Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Quasi-Static Operation of 2-Axis Microscanners With AlN Piezoelectric Quad-Actuators
(Institute of Electrical and Electronics Engineers Inc., 2021) Hah, D.
Aluminum nitride (AlN) started to draw attentions as a material for piezoelectric actuation owing to its CMOS process compatibility and safeness for biomedical applications. Due to its relatively low piezoelectric coefficients, AlN-based piezoelectric actuators have been mostly operated in resonance modes, especially in optical scanning. This paper presents a novel design of a 2-axis-tilt microscanner with AlN piezoelectric quad-actuators and meander-shaped hinges for reasonable quasi-static operation. Through finite-element-method simulation, it is shown that the proposed device can have about 9 degree of optical scan angle in two dimensions with the voltage amplitude of 50 V. Lissajous scanning operation of the device is demonstrated as well via simulation. © 2021 Elsevier B.V., All rights reserved.
Citation - Scopus: 19
A Novel Feature Design and Stacking Approach for Non-Technical Electricity Loss Detection
(Institute of Electrical and Electronics Engineers Inc., 2018) Aydin, Zafer; Güngör, Vehbi Çağrı
Non-technical electricity losses continue to jeopardize economic and social well-being of many countries. In this work, we develop machine learning classifiers that can identify anomalous electricity consumption in Turkey. Starting from weekly electricity usage data, we develop new features that capture statistical and frequency domain characteristics of the customers and their consumption patterns. We analyze the effect of reducing number of feature descriptors through dimensionality reduction and feature selection techniques. To overcome the class imbalance problem, we implement several ensemble methods and compare their prediction accuracy to those of the standard classifiers. The proposed features and combining strengths of different classifiers bring significant improvements on performance metrics, which is demonstrated through detailed simulations on shopping mall sector. We anticipate that advances in this field will contribute to the economies considerably. © 2018 Elsevier B.V., All rights reserved.
Citation - Scopus: 5
Emotion Detection Using Multivariate Synchrosqueezing Transform via 2D Circumplex Model
(Institute of Electrical and Electronics Engineers Inc., 2018) Ozel, Pinar; Akan, Aydin; Yilmaz, Bulent; Özel, Pınar; Akan, Aydin I.; Yilmaz, Bulent
Emotion detection by utilizing signal processing methods is a challenging area. An open issue in emotional modeling is to obtain an optimum feature set to use for the classification process. This study proposes an approach for emotional state classification by the investigation of EEG signals via multivariate synchrosqueezing transform (MSST). MSST is a post-processing technique to compose a localized time-frequency representation yielding multivariate syncyrosqueezing coefficients. After obtaining these coefficients from EEG signals for 18 subjects from DEAP dataset, coefficients and self-assessment-mannequins (SAM) labels of those subjects are used for emotional state classification by using support vector machines (SVM) nearest neighbor, decision tree, and ensemble methods. The accuracy rate is 70.6% for high valence high arousal (HVHA), 75.4% for low valence high arousal (LVHA), 77.8% for high valence low arousal (HVLA), and 77.2% for low valence low arousal (LVLA) cases using SVM. © 2019 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Detection of Epileptic Seizures With Tangent Space Mapping Features of EEG Signals
(IEEE, 2021) Altindis, Fatih; Yilmaz, Bulent
Detection of epileptic seizures from EEG signals is well-studied topic for the last couple of decades. Lately, automated signal processing and machine learning methods were developed to detect epileptic seizures. However, most of the methods are tailored to subjects and require fine tuning of many parameters. In this study, we proposed to use Riemannian geometry-based signal processing method that already showed superior performance on brain-computer interface problems, to extract features. We showed that tangent space mapping features of EEG signals can be used to detect seizures with high accuracy and precision.
