Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
12 results
Search Results
Conference Object Citation - Scopus: 2Combining Classifiers for Protein Secondary Structure Prediction(Institute of Electrical and Electronics Engineers Inc., 2017-09) Aydin, Zafer; Uzut, Ömmu GülsümArticle Citation - WoS: 40Citation - Scopus: 49Spatio-Temporal Rich Model-Based Video Steganalysis on Cross Sections of Motion Vector Planes(Institute of Electrical and Electronics Engineers Inc., 2016-07) Taşdemir, K.; Kurugöllü, F.; Sakir Sezer, S.; Sezer, SakirA rich model-based motion vector (MV) steganalysis benefiting from both temporal and spatial correlations of MVs is proposed in this paper. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this paper. First, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring MVs for longer distances. Therefore, temporal MV dependency alongside the spatial dependency is utilized for rigorous MV steganalysis. Second, unlike the filters previously used, which were heuristically designed against a specific MV steganography, a diverse set of many filters, which can capture aberrations introduced by various MV steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in the previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-Temporal range and provides a better cover model. The proposed method is tested against the most prominent MV steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in MV steganalysis field, including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads. © 2016 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Opportunities and Challenges in Designing a Blended International Student Project Activity: Experiences from the Epic Project(Institute of Electrical and Electronics Engineers Inc., 2018-04) Kuran, Mehmet Şükrü; Pedersen, Jens Myrup; van Hattum-Janssen, Natascha; Sole-Pareta, Josep; Pareta, Josep SoleIn this paper we explain our experiences and observations on a blended international teaching/training student project activity designed for students of different academic levels and programs at different universities working together on a project given by an industrial partner. This project activity is designed based on the EPIC project, funded by the Erasmus+ programme of the European Commission, which aims to provide a framework for carrying out multi-cultural and multidisciplinary student projects for increasing employability in an international job market. © 2018 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 1Citation - Scopus: 1Microgrid Environmental Impact(Institute of Electrical and Electronics Engineers Inc., 2020-09-28) Al-Agtash, Salem Y.; al-Hashem, Mohammad; Batarseh, Mohanad; Bintoudi, Angelina D.; Tsolakis, Apostolos Charalampos; Tzovaras, Dimitrios K.; Hadjidemetriou, Lenos; Khiat, MounirPower plants have bad impacts on the environment. One of these impacts is Carbon Dioxide (CO2) emission resulted from power plants that depend on fossil fuel, oil and natural gas. Renewable energy is considered as an important solution for this problem since it is classified as clean and environmentally friendly source of energy and helps reducing the dependency on conventional power plants. High renewable energy penetration into power systems is a big challenge that can be solved by deploying the concept of smart Micro-Grids. This paper presents a study on how much reduction of CO2 emission can be resulted from deploying smart micro-grid concept on a university campus, German Jordanian University (GJU) campus was taken as a pilot. The micro-grid is meant to operate according to an optimum resource scheduling framework that guarantee a minimum operational cost while achieving high local power availability. © 2020 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Many-to Transfer Learning on Motor Imagery BCI(Institute of Electrical and Electronics Engineers Inc., 2024-12-11) Altindis, Fatih; Yilmaz, Bulent; Congedo, MarcoThis paper presents many-to-many domain adaptation strategy, named group learning, for motor imagery brain-computer interfaces (BCIs). Group learning, grounded in Riemannian geometry, simultaneously aligns multiple domains in a unified model, whereas fast alignment approach integrates new, unseen domains without re-estimating alignment matrices for all domains. Group learning creates a single machine learning model using data from previous subjects and/or sessions. Fast alignment utilizes the already trained model for an unseen domain without requiring any additional classifier training. The tests on five publicly available motor imagery databases demonstrate the robustness of group learning against negative learning. The classification accuracy scores of binary and multiclass databases show comparable, if not superior, performance to conventional subject-wise learning method. © 2025 Elsevier B.V., All rights reserved.Conference Object Machine Learning Based Beamwidth Adaptation for mmWave Vehicular Communications(Institute of Electrical and Electronics Engineers Inc., 2023-12-10) Manic, Setinder; Heng Foh, Chuan; Köse, Abdulkadir; Lee, Haeyoung; Leow, Chee Yen; Chatzimisios, Periklis; Suthaputchakun, Chakkaphong; Foh, Chuan HengThe incorporation of mmWave technology in vehicular networks has unlocked a realm of possibilities, propelling the advancement of autonomous vehicles, enhancing interconnectedness, and facilitating communication for intelligent transportation systems (ITS). Despite these