Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
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Publication Metadata only Low-Powered Agriculture IoT Systems with LoRa(Institute of Electrical and Electronics Engineers Inc., 2020) Kokten, Esma; Çalişkan, Bahadir Can; Karamzadeh, Saeid; Gelal, Ece; Aboltins, A.; Litvinenko, A.; Kokten, Esma, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Çalişkan, Bahadir Can, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karamzadeh, Saeid, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyMonitoring is key to increase the efficiency of food storage in the open field in terms of cost, logistics and quality of the crops. For the long range data transmission in such environments, mobile technologies are not suitable, as the end devices are generally battery-limited. In this work, a prototype has been developed for monitoring goods in storage. The battery life time of this prototype is analysed in terms of calculations as well as measurements, on LoRa technology. Our results show that (i) while sleeping current has the smallest percentage, it has the greatest impact in increasing battery life, (ii) monitoring node shall have low self discharge battery for long battery life, and (iii) sensors are the main power sink that deplete battery. © 2020 Elsevier B.V., All rights reserved.Publication Metadata only Faster Wi-Fi Fingerprinting Using Feature Selection(Institute of Electrical and Electronics Engineers Inc., 2020) Aydin, Hurkan M.; Ali, Muhammad Ammar; Gelal, Ece; Aydin, Hurkan M., Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ali, Muhammad Ammar, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyWi-Fi fingerprinting has been widely used for indoor positioning, as Wi-Fi technology is easily deployed and supported. In fingerprinting, a database is created using the received signal strength indicator (RSSI) values in the area of interest, position prediction is performed by finding the best match for a measured RSSI among the values in the database. As location positioning gains importance for continuous interactive (CI) applications in large indoor spaces such as malls and airports, the fingerprinting databases become larger, making it computationally more difficult to position targets in real-time. On the other hand, CI applications such as Augmented Reality (AR) require low-latency positioning for a good user experience. In this work, we propose to use feature selection methods along with the K-nearest neighbors (KNN) classification and regression algorithms in order to create a simple and swift location positioning system. Our evaluation of various feature selection methods shows that computation times for positioning can be reduced by 75% using feature selection. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only The analysis of feature selection with machine learning for indoor positioning(Institute of Electrical and Electronics Engineers Inc., 2021) Aydin, Hurkan M.; Ali, Muhammad Ammar; Gelal, Ece; Aydin, Hurkan M., Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ali, Muhammad Ammar, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyIndoor positioning is useful in various venues including warehouses, convention centers, malls, airports, nursing homes. In these scenarios, reducing the complexity of location estimation both improves responsiveness and helps to elongate battery life of the mobile device. In this work, we carry out a detailed analysis of the impact of Principal Component Analysis (PCA) on the computational complexity and accuracy with different machine learning algorithms on a large data set containing 520 APs. We compare the algorithms' training and testing times, as well as their accuracies in the presence and absence of PCA. Our results show that (i) PCA significantly reduces both the training and testing times for classification and regression using k-nearest neighbor (kNN) and support vector machine (SVM) algorithms while preserving if not improving accuracy, (ii) PCA slightly improves the training/testing times for regression using multi-layer perceptron (MLP), (iii) random forest (RF) does not perform well with PCA. © 2021 Elsevier B.V., All rights reserved.Publication Open Access SCORING: Towards Smart Collaborative cOmputing, caching and netwoRking paradIgm for Next Generation communication infrastructures(Institute of Electrical and Electronics Engineers Inc., 2022) Hmitti, Zakaria Ait; Ben-Ammar, Hamza Haj; Gelal, Ece; Kardjadja, Youcef; Malektaji, Sepideh; Ali, Soukaina Ouledsidi; Rayani, Marsa; Saqib, Muhammad; Taghizadeh, Seyedreza R.; Ajib, Wessam; Hmitti, Zakaria Ait, Université du Québec à Montréal, Montreal, Canada; Ben-Ammar, Hamza Haj, La Rochelle Université, La Rochelle, France; Gelal, Ece, Sabancı Üniversitesi, Tuzla, Turkey, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kardjadja, Youcef, La Rochelle Université, La Rochelle, France; Malektaji, Sepideh, Concordia University, Montreal, Canada; Ali, Soukaina Ouledsidi, Université du Québec à Montréal, Montreal, Canada; Rayani, Marsa, Concordia University, Montreal, Canada; Saqib, Muhammad, Université du Québec à Montréal, Montreal, Canada; Taghizadeh, Seyedreza R., Université du Québec à Montréal, Montreal, Canada; Ajib, Wessam, Université du Québec à Montréal, Montreal, CanadaThe 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. