Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Machine Learning-Based Antenna Selection and Secrecy Capacity Analysis(Institute of Electrical and Electronics Engineers Inc., 2025) Erdurak, Burak; Erdoǧan, Eylem; Gürkan, FilizThe performance of machine learning methods was analyzed to optimize antenna selection in wireless communication systems, and system's secrecy performance was observed. To enhance the antenna selection process, Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and the KNearest Neighbors (KNN) algorithm were utilized. Channel vectors were used as model inputs, aiming to select the most optimal transmission path among N possible candidates. During the training phase, the antenna with the highest Signal-to-Noise Ratio (SNR) was selected for data labeling. The performance of Single-Input Multiple-Output (SIMO), Multiple-Input SingleOutput (MISO), and Multiple-Input Multiple-Output (MIMO) system architectures was evaluated using model accuracy and the F1-score. Additionally, the secrecy capacity corresponding to the selected antennas was computed, demonstrating the feasibility of secure communication. The results indicate that deep learningbased methods achieved higher accuracy, with the CNN model emerging as the most successful approach, reaching an accuracy of over 95% across all system configurations. © 2025 Elsevier B.V., All rights reserved.Review Citation - WoS: 3Citation - Scopus: 4Predictive Video Analytics in Online Courses: a Systematic Literature Review(SPRINGER, 2023) Yurum, Ozan Rasit; Taskaya-Temizel, Tuğba; Yildirim, SonerThe purpose of this study was to investigate the use of predictive video analytics in online courses in the literature. A systematic literature review was performed based on a hybrid search strategy that included both database searching and backward snowballing. In total, 77 related publications published between 2011 and April 2023 were identified. The findings revealed an increase in the number of publications on predictive video analytics since 2016. In the majority of studies, edX and Coursera platforms were used to collect learners' video interaction data. In addition, computer science was shown to be the top course domain, whilst data collection from a single course was found to be the most common. The results related to input measures showed that pause, play, backward, and forward were the top in-video interactions, whilst video transcript and subtitle were the least used. Learner performance and dropout were the primary output measures, whereas learning variables such as engagement, satisfaction, and motivation were investigated in only a few studies. Furthermore, most of the studies utilized data related to forums, navigation, and exams in addition to video data. The top algorithms used were Support Vector Machine, Random Forest, Logistic Regression, and Recurrent Neural Networks, with Random Forest and Recurrent Neural Networks being two rising algorithms in recent years. The top three evaluation metrics used were Accuracy, Area Under the Curve, and F1 Score. The findings of this study may be used to aid effective learning design and guide future research.Review Citation - WoS: 18Citation - Scopus: 18A Review on New Cobalt-Free Cathode Materials for Reversible Solid Oxide Fuel Cells(Chulalongkorn Univ, Metallurgy & Materials Science Research Inst, 2023) Akkurt, Sedat; Sındırac, Can; Özmen Egesoy, Tuğce; Ergen, EmreThe exponential growth in the requirement of fuel cells and batteries leads to increased demand for cobalt due to its common use in high-performance Li-ion batteries and high-temperature fuel cells/electrolyzers. This sharp increment in demand raises concern about the availability of limited reserves of cobalt which can impact the price of cobalt. Moreover, the geographic limitations of cobalt resources may endanger the whole supply chain. In addition to all those, huge moral issues of cobalt mining are also another problem. Hence, leading battery, fuel cells and electrolyzer manufacturers are looking for sustainable alternatives to reduce cobalt dependency. A more specific limitation is shown in Solid Oxide Fuel Cells (SOFCs) cathode materials that contain cobalt. Incompatibilities have already been observed between the cathode materials containing cobalt and the electrolytes in terms of the thermal expansion coefficient mismatch during the transition of the operating temperature from high to low. An advantage of low operating temperatures is the reduction of material costs compared to