Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
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Publication Metadata only Delivering Machine Learning Applications via Cloud Platforms: An Experience Report(Institute of Electrical and Electronics Engineers Inc., 2020) Sener, Yigit; Yetim, Hasan Fahri; Baǧriyanik, Selami; Akgun, B.T.; Ayav, T.; Bilgen, S.; Kardas, G.; Sener, Yigit, M.Sc. Program in Big Data Analytics and Management, Bahçeşehir Üniversitesi, Istanbul, Turkey; Yetim, Hasan Fahri, M.Sc. Program in Big Data Analytics and Management, Bahçeşehir Üniversitesi, Istanbul, Turkey; Baǧriyanik, Selami, Turkcell Teknoloji, Digital Services and Solutions Technology Department, Istanbul, TurkeyCloud technologies enable developers and organizations to focus on their product, without having to consider issues such as local server capacity, infrastructure modifications, data security, licensing or human capital. This paper attempts to explain a case in which a Machine Learning application is deployed via Amazon Web Services (AWS) tools. In doing so, it demonstrates the reasoning behind choosing a cloud-based environment instead of on-premise sources, by putting forward the advantages of the former. On the other hand, it should be noted that the application in this experience is generated with a hybrid approach: It is developed using on-premise infrastructure and then moved to the Cloud environment for the deployment phase only. In this regard, it can be read as a PaaS experience. This study is considered to be a beneficial guide for entrepreneurs and start-ups on a budget who aim at launching their products in a swift and scalable manner. © 2020 Elsevier B.V., All rights reserved.Publication Metadata only Automating Customer Claim Registration by Text Mining(Institute of Electrical and Electronics Engineers Inc., 2020) Beyranvand, Peyman; Aytekin, Tevfik; Beyranvand, Peyman, Research and Development Mayen CRM, Istanbul, Turkey; Aytekin, Tevfik, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this paper, we present the use of text mining and machine learning in call centers to increase the efficiency of registering customer claims and improving customer satisfaction. Our proposed method makes the process of claim registration faster and more accurate compared to experienced call center agents. Use of text mining and machine learning techniques will increase the customer satisfaction and endows the call center staff with better ways to help the customer. © 2020 Elsevier B.V., All rights reserved.Publication Metadata only A Touchless Control Interface for Low-Cost ROVs(Institute of Electrical and Electronics Engineers Inc., 2021) Kapicioglu, Kagan; Getmez, Enis; Akbulut, Batuhan Ekin; Akgul, Arda; Ucar, Burak; Kanlikilic, Berke; Koc, Mehmet; Gür, M. Berke; Kapicioglu, Kagan, Mechatronics Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Getmez, Enis, Software Development, Caretta Yazilim Ltd. Şti., Istanbul, Turkey; Akbulut, Batuhan Ekin, Mechatronics Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Akgul, Arda, Mechatronics Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ucar, Burak, Mechatronics Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kanlikilic, Berke, Mechatronics Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Koc, Mehmet, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gür, M. Berke, Mechatronics Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this paper, a fully touchless control interface for low cost remotely operated vehicles (ROVs) is presented. This interface aims to decrease training time, reduce workload, and ensure the operation ergonomics of ROV operators. Fully touchless control interface is achieved by a machine learning (ML) algorithm for ROV operator's face and orientation recognition, and controling the angle of an ROV-based camera. Furthermore, a Leap Motion sensor captures hand gestures and movements, thereby allowing the ROV operator to execute maneuvers and perform other functions (e.g., gripper or lighting control) based on pre-determined hand gestures. Fusion of face and hand gestures allows the operator to control ROV in a fully touchless way. The proposed system is tested in a realistic underwater simulation environment designed specifically for typical tasks that are present in student competitions. Trials with inexperienced operators show that the touchless interface can cut training times, speed up operations, reduce workload, and can provide the operator with a more natural feeling of command and control as well as better ergonomy. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Credit risk analysis using machine-learning algorithms, Makine öǧrenmesi algoritmalarini kullanarak kredi riski analizi(Institute of Electrical and Electronics Engineers Inc., 2021) Alagoz, Gokhan; Çanakoǧlu, Ethem; Alagoz, Gokhan, Kuveyt Türk Katilim Bankasi, Kocaeli, Turkey; Çanakoǧlu, Ethem, Bahçeşehir Üniversitesi, Istanbul, TurkeyCredit risk and default risk are very important concepts for all banks and financial institutions globally. As credit risk measurement and modeling requires working with large samples, it was preferred to use machine learning, one of the modern analysis techniques. In the study, Logistic Regression, Random Forest and Artificial Neural Networks, which are quite common among machine learning algorithms and have taken their place in research, were used for data analysis and modeling studies. 66,078 loan samples and 11 variables belonging to these samples have been used in the modeling, and research has been carried out on the estimation of the results of successful (0) or unsuccessful (1) credits using machine learning algorithms according to the effect of these variables. These records were taken anonymously from the test environment of a financial institution and the necessary data engineering and modeling studies were carried out using the Python programming language. As a result of the study, it was seen that the model established with Logistic Regression produces better results and is a more suitable method for the research subject. © 2021 Elsevier B.V., All rights reserved.Publication Metadata only Toward Automatic Water Pollution Analysis: A Machine Learning Approach for Water-Quality Monitoring Through Pattern Classification of Water Crystallization(Springer Science and Business Media Deutschland GmbH, 2022) Rada, Lavdie; Tanrıverdi, Yusuf Baran; Kara, Ömer Ekmel; Hemond, Elizabeth M.; Tezel, Ulaş; Brito-Loeza, C.; Martin-Gonzalez, A.; Castañeda-Zeman, V.; Safi, A.; Rada, Lavdie, Bahçeşehir