıştır. Araştırmanın kapsamını Konya ili ve ilçelerinde görev yapan çocuk hekimleri oluşturmaktadır. Araştırmanın örneklem büyüklüğü kartopu örnekleme yöntemine göre hesaplanmıştır. Örneklem büyüklüğü, araştırma kriterlerine uyan bir çocuk doktorunun seçilmesi ile başlar ve seçilen çocuk doktorunun yardımı ile diğer çocuk doktorlarına ulaşılır. Çocuk doktoruna aynı özelliklere sahip kişi veya kişileri tavsiye etmesi söylenir. Böylece söz konusu örnekleme yöntemi kullanılarak çalışmada 50 çocuk hekimine ulaşılmıştır. Bu çalışmada çocuk hekimlerinin antibiyotik seçimindeki çoklu faktörlerin belirlenmesi amacıyla kamu kurum ve kuruluşlarında çalışan uzmanların görüşleri alınmıştır. Araştırmacılar tarafından gönüllülük esasına dayalı olarak çocuk hekimlerine yüz yüze anket uygulanarak elde edilen veriler Entropi ve TOPSIS yöntemi kullanılarak analiz edilmiştir. Bulgular: Hekimler tarafından tercih edilen ilk antibiyotik ajan amoksisilin ve klavulanik asit oldu. İkinci sırada azitromisin, üçüncü sırada klaritromisin yer alırken, son iki sırayı sefdinir ve sefiksim paylaştı. Çalışmada hekim ve ebeveynle ilgili faktörler açısından elde edilen sonuçların genel sonuçlarla aynı olduğu gözlendi. İlaçla ilgili faktörler, maliyet ve geri ödeme koşulları sonuçları aynı olurken, Azitromisin bu sonuçlarda ilk sırada yer aldı. Tüm alt çözümlerde ikinci sıradaki seçenek ve son iki sıra aynıdır. Hekimlerin cinsiyetine göre beş erkek pediyatri hekiminin tercih sıralaması amoksisilin klavulanik asit > azitromisin > klaritromisin > sefdinir > sefiksim şeklindeyken, 15 kadın hekimin tercihleri azitromisin > amoksisilin klavulanik asit > klaritromisin > sefdinir > sefiksim şeklinde belirlenmiştir. Azitromisin ilk sırada tercih edilmiştir. Sonuç: Çoklu faktörler hem tanı hem de tedaviyi etkileyerek antibiyotik seçimini etkiler ve tedavi başarısını artırır.
A Comparative Study on Experimental and FEA-Based Simulation of Dry Sliding Wear Behavior of Boronized AISI 304 Stainless Steel at Elevated Temperatures
(Pleiades Publishing Ltd, 2025) Gok, Mustafa Sabri; Kucuk, Yilmaz; Khosravi, Farshid; Gunen, Ali; Karakas, Mustafa Serdar; Guden, Mustafa
In this study, the influence of boronizing on the high-temperature wear behavior of AISI 304 was examined experimentally and with FEA simulation. Boronizing, conducted at 950 degrees C for 3 h using the powder-pack boronizing technique, showed an approximately 7-fold increase in hardness compared to untreated sample. Boride layer characterization was performed using XRD, SEM, and EDS line analyses. Wear tests were performed at ambient temperatures of 25, 250, and 500 degrees C. While the wear rates of the untreated sample increased dramatically with increasing temperature, those of the boronized samples were significantly limited. FEA simulation using the Johnson-Cook fracture model demonstrated a high degree of consistency with the experimental wear profiles and this alignment enables reliable wear predictions. The oxide layer formation was observed on the worn surface of boronized samples during the tests at elevated temperatures, resulting in less plastic deformation.
