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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 Characterizing Attenuation of Bumper Modifications: A Comparative Study in the E-Band(IEEE, 2024) Neubauer, Michael; Hirschmugl, Michael; Petanjek, David; Kiebach, Helge; Karamzadeh, Saeid; Kolosovs, D; Anstalt fur Verbrennungskraftmaschinen List; Bahcesehir UniversityHaving knowledge about the attenuation of the materials surrounding a radar sensor is of great importance in the automotive sector. Only then can the manufacturer guarantee the desired functionality of the sensor. Due to the fact that a lot of automotive manufacturers also started integrating radar sensors behind rear bumpers for improved visibility to the back of a car, it is vital to know how those bumpers and possible repair scenarios done to them can affect the radar sensor. This paper proposes a way to measure the attenuation in the E-band of not only square slab samples representing various types of paint and repair scenarios on automotive bumpers, but also actual rear bumpers with different types of repair scenarios. The results are then presented and compared relative to each other.Publication Metadata only Assistive Visual Tool: Enhancing Safe Navigation with Video Remapping in AR Headsets(SPRINGER INTERNATIONAL PUBLISHING AG, 2025) Sadeghzadeh, Arezoo; Islam, Md Baharul; Uddin, Md Nur; Aydin, Tarkan; DelBue, A; Canton, C; Pont-Tuset, J; Tommasi, T; Bahcesehir University; State University System of Florida; Florida Gulf Coast UniversityVisual Field Loss (VFL) is characterized by blind spots or scotomas that poses detrimental impact on fundamental movement activities of individuals. Addressing the challenges (e.g., low video quality, content loss, high levels of contradiction, and limited mobility assessment) faced by existing Extended Reality (XR) systems as vision aids, we introduce a groundbreaking method that enriches the real-time navigation using Augmented Reality (AR) glasses. Our novel vision aid employs advanced video processing techniques to enhance visual perception in individuals with moderate to severe VFL, bridging the gap to healthy vision. A unique optimal video remapping function, tailored to our selected AR glasses characteristics, dynamically maps live video content to the largest intact region of the Visual Field (VF) map. Our method preserves video quality, minimizing blurriness and distortion. Through a comprehensive empirical user study involving 29 subjects with artificially induced scotomas, statistical analyses of object counting and multi-tasking walking track tests demonstrate the promising performance of our method in enhancing visual awareness and navigation capability in real-time.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 SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes(IEEE, 2020) Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul; Daffodil International University; Khulna University of Engineering & Technology (KUET); Bahcesehir University; Daffodil International UniversitySkin Cancer is one of the most common types of cancer. A solution for this globally recognized health problem is much required. Machine Learning techniques have brought revolutionary changes in the field of biomedical researches. Previously, It took a significant amount of time and much effort in detecting skin cancers. In recent years, many works have been done with Deep Learning which made the process a lot faster and much more accurate. In this paper, We have proposed a novel Convolutional Neural Networks (CNN) based approach that can classify four different types of Skin Cancer. We have developed our model SkNet consisting of 19 convolution layers. In previous works, the highest accuracy gained on 1000 images was 80.52%. Our proposed model exceeded that previous performance and achieved an accuracy of 95.26% on a dataset of 4800 images which is the highest acquired accuracy.Publication Metadata only A Holistic Methodology for Digital Governance Transition and Measuring Its Effect on Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansDigital transformation has become one of the most emphasized issues for organizations today. Organizations have a long way to go in this difficult road. First of all, they should develop and measure their governance skills, and then they need to create the necessary studies in both technological and managerial terms for IT systems to become a value. While doing all these, they should also create a risk management structure with a correct approach by performing consultancy, supervision, and evaluation services completely. In line with this whole process, there are different standards and practices in every field they need to follow. This paper presents a methodology proposition in a general perspective that can be applied end-to-end in the field of digitalization, starting from the governance structure to the IT value-adding strategy. Thanks to this new methodology, it is possible to see many processes such as governance, consultancy, digital transformation, auditing, internal control, which are disconnected from each other, in the big picture and arrange all activities accordingly. With long-term follow-up, the strategic and tactical perspectives of institutions can be created and their progress over time can be followed by this new methodology. This paper performs both qualitative and quantitative measurements of the applications of the new methodology to compare them with existing ones. This methodology introduces a holistic methodology lacking in the market.Publication Metadata only A Research for Measuring the Effects of COVID-19 on Digital Transformation within Enterprise Companies(ASSOC COMPUTING MACHINERY, 2021) Ozturk, Ediz; Kocak, Taskin; Loukis, E; Macadar, MA; Nielsen, MM; Bahcesehir University; University of Louisiana System; University of New OrleansPandemics are not only medical phenomenon, but they also influence people and society in many respects. It has an effect on almost all markets all over the world. COVID-19 pandemic, expressed as change, empowerment, or post-traumatic growth, with several negative consequences as well as positive consequences. It also has the potential for opportunities. Years of change in the way companies do business have resulted from the COVID 19 crisis across all industries and regions. The aim of this research is to examine the relationship between different demographic variables, COVID-19's impact on digital transformation and post-traumatic effects. The article reflects in practical terms on whether and how the COVID-19 emergence in organizations accelerates digital transformation. This study is a descriptive quantitative approach research based on general survey model.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 Machine Vision-Based Expert System for Automated Skin Cancer Detection(SPRINGER INTERNATIONAL PUBLISHING AG, 2022) Junayed, Masum Shah; Jeny, Afsana Ahsan; Rada, Lavdie; Islam, Md Baharul; BritoLoeza, C; MartinGonzalez, A; CastanedaZeman, V; Safi, A; Bahcesehir University; Daffodil International UniversitySkin cancer is the most frequently occurring kind of cancer, accounting for about one-third of all cases. Automatic early detection without expert intervention for a visual inspection would be of great help for society. The image processing and machine learning methods have significantly contributed to medical and biomedical research, resulting in fast and exact inspection in different problems. One of such problems is accurate cancer detection and classification. In this study, we introduce an expert system based on image processing and machine learning for skin cancer detection and classification. The proposed approach consists of three significant steps: pre-processing, feature extraction, and classification. The pre-processing step uses the grayscale conversion, Gaussian filter, segmentation, and morphological operation to represent skin lesion images better. We employ two feature extractors, i.e., the ABCD scoring method (asymmetry, border, color, diameter) and gray level co-occurrence matrix (GLCM), to extract cancer-affected areas. Finally, five different machine learning classifiers such as logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) used to detect and classify skin cancer. Experimental results show that random forest exceeds all other classifiers achieving an accuracy of 97.62% and 0.97 Area Under Curve (AUC), which is state-of-the-art on the experimented open-source dataset PH2.Publication Metadata only Multivariate Spatio-temporal Cellular Traffic Prediction with Handover Based Clustering(IEEE, 2022) Tuna, Evren; Soysal, Alkan; Turkcell Turkey; Bahcesehir University; Virginia Polytechnic Institute & State UniversityWe consider an RNN-based traffic volume prediction, which is a critical problem for network slice management and resource allocation in slicing-enabled next generation cellular networks. We propose to use a novel cost function that takes SLA violations into account. Our approach is multivariate and spatio-temporal in three aspects. First, we consider the effects of several other RAN features in a cell besides the traffic volume. Second, we introduce feature vectors based on peak hours of the day and days of the week. Third, we introduce feature vectors based on incoming handover statistics from the neighboring cells. Our results show about 60% improvement over MAE-based univariate LSTM models and about 20% improvement over SLA-based univariate models.
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