Üniversitesi, Istanbul, Turkey; Tanrıverdi, Yusuf Baran, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kara, Ömer Ekmel, Bahçeşehir Üniversitesi, Istanbul, Turkey; Hemond, Elizabeth M., Bahçeşehir Üniversitesi, Istanbul, Turkey; Tezel, Ulaş, Boğaziçi Üniversitesi, Bebek, TurkeyHeavy metal contamination in drinking water and water resources is one of the problems generated by increasing water demand and growing industrialization. Heavy metals can be toxic to humans and other living beings when their intake surpasses a certain threshold. Generally, heavy metal contamination analysis of water resources requires qualified experts with specialized equipment. In this paper, we introduce a method for citizen-based water-quality monitoring through simple pattern classification of water crystallization using a smartphone and portable microscope. This work is a first step toward the development of a Water Expert System smartphone application that will provide the ability to analyze water resource contamination remotely by sending images to the database and receiving an automatic analysis of the sample via machine learning software. In this study, we show the ability of the method to detect Fe 2 mg/1 L, 5 mg/L,10 mg/L polluted distilled water compared with other heavy metals (Al, Pb) pollution. The experimental results show that the classification used method has an accuracy greater than 90%. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Microservice Interaction Prediction in Communication Platform as a Service, Hizmet Olarak Iletişim Platformunda Mikroservis Etkileşim Tahmini(Institute of Electrical and Electronics Engineers Inc., 2022) Aktaş, Kemal; Kilinc, H. Hakan; Arica, Nafiz; Aktaş, Kemal, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kilinc, H. Hakan, Ar-Ge Merkezi, Istanbul, Turkey; Arica, Nafiz, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn telecommunication platforms, it is necessary to monitor the system, take quick action against possible and observed errors, and ensure the continuous operability of the system. However, debugging and problem addressing processes can take a long time in microservice architecture-based platforms with high number of users and heavy traffic. This difficulty arises from the necessity of detecting microservice interactions. In the first phase of this study, it has been observed that providing microservice flows and interactions saves operational teams a significant time during the debugging process. In this scope, different machine learning-based models that predict microservice interactions have been developed and performances are compared. In these models, log patterns are extracted on microservice log data and the interaction map of the mentioned microservice is created by estimating the previous and next microservices that the current microservice interacts with at a certain moment. In the experiments on microservice logs working with the basic call scenario, successful estimation results were obtained that could contribute positively to the debugging process. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Quality of Experience Prediction for VoIP Calls Using Audio MFCCs and Multilayer Perceptron(Institute of Electrical and Electronics Engineers Inc., 2022) Kaledibi, Faruk; Kilinc, H. Hakan; Sakar, C. Okan; Kaledibi, Faruk, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kilinc, H. Hakan, Huawei Turkey R&D Center, Istanbul, Turkey; Sakar, C. Okan, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyTo provide a high-quality communication service to their users, VoIP service providers use some monitoring and warning systems that notify them of any malfunctions that may occur in the system. Because the VoIP service is delivered over the internet, issues with the internet infrastructure and related hardware have a direct impact on the quality of service (QoS) and experience provided. In such cases, service providers analyze the QoS reports to analyze the incidents. The QoS reports consist of various parameters such as packet loss, delay, jitter, and codec information extracted from the related VoIP call. However, in some cases, these parameters may be insufficient or corrupted. Therefore, real sound recordings are used to determine the source of the complaint. However, listening to audio recordings made by third parties is not preferred when the content is sensitive. Thus, a computer-based analysis is an important requirement in such cases. In this study, a machine learning-based model was developed that can classify a given packet loss into six classes, which is one of the most important factors affecting the quality of experience. The audio recordings were represented with Mel Frequency Cepstrum Coefficients (MFCCs). The model trained using 9000 5-second audio recordings from 15 different speakers can predict the packet loss rate and the mean opinion score (MOS) with an accuracy of 87%. © 2022 Elsevier B.V., All rights reserved.Publication Metadata only Insult Detection in the Turkish Language Through Different Machine Learning Algorithms, Insult Detection in the Turkish Language Through(Institute of Electrical and Electronics Engineers Inc., 2023) Özgen, Kerem; Rada, Lavdie; Özgen, Kerem, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, Turkey; Rada, Lavdie, Faculty of Engineering and Natural Sciences, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this research paper, we propose to use the Turkish Court of Cassation- Yargitay- cases to build a dataset for insult detection tasks and compare machine learning models trained on this dataset. We accumulated studies available in the literature compiling Court of Cassation cases and generated a train and test set for testing machine learning algorithms for insult detection. Although machine learning is not capable of understanding the legal context, cultural background, and the nature of insults or non-insults, it can help identify insults with proper training data created by experts. As far as for the authors knowledge this is the first study to use machine learning for the purpose of automatically distinguishing between insult and non-insult cases within the Turkish justice system. Our research, though its is in its first steps, represents a significant contribution to the field, as it addresses a gap in the existing literature and provides a machine learning approach to improving the efficiency and accuracy of legal decision-making. © 2023 Elsevier B.V., All rights reserved.