Citation - WoS: 2
Citation - Scopus: 3
A Comparison of Improved Nature-Inspired Algorithms for Optimal Power System Operation
(ROMANIAN SOC CONTROL TECH INFORMATICS, 2018) Shehu, Gaddafi S.; Çetinkaya, Nurettin
The influencing factors associated with the efficient operation of power systems are minimum fuel cost and losses in the transmission line. Optimal Power Dispatch (OPD) problem is treated to minimize instantaneous operating cost, incremental cost, and transmission line losses considering various network operating constraint. Newly developed Nature-inspired optimization algorithms approach are proposed in this analysis with robust parameter selections. The results of most popular Genetic Algorithm (GA) and based on swarm behavior Particle Swarm Optimization (PSO) are compared with four Nature-inspired metaheuristic algorithms of Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and Firefly Algorithm (FA). The quadratic cost function of power generation and penalty function to account for inequality constraints on dependent variables are added for solving OPD problem. A common algorithms evaluation parameters such as population size and generation limit are designated on an equal scale. Explicit parameters for each algorithm are tuned properly for optimal operations. The algorithms are tested on IEEE-26 and IEEE-30 system. Analysis Outcomes obtained showcase the efficiency of each algorithms parametric turning improvement.
Citation - WoS: 9
Citation - Scopus: 13
Comparison of the Effects of Mel Coefficients and Spectrogram Images Via Deep Learning in Emotion Classification
(INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC, 2020) Demircan, Semiye; Örnek, Humar Kahramanlı
In the present paper, an approach was developed for emotion recognition from speech data using deep learning algorithms, a problem that has gained importance in recent years. Feature extraction manually and feature selection steps were more important in traditional methods for speech emotion recognition. In spite of this, deep learning algorithms were applied to data without any data reduction. The study implemented the triple emotion groups of EmoDB emotion data: Boredom, Neutral, and Sadness-BNS; and Anger, Happiness, and Fear-AHF. Firstly, the spectrogram images resulting from the signal data after preprocessing were classified using AlexNET. Secondly, the results formed from the MelFrequency Cepstrum Coefficients (MFCC) extracted by feature extraction methods to Deep Neural Networks (DNN) were compared. The importance and necessity of using manual feature extraction in deep learning was investigated, which remains a very important part of emotion recognition. The experimental results show that emotion recognition through the implementation of the AlexNet architecture to the spectrogram images was more discriminative than that through the implementation of DNN to manually extracted features.
Citation - WoS: 1
Citation - Scopus: 1
Comparison of Time-Frequency Analyzes for a Sleep Staging Application With Cnn
(Trans Tech Publications Ltd, 2022) Efe, Enes; Özşen, Seral
Sleep staging is the process of acquiring biological signals during sleep and marking them according to the stages of sleep. The procedure is performed by an experienced physician and takes more time. When this process is automated, the processing load will be reduced and the time required to identify disease will also be reduced. In this paper, 8 different transform methods for automatic sleep-staging based on convolutional neural networks (CNNs) were compared to classify sleep stages using single-channel electroencephalogram (EEG) signals. Five different labels were used to stage the sleep. These are Wake (W), NonREM-1 (N1), NonREM-2 (N2), NonREM-3 (N3), and REM (R). The classifications were done end-to-end without any hand-crafted features, ie without requiring any feature engineering. Time-Frequency components obtained by Short Time Fourier Transform, Discrete Wavelet Transform, Discrete Cosine Transform, Hilbert-Huang Transform, Discrete Gabor Transform, Fast Walsh-Hadamard Transform, Choi-Williams Distribution, and Wigner-Willie Distribution were classified with a supervised deep convolutional neural network to perform sleep staging. The discrete Cosine Transform-CNN method (DCT-CNN) showed the highest performance among the methods suggested in this paper with an F1 score of 89% and a value of 0.86 kappa. The findings of this study revealed that the transformation techniques utilized for the most accurate representation of input data are far superior to traditional approaches based on manual feature extraction, which acquires time, frequency, or nonlinear characteristics. The results of this article are expected to be useful to researchers in the development of low-cost, and easily portable devices.

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