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Publication Metadata only SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(IEEE, 2022) Hmitti, Zakaria Ait; Ben Ammar, Hamza; Soyak, Ece Gelal; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza; Ajib, Wessam; Elbiaze, Halima; Ercetin, Ozgur; Ghamri-Doudane, Yacine; Glitho, Roch; University of Quebec; University of Quebec Montreal; Sabanci University; Concordia University - Canada; Bahcesehir UniversityThe unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.Publication Metadata only Throughput-maximizing OFDMA Scheduler for IEEE 802.11ax Networks(IEEE, 2020) Kuran, Mehmet Sukru; Dilmac, A.; Topal, Omer; Yamansavascilar, Baris; Avallone, Stefano; Tugcu, Tuna; Bahcesehir University; Bogazici University; University of Naples Federico IIIn this paper, we develop a novel throughput-maximizing OFDMA scheduler for the multi-user MAC framework for the IEEE 802.11ax networks. The scheduler works both in the downlink and uplink directions and assigns resource units to stations using a linear programming technique considering load of each client, possible resource unit configurations, modulation-coding scheme of each client, and ageing factor of each client's load. The performance of the proposed scheduler has been evaluated using the NS3 simulator and compared against the legacy MAC layer mechanism of IEEE 802.11 protocol (i.e., DCF/EDCA). Simulation results show that our proposed throughput-maximizing scheduler increases the total throughput in the network as well as decrease the average end-to-end delay regardless of the number of stations connected to the access point by prioritizing the traffic of clients connected via high modulation-coding schemes.Publication Metadata only Performance Comparison of Oral, Laryngeal and Thoracic Sounds in the Detection of COVID-19 by Employing Machine Learning Techniques(IEEE, 2022) Gozuacik, Necip; Serbes, Gorkem; Kara, Eyup; Atar, Eren; Sakar, C. Okan; Yener, H. Murat; Borekci, Sermin; Korkmazer, Bora; Karaali, Ridvan; Kara, Halide; Gulmez, Zuleyha; Cogen, Talha; Atas, Ahmet; Bahcesehir University; Yildiz Technical University; Istanbul University; Istanbul University - CerrahpasaCOVID-19 can directly or indirectly cause lung involvements by crossing the upper airways. It is essential to quickly detect the lung involvement condition and to follow up and treat these patients by early hospitalization. In recent COVID-19 diagnosis procedure, PCR testing is applied to the samples taken from the patients and a quarantine period is applied to the patient until the test results are received. As a complement to PCR tests and for faster diagnosis, thin-section lung computed tomography (CT) imaging is used in COVID-19 patients. In this study, it is aimed to develop a method that is as reliable as CT, and compared to CT, less risky, more accessible, and less costly for the diagnosis of COVID-19 disease. For this purpose, first speech and cough sounds from the oral, laryngeal and thoracic regions of COVID-19 patients and healthy individuals were obtained with the multi-channel voice recording system we proposed, the obtained data were processed with machine learning methods and their accuracies in COVID-19 diagnosis were presented comparatively. In our study, the best results were obtained with the features extracted from the cough sounds taken from the oral region.Publication Metadata only Assessing Automotive Bumper Materials: A Comparative Study on Attenuation at 77GHz(IEEE, 2024) Neubauer, Michael; Petanjek, David; Hirschmugl, Michael; Kiebach, Helge; Karamzadeh, Saeid; Bahcesehir UniversityHaving information about the materials surrounding the radar sensors is essential in the automotive sector, in order to guarantee good functionality of the sensor. Since automotive manufacturers also start integrating radar sensors behind bumpers, it is essential to know the characteristics of the materials used in bumpers and how a repair of a bumper would alter those characteristics. This paper shows, how the damping characteristics of different samples were measured at 77GHz using a scalar measurement setup and compares the attenuation for different types of paints and modifications.Publication Metadata only Design and analysis of a label-free water-soluble glucose sensor based on suspended hybrid plasmonic micro-ring resonator(IEEE, 2022) Moeinimaleki, Kaveh; Moeinimaleki, Babak; Zarean, Rana; Abedashtiani, Alireza; Karamzadeh, Saeid; Akdeniz University; Bahcesehir UniversityIn this paper, the design and analysis of a label-free water-soluble glucose sensor based on a suspended hybrid plasmonic micro-ring resonator with an external radius of 940 nm in the third telecommunication window to sense the concentration of water-soluble glucose is presented. The simulation results obtained using three-dimensional FDTD show that the sensitivity and the figure of merit values obtained for glucose measurements with concentrations of 0 to 50% in water solution are 218.5 (nm/RIU) and 16.11 (1/RIU), respectively. Comparing the results of the proposed sensor with the previous work, which has four times more footprint, shows that the structure's sensitivity has increased by 17 percent.Publication Metadata only Electricity Price Prediction Using Encoder-Decoder Recurrent Neural Networks in Turkish Dayahead Market(IEEE, 2020) Gunduz, Salih; Ugurlu, Umut; Oksuz, Ilkay; Ministry of National Education - Turkey; Bahcesehir University; Istanbul Technical UniversityForecasting electricity prices accurately is an essential requirement for the players in the market. The sequential data of electricity poses many challenges such as seasonality and high volatility. Recently, recurrent neural network based methods have showed great success in forecasting time series