Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741
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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 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.