Citation - Scopus: 7
A Comparative Analysis on Medical Article Classification Using Text Mining & Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2021) Kolukisa, Burak; Dedeturk, Bilge Kagan; Dedeturk, Beyhan Adanur; Gulsen, Abdulkadir; Bakal, Gokhan
The document classification task is one of the widely studied research fields on multiple domains. The core motivation of the classification task is that the manual classification efforts are impractical due to the exponentially growing document volumes. Thus, we densely need to exploit automated computational approaches, such as machine learning models along with data & text mining techniques. In this study, we concentrated on the classification of medical articles specifically on common cancer types, due to the significance of the field and the decent number of available documents of interest. We deliberately targeted MEDLINE articles about common cancer types because most cancer types share a similar literature composition. Therefore, this situation makes the classification effort relatively more complicated. To this end, we built multiple machine learning models, including both traditional and deep learning architectures. We achieved the best performance (R¿82% F score) by the LSTM model. Overall, our results demonstrate a strong effect of exploiting both text mining and machine learning methods to distinguish medical articles on common cancer types. © 2022 Elsevier B.V., All rights reserved.
Citation - Scopus: 2
Investigation of the Beneficiation of Low Grade Manganese Ores
(Chamber of Mining Engineers of Turkey maden@maden.org.tr, 2013) Bayat, Oktay; Altiner, Mahmut; Top, S.
In this study, beneficiation of low grade manganese ores was investigated by applying high intensity dry magnetic separation, MGS (Multi Gravity Separator) and flotation methods. Manganese grades of the ores were 25.65% Mn and 13.96% Mn taken from Antalya and Kayseri regions, respectively. Flotation and magnetic separation recoveries of both tested samples were low and the grades of the concentrates were less than 45% Mn. Similar results were also observed using a lab-type MGS but a concentrate could be obtained with 41.24% Mn and 78.71% recovery for manganese ores taken from Antalya region. © 2014 Elsevier B.V., All rights reserved.
Stimuli-Responsive and Self-Assembled Sericin Materials for Various Applications
(Elsevier, 2025) Arabaci, N.; Demirbas, A.; Dadi, S.; Dogan, F.; Öçsoy, I.
The silkworm cocoon's structural integrity is maintained by sericin, which acts as a sticky binding layer that envelops the fibroin fibers, effectively holding them together. In the silk industry, sericin is removed from the structure of fibroin during the degumming process in order to provide the silk's whiteness, softness, and smoothness and also to make it dyeable. Sericin, which is separated from the fibroin of the cocoon by the degumming process in the textile industry in the production of silk fabric, is discarded as waste material. This waste helps cell attachment, proliferation, and differentiation in sericin-based materials, owing to its biocompatibility, biodegradability, and bioactivity features. Due to all these specific features, sericin protein is involved in the production of various biomaterials such as films, hydrogels, scaffolds, conduits, fibers, and devices used in tissue engineering and regenerative medicine. © 2025 Elsevier B.V., All rights reserved.
Evaluating the Impact of Sentiment Analysis on Deep Reinforcement Learning-Based Trading Strategies
(Institute of Electrical and Electronics Engineers Inc., 2024) Etcil, Mustafa; Kolukisa, Burak; Bakir-Güngör, Burcu
Portfolio optimization is a form of investment management that aims to maximize returns while minimizing risks. However, the inherent complexity and unpredictability of financial markets pose a challenge. Recent advancements in machine learning, particularly in deep reinforcement learning (DRL), offer promising solutions by enabling dynamic and adaptive trading strategies. This paper presents a comprehensive evaluation of three actor-critic-based DRL algorithms-Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO)-applied to portfolio optimization. These strategies were implemented in both sentiment-aware and non-sentiment-aware versions, allowing for a direct comparison of their performance. The sentiment-aware models incorporated sentiment analysis using FinBERT and knowledge graphs to measure market sentiment from financial news, while the non-sentiment-aware models relied solely on stock prices and technical indicators. Our comparative study demonstrates that incorporating sentiment analysis resulted in consistently superior risk-adjusted returns and portfolio resilience during market fluctuations compared to non-sentiment-aware strategies. © 2025 Elsevier B.V., All rights reserved.