strides in connectivity, challenges such as high path-loss have arisen, impacting existing beam management procedures. This work aims to address this issue by improving beam management techniques, specifically focusing on enhancing the service time between vehicles and base stations through adaptive mmWave beamwidth adjustments, accomplished using a Contextual Multi-Armed Bandit Algorithm. By leveraging various conditions to train the ML agent of the Contextual Multi-Armed Bandit Algorithm, it seeks to learn about vehicle mobility profiles and optimize the usage of different antenna beamwidth settings to maximize seamless connection time. The extensive simulation results showcase the effectiveness of an adaptive beamwidth for mobility profiles, extending the connection time a vehicle experiences with a base station when compared to the existing strategies. © 2024 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 8MOL-Eye: A New Metric for the Performance Evaluation of a Molecular Signal(Institute of Electrical and Electronics Engineers Inc., 2018-04) Turan, Meric; Kuran, Mehmet Şükrü; Yilmaz, Huseyin Birkan; Chae, Chan Byoung; Tuǧcu, TunaInspired by the eye diagram in classical radio frequency (RF) based communications, the MOL-Eye diagram is proposed for the performance evaluation of a molecular signal within the context of molecular communication. Utilizing various features of this diagram, three new metrics for the performance evaluation of a molecular signal, namely the maximum eye height, standard deviation of received molecules, and counting SNR (CSNR) are introduced. The applicability of these performance metrics in this domain is verified by comparing the performance of binary concentration shift keying (BCSK) and BCSK with consecutive power adjustment (BCSK-CPA) modulation techniques in a vessel-like environment with laminar flow. The results show that, in addition to classical performance metrics such as biterror rate and channel capacity, these performance metrics can also be used to show the advantage of an efficient modulation technique over a simpler one. © 2018 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 2History-Themed Games in History Education: Experiences on a Blended World History Course(Institute of Electrical and Electronics Engineers Inc., 2018-04) Kuran, Mehmet Şükrü; Tozoğlu, Ahmet Erdem; Tavernari, CinziaIn this paper we explain our experiences and observations on a blended world history course which combines classical lecture and discussion elements as well as video game sessions in which the students play strategy video games with heavy historical focus. The course, named Playing with The Past, is designed to experiment on how to integrate video games on teaching history especially in order to achieve a higher understanding of the contemporary social, political, economical, and technological context of a given era for a given nation. We ran the course four times between 2015-2018 with different video game titles having different historical models and observe the experiences and learning of students based on the quality of their written essays and articles. Our experiments and observations could be beneficial not only for the design of a general world history course, but also for a history course on specific periods, cultures, and nations. © 2018 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 17Citation - Scopus: 37Channel Model of Molecular Communication Via Diffusion in a Vessel-Like Environment Considering a Partially Covering Receiver(Institute of Electrical and Electronics Engineers Inc., 2018-06) Turan, Meric; Kuran, Mehmet Şükrü; Yilmaz, Huseyin Birkan; Demirkol, Ilker; Tuǧcu, Tuna; Birkan Yilmaz, H.By considering potential health problems that a fully covering receiver may cause in vessel-like environments, the implementation of a partially covering receiver is needed. To this end, distribution of hitting location of messenger molecules (MM) is analyzed within the context of molecular communication via diffusion with the aim of channel modeling. The distribution of these MMs for a fully covering receiver is analyzed in two parts: angular and radial dimensions. For the angular distribution analysis, the receiver is divided into 180 slices to analyze the mean, standard deviation, and coefficient of variation of these slices. For the axial distance distribution analysis, Kolmogorov-Smirnov test is applied for different significance levels. Also, two different implementations of the reflection from the vessel surface (i.e., rollback and elastic reflection) are compared and mathematical representation of elastic reflection is given. The results show that MMs have tendency to spread uniformly beyond a certain ratio of the distance to the vessel radius. By utilizing the uniformity, we propose a channel model for the partially covering receiver in vessel-like environments and validate the proposed model by simulations. © 2018 Elsevier B.V., All rights reserved.Article Citation - Scopus: 98An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo-Tompa and Stacked Genetic Algorithm(Institute of Electrical and Electronics Engineers Inc., 2020) Ali, Syed Arslan; Raza, Basit; Malik, Ahmad Kamran Kamran; Shahid, Ahmad Raza; Faheem, Muhammed Yasir; Alquhayz, Hani Ali; Kumar, Y. J.A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew's correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%. © 2020 Elsevier B.V., All rights reserved.