high temperature. Increasing the electrochemical performance of the cell and eliminating thermal expansion coefficient difference problems are in concert aimed at the development of cobalt-free cathode materials. Therefore, cobalt-free cathode materials are vital for the sustainability of SOFCs and green transition of the energy sector since they can be used as cathode and anode material in symmetrical SOFCs which is also known as reversible SOFC (RSOFC). In this review, we comprehensively summarize the recent advances of cobalt-free perovskite cathode materials for intermediate temperature RSOFCs.Article Citation - WoS: 45Citation - Scopus: 51Experimental Investigation of the Effect of Graphene/Water Nanofluid on the Heat Transfer of a Shell-And Heat Exchanger(Wiley-Hindawi, 2023) Zolfalizadeh, Mehrdad; Heris, Saeed Zeinali; Pourpasha, Hadi; Mohammadpourfard, Mousa; Meyer, Josua P. P.The most common type of heat exchanger used in a variety of industrial applications is the shell-and-tube heat exchanger (STHE). In this work, the impact of graphene nanoplate (GNP)/water nanofluids at 0.01 wt.%, 0.03 wt.%, and 0.06 wt.% on the thermal efficiency, thermal performance factor, pressure drop, overall heat transfer, convective heat transfer coefficient (CVHTC), and heat transfer characteristics of a shell-and-tube heat exchanger was examined. For these experiments, a new STHE was designed and built. The novelty of this study is the use of GNPs/water nanofluids in this new STHE for the first time and the fully experimental investigation of the attributes of nanofluids. GNP properties were analysed and confirmed using analyses including XRD and TEM. Zeta potential, DLS, and camera images were used to examine the stability of nanofluids at various periods. The zeta potential of the nanofluids was lower than -27.8 mV, confirming the good stability of GNP/water nanofluids. The results illustrated that the experimental data for distilled water had a reasonably good agreement with Sieder-Tate correlation. The maximum enhancement in the CVHTC of nanofluid with 0.06 wt.% of GNP, was equal to 910 (W/m(2)K), an increase of 22.47%. Also, the efficiency of the heat exchanger for nanofluid at 0.06 wt.% improved by 8.88% compared with that of the base fluid. The heat transfer rate of the nanofluid at maximum concentration and volume flow rate was 3915 (J/kg.K), an improvement of 15.65% over the base fluid. The pressure drops increased as the flow rate and concentration of the nanofluid increased. Although increasing the pressure drop in tubes would increase the CVHTC, it would also increase the power consumption of the pump. In conclusion, nanofluid at 0.06 wt.% had good performance.Article Citation - WoS: 15Citation - Scopus: 18Achieving Query Performance in the Cloud Via a Cost-Effective Data Replication Strategy(Springer, 2021) Tos, Uras; Mokadem, Riad; Hameurlain, Abdelkader; Ayav, TolgaMeeting performance expectations of tenants without sacrificing economic benefit is a tough challenge for cloud providers. We propose a data replication strategy to simultaneously satisfy both the performance and provider profit. Response time of database queries is estimated with the consideration of parallel execution. If the estimated response time is not acceptable, bottlenecks are identified in the query plan. Data replication is realized to resolve the bottlenecks. Data placement is heuristically performed in a way to satisfy query response times at a minimal cost for the provider. We demonstrate the validity of our strategy in a performance evaluation study.Article Citation - WoS: 24Citation - Scopus: 31Dynamic Replication Strategies in Data Grid Systems: A Survey(Springer Verlag, 2015) Tos, Uras; Mokadem, Riad; Hameurlain, Abdelkader; Ayav, Tolga; Bora, ŞebnemIn data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv) objective function based. Dynamic data replication is a form of data replication that is performed with respect to the changing conditions of the grid environment. In this paper, we present a survey of recent dynamic data replication strategies. We study and classify these strategies by taking the target data grid architecture as the sole classifier. We discuss the key points of the studied strategies and provide feature comparison of them according to important metrics. Furthermore, the impact of data grid architecture on dynamic replication performance is investigated in a simulation study. Finally, some important issues and open research problems in the area are pointed out.