Gain Enhancement of a mmWave Patch Antenna Array Having Limited Number of Input Ports
(Institute of Electrical and Electronics Engineers Inc., 2024) Keskin, Mehmet Ziya; Yentur, Abdulkadir; Ozdur, Ibrahim T.; Kiliç, Veli Tayfun
mmWave sensors are rapidly being used in various fields due to their low power consumption, compact size, and cost-effectiveness, particularly for applications like target detection and tracking. This study investigates the gain enhancement of a patch antenna array operating in the mmWave frequency band of 76 GHz - 81 GHz, where commercial single-chip integrated circuits are available. We analyze the S-parameters and radiation patterns of a reference antenna in detail, comparing simulation results with experimental measurements to ensure accuracy. Acknowledging the challenges posed by mmWave patch antennas, we propose a straightforward method to enhance peak gain while preserving the capabilities of the commercial patch structure. Specifically, by twinning one of the input port signals using a power divider, we increase the number of elements in the array without altering the number of input ports. Our findings suggest that this technique can increase the peak gain by 1.3 dB and narrow the beam by 4°, resulting in practical benefits such as enhanced target detection range and accuracy in radar applications while mostly preserving the functioning of the system. © 2025 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
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.
Citation - Scopus: 5
Development of Knowledge Based Response Correction for a Reconfigurable N-Shaped Microstrip Antenna Design
(Institute of Electrical and Electronics Engineers Inc., 2015) Aoad, Ashrf; Simsek, Murat; Aydin, Zafer
This study presents the use of prior knowledge of inverse artificial neural network (ANN) to model and optimize a reconfigurable N-shaped microstrip antenna. Three accurate prior knowledge inverse ANNs with large amount training data are proposed where the frequency information is incorporated into the structure of ANN. The complexity of the input/output relationship is reduced by using prior knowledge. Three separate methods of incorporating knowledge in the second step of the training process with a multilayer perceptron (MLP) in the first step are demonstrated and their results are compared to EM simulation. © 2023 Elsevier B.V., All rights reserved.
Range-Based Wireless Sensor Network Localization by a Circumnavigating Mobile Anchor Without Position Information
(IEEE, 2024) Guler, Samet
Typical range-based wireless sensor network (WSN) localization approaches aim at estimating the sensor node positions by using a set of anchors with known positions. In some applications, assuming the knowledge of the anchors' positions may be impractical, and estimation of the sensors' positions in an arbitrary fixed frame may be sufficient. Considering such scenarios, we propose a WSN localization algorithm by single mobile anchor without self location information. The mobile anchor obtains distance measurements from the sensors while tracking a custom trajectory which is shown to improve the localization performance over time for high signal-to-noise ratio cases. By utilizing two stationary reference nodes within the WSN, the proposed framework generates sensor node position estimation up to translation and rotation with sufficient precision in the absence of global positioning aids. We foresee that the proposed framework can demonstrate benefits in several WSN applications ranging from internet-of-things to service robotics.
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
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: 3
Endüstriyel Kablosuz Algılayıcı Ağlarda Hata Kontrol Sistemlerinin Ağ Yaşam Süresine Etkileri
(IEEE, 2019) Tekin, Nazli; Gungor, V. Cagri
Due to the harsh channel conditions of the industrial environment, the data transmission over wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of vital importance for battery-powered Wireless Sensor Networks (WSNs). In this paper, the performance evaluation of error control schemes namely, Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ) in industrial environment in terms of energy efficiency is presented. The impact of the existing error control schemes on the industrial wireless sensor network lifetime is analyzed. A novel Mixed Integer Programming (MIP) framework is developed to maximize network lifetime. Performance results show that utilizing BCH (31,21,5) for Telos at the link layer maximizes the network lifetime while attaining the desired application reliability rate.
Citation - WoS: 3
Citation - Scopus: 3
Performance Improvements of Photonic Lantern Based Coherent Receivers
(Institute of Electrical and Electronics Engineers Inc., 2014) Ozdur, Ibrahim T.; Toliver, Paul; Woodward, Ted K.
In this work, the signal-to-noise ratio improvement of photonic lantern-based coherent receivers over single-mode coherent receivers is demonstrated. The signal-to-noise ratio is improved by a factor of 2.8 when other parameters kept constant. © 2021 Elsevier B.V., All rights reserved.