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Fault Estimation for Operational Systems(Institute of Electrical and Electronics Engineers Inc., 2022) Ozkent, Tuncberk; Gelal, Ece; Ozkent, Tuncberk, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyOperational systems are crucial for corporations. A majority of the business processes flow through these systems and even minor downtimes on these systems may cause serious financial consequences. System logs are a promising way for analyzing the behaviors of operational systems. This work investigates fault estimation on a real-life data set derived from the system logs of a large-scale insurance company. Data set consists of operational system indicators like visit duration, connection properties and time of connection collected over four months. Regression and classification algorithms have been used to estimate the impact of the system and environmental parameters on the system response time. The best performance is obtained with the CatBoost classification, which yields 99% accuracy in estimating whether system responds within normal interval. This study assists the operational team in identifying problem scenarios, future improvements may be possible as logs from other operational systems from the company are considered using transfer learning. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only QoE Estimation for the Wi-Fi Edge with Gradient Boosting-based Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2023) Argın, Berke; Demir, Mehmet Özgün; Onalan, Aysun Gurur; Salik, Elif DIlek; Gelal, Ece; Argın, Berke, Lifemote Networks, Istanbul, Turkey; Demir, Mehmet Özgün, Lifemote Networks, Istanbul, Turkey; Onalan, Aysun Gurur, Lifemote Networks, Istanbul, Turkey; Salik, Elif DIlek, Lifemote Networks, Istanbul, Turkey; Gelal, Ece, Bahçeşehir Üniversitesi, Istanbul, TurkeyAn integral part of the Intent-Based Networking paradigm is estimating and improving the end-user quality of experience (QoE). Estimating user experience from the (wide-area) network data alone does not accurately represent the performance at customer premises since Wi-Fi at the edge also significantly affects the perceived QoE. We propose machine learning-based estimation of the end-users' perceived QoE for web browsing and video streaming applications, based on Wi-Fi statistics. We implement support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), XGBoost, and CatBoost algorithms and compare their performance. To the best of our knowledge, our CatBoost-based model yields the highest accuracy to date, 0.92 R2, in estimating the QoE for web browsing based on Wi-Fi statistics. Our experiments also show that the XGBoost-based QoE estimator outperformed the neural network-based model in estimating the QoE for video streaming. Our work demonstrates that network operators can infer the user-perceived QoE in a Wi-Fi network through telemetry data obtained by passive measurements. © 2023 Elsevier B.V., All rights reserved.Publication Metadata only Unmasking Twitter Bots: Feature Engineering and Machine Learning for Bot Account Identification(Institute of Electrical and Electronics Engineers Inc., 2023) Babur, Zeynep; Bekdemir, Umut; Sen, Acelyanur; Carkit, Sevval Ozlem; Genc, Oguzhan; Gülcü, Ayla; Gümüştaş, Cihangir; Gelal, Ece; Babur, Zeynep, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Bekdemir, Umut, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Sen, Acelyanur, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Carkit, Sevval Ozlem, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Genc, Oguzhan, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gülcü, Ayla, Department of Software Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gümüştaş, Cihangir, Department of Engineering Management, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyBot accounts on X (formerly known Twitter)1 are a growing issue that limits and negatively impacts the browsing and sharing experience of Twitter users, and it is important to identify such accounts. In this work, we perform machine learning-based estimation of bot accounts on Twitter. Using Twitter's API, a data set is collected containing tweets and related metadata from various accounts. Feature engineering techniques are then applied to highlight relevant features such as sentiment analysis of the account's tweets, or the account's friend/follower ratio. Using these features to train and evaluate machine learning models, the likelihood of a given account being a bot is estimated. The performances of three different models are comparatively analyzed based on their fl score, accuracy, precision, and recall. Analysis of feature importances shows the success of derived features in identifying bot