type problems. In this paper, we propose to use an encoder-decoder recurrent neural network for forecasting hourly electricity prices in the Turkish Day-ahead Market. The approach involves two recurrent neural networks, one to encode the source sequence, called the encoder, and a second to decode the encoded source sequence into the target sequence, called the decoder. We trained and tested our framework on the Turkish electricity price data from 2013 to 2016 and report the accuracy of various recurrent neural network models in terms of mean absolute error. The encoder-decoder recurrent neural networks achieved better accuracy compared to classical recurrent neural networks.Publication Metadata only Multivariate and Multi-step Traffic Prediction for NextG Networks with SLA Violation Constraints(IEEE, 2023) Tuna, Evren; Soysal, Alkan; Bahcesehir University; Virginia Polytechnic Institute & State UniversityThis paper focuses on predicting downlink (DL) traffic volume in mobile networks while minimizing overprovisioning and meeting a given service-level agreement (SLA) violation rate. We present a multivariate, multi-step, and SLA-driven approach that incorporates 20 different radio access network (RAN) features, a custom feature set based on peak traffic hours, and handover-based clustering to leverage the spatiotemporal effects. In addition, we propose a custom loss function that ensures the SLA violation rate constraint is satisfied while minimizing overprovisioning. We also perform multi-step prediction up to 24 steps ahead and evaluate performance under both single-step and multi-step prediction conditions. Our study makes several contributions, including the analysis of RAN features, the custom feature set design, a custom loss function, and a parametric method to satisfy SLA constraints.Publication Metadata only Natural Language Processing-Based Product Category Classification Model for E-Commerce(IEEE, 2022) Koksal, Deniz; Alacan, M. Mert; Olgun, Ecesu; Sakar, C. Okan; Bahcesehir UniversityIn scope of the great dynamism that the world of e-commerce gained with the post-pandemic changes on customer behavior, extending the customer's time on site has become much more valuable. With regards to creating a better understanding of the online shopper's intent on site, an effective search engine is the best tool for improving the user experience. A useful search engine needs to produce fast results and do intent analysis of customers correctly to present successful recommendations. In this study, BERT, ELECTRA and RoBERTa which are language models that give successful results in similar problems and pre-training libraries in Turkish languages are used for developing text classification and customer intent analysis models based on e-commerce product categories. The results of these deep learning models, which were optimized using the product description and comment libraries of the selected e-commerce brand, are shared in a comparative way, and the results of our e-commerce end-user intent analysis model, which was tested on user search history, were detailed on the basis of product category. The developed model can be used for e-commerce product prioritization in Turkish language, as well as creating an infrastructure for the development of intent analysis models that are able to predict as far as the main product that the user wants to display in searches containing multiple products.Publication Metadata only Consumer Preference Estimation Using EEG Signals and Deep Learning(IEEE, 2024) Ceylan, Burak; Cekic, Yalcin; Akan, Aydin; Bahcesehir University; Izmir Ekonomi UniversitesiEmotion estimation is an extremely critical and current research topic for human-computer interaction. In this study, a liking estimation method using electroencephalogram (EEG) signals is proposed to be used in neuromarketing studies. EEG data recorded while participants watch the advertisement videos of two different automobile brands are processed with deep learning techniques to estimate their liking status. After watching the videos, participants were presented with selected image sections from the advertisements (front view, console, side view, rear view, stop lamp, brand logo and front grille) and were asked to rate their liking by scoring from 1 to 5. EEG signals corresponding to these regions were converted into a two dimensional and RGB colored image using the short-time Fourier transform (STFT) method, and liking status was estimated using Deep Learning. The successful results obtained show that the proposed method can be used in neuromarketing studies.Publication Metadata only Creation of Annotated Synthetic UAV Video Dataset for Object Detection and Tracking(IEEE, 2023) Yilmaz, Can; Maras, Bahri; Arica, Nafiz; Ertuzun, Aysin Baytan; Bahcesehir University; Bogazici University; Piri Reis UniversityIn order for object detection and tracking in videos obtained from unmanned aerial vehicles (UAVs) by deep convolutional neural networks (DCNN), extensive ground truth optical flow, occlusion and segmentation datasets, of various objects or vehicles, are required during the training and testing processes. The mentioned ground truth informations are not widely available in the literature due to the difficulty of labeling or extracting them from real-life recorded UAV video images. In this study, ground truth optical flow, occlusion and segmentation datasets were produced synthetically for the first time with the UAV point of view in a novel way, so as to fill the gap in literature. The ground truth datasets were created for each vehicle by subjecting the triangles (mesh) automatically generated by the Unity engine to the homography method. With this method, 1920x1080 and 250x250 sized synthetic datasets consisting of 100 scenarios were obtained.