Citation - Scopus: 2
PI-V plus Sliding Mode Based Cascade Control of Magnetic Levitation
(Institute of Electrical and Electronics Engineers Inc., 2016) Eroǧlu, Yakup; Ablay, Günyaz
Magnetic levitation systems are able to provide frictionless, reliable, fast and economical operations in wide-range applications. The effectiveness and applicability of these systems require precise feedback control design. The position control problem of the magnetic levitation can be solved with robust current control approaches. A cascade control approach consisting of PI-velocity plus sliding mode control (PI-V plus SMC) is designed to render high control performance and robustness to the magnetic levitation. It will be shown that the SMC designed for electrical part of the plant (current controller) is able to eliminate the effects of the inductance related uncertainties of the electromagnetic coil of the plant. Experimental results are provided to validate the efficacy of the approach. © 2016 Elsevier B.V., All rights reserved.
Isothiocyanates as Drug Candidates in Cancer Prevention and Treatment
(IGI Global, 2023) Saylan, Demet; Cebeci, Fatma
Cancer is the second most common cause of death worldwide. Many methods such as surgery, chemotherapy, radiation therapy, etc. are being used for treatment. Most patients have a combination treatment with chemotherapy along with surgery or radiotherapy or both. Chemotherapy kills cancer cells by preventing them from reproducing, growing, and spreading in the body. Recently, safer alternatives to chemotherapy have been discovered and developed, as most of the drugs used in cancer treatment have side effects and a serious impact on patient comfort. As an alternative, the phytochemicals found in daily consumed plants are attractive candidates for clinical/pre-clinical evaluation because of their higher safety. In this context, certain degradation products of glucosinolates (isothiocyanates) are promising agents for cancer prevention and treatment. © 2023 Elsevier B.V., All rights reserved.
Citation - WoS: 5
Citation - Scopus: 4
Single-Country Versus Multiple-Country Studies
(Academic Press Ltd-Elsevier Science Ltd, 2019) Aslan, Alper; Dogan, Eyup; Altinoz, Buket
Citation - Scopus: 10
Sağlıkta Blokzincir Tabanlı Sistem Bilişimi Uygulamaları
(Institute of Electrical and Electronics Engineers Inc., 2020) Dedeturk, Beyhan Adanur; Bakir-Güngör, Burcu; Soran, Ahmet
Recently, the use of blockchain technology in the field of healthcare has increased. Although blockchain technology brought several innovations to healthcare, still there are problems waiting to be resolved. In order to provide alternative solutions to these problems, the use of fog computing together with blockchain technology has been proposed. In this study, the applications of blockchain based fog computing technology in healthcare are investigated. The aim of this study is to provide the readers an idea about the interactive use of blockchain and fog computing in the field of healthcare. For this purpose, firstly, fog computing and blockchain technologies are introduced. Afterwards, the integration of these areas, the advantages and disadvantages of using these technologies in the field of healthcare is discussed and a new system architecture is proposed. © 2021 Elsevier B.V., All rights reserved.
Email Clustering & Generating Email Templates Based on Their Topics
(Assoc Computing Machinery, 2021) Coskun, Fatih; Gezer, Cengiz; Gungor, V. Cagri
Email templates have a significant impact on users in terms of productivity. Using an email template that is produced successfully is going to transfer the main information with a considerable impression. While the previous studies were focused on the email generation by text-differences in the content of the emails, generated templates based on email topics can provide better productivity for the companies. This article proposes a system, in which user emails are clustered according to the topics of the emails, and introduces an email template generation system that utilizes the sample emails belonging to the formed email clusters. For this purpose, the Enron email dataset has been used and the performance of different text preprocessing and topic modeling algorithms, such as DMM, GPU-DMM, GPU-PDMM, LF-DMM, LDA, LF-LDA, BTM, WNTM, PTM, SATM, have been investigated and compared to determine the most efficient one. After obtaining the email topics, the system shows the examples of the emails representing the selected topics and enables the authorized users to create templates that generalize these topics.

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