accounts. This work demonstrates the potential of using feature engineering with tweet and profile properties to detect bot accounts on Twitter, and provides a foundation for further research on this topic.1In October 2022, Twitter was acquired, after which Twitter Inc. ceased to exist as an independent company and was merged with X Corp. To prevent possible inconsistencies and confusion in literature search results, we used the name Twitter throughout the text. © 2023 Elsevier B.V., All rights reserved.Publication Metadata only Accelerating Smart Contract Vulnerability Scan Using Transformers(Institute of Electrical and Electronics Engineers Inc., 2023) Balci, Emre; Yilmaz, Görkem; Uzunoǧlu, Anil; Gelal, Ece; Balci, Emre, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Yilmaz, Görkem, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Uzunoǧlu, Anil, Department of Information Systems Engineering, Sakarya Üniversitesi, Serdivan, Turkey; Gelal, Ece, Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyEthereum technology has brought upon the smart contract concept, enabling multiple independent parties to engage in transactions without the need for an external trusted authority. While this distributed network operates correctly and autonomously, smart contracts may expose security vulnerabilities, which may be exploited by malicious actors to illegitimately transfer funds. Furthermore, once a smart contract is created, it cannot be changed due to the immutability of the blockchain structure. Hence, it is critical to detect smart contract vulnerabilities before they are deployed. In this work, we propose VASCOT, a Vulnerability Analyzer for Smart COntracts using Transformers, to automatically perform sequential analysis on the EVM bytecode of smart contracts, to detect trace vulnerabilities. We construct a data set comprising Ethereum smart contracts verified in 2022, our evaluation of VASCOT on this data set demonstrates improvement in accuracy and significant reduction in time cost compared to the previously proposed sequential vulnerability scanners. To the best of our knowledge, this work constitutes the first use of transformers for smart contract security. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only Advancing WebRTC QoE Assessment with Machine Learning in Real-World Wi-Fi Scenarios(Institute of Electrical and Electronics Engineers Inc., 2024) Argın, Berke; Demir, Mehmet Özgün; Salik, Elif DIlek; Onalan, Aysun Gurur; Batum, Öykü Han; Gelal, Ece; Argın, Berke, Lifemote Networks, Istanbul, Turkey; Demir, Mehmet Özgün, Lifemote Networks, Istanbul, Turkey; Salik, Elif DIlek, Lifemote Networks, Istanbul, Turkey; Onalan, Aysun Gurur, Lifemote Networks, Istanbul, Turkey; Batum, Öykü Han, Lifemote Networks, Istanbul, Turkey; Gelal, Ece, Bahçeşehir Üniversitesi, Istanbul, TurkeyVideo conferencing applications play a key role in enabling use cases like remote working, education, and potentially the metaverse. From the perspective of Internet service providers, predicting the end user’s Quality of Experience (QoE) in such applications is critical in allocating the right resources to ensure consistently high QoE. This work addresses the estimation of user QoE from link-layer performance metrics such as transferred packets, queue size, signal strength, and channel occupancy for WebRTC-supported applications. Our study entails collecting a data set capturing various Wi-Fi scenarios in practical environments and training machine learning models on this data to estimate the perceived QoE. Our findings demonstrate improvement in prediction accuracy compared to earlier models and QoE representations, furthermore, we also investigate the explainability of the models with the help of SHAP values. © 2024 Elsevier B.V., All rights reserved.Publication Metadata only Neural Network-Based Human Detection Using Raw UWB Radar Data(Institute of Electrical and Electronics Engineers Inc., 2024) Dogan, Emine Berjin; Yousefi, Mohammad; Gelal, Ece; Karamzadeh, Saeid; Kolosovs, D.; Dogan, Emine Berjin, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Yousefi, Mohammad, Department of Artificial Intelligence Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karamzadeh, Saeid, Silicon Austria Labs GmbH, Graz, Austria, Bahçeşehir Üniversitesi, Istanbul, TurkeyUltra-Wideband (UWB) radar technology is a widely used technology for human detection and tracking through walls, because of its effectiveness in low-visibility situations. This study demonstrates a neural network-based identification of human presence using raw data obtained directly from the UWB radar. First, measurements have been collected with different human subjects at different positions relative to the UWB radar. A convolutional neural network (CNN) model has been trained on this dataset, to detect the presence of a human. Next, the algorithm effectiveness is deeply investigated using the Gradient-weighted Class Activation Mapping (Grad-CAM) method, and the observations on detected presence are discussed. © 2024 Elsevier B.V., All rights reserved.