Research Outputs
Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2
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Publication Restricted A Mixed-integer programming approach to multi-class data classificiation problem(Koç University, 2005) Yüksektepe, Fadime Üney; Türkay, Metin; 0000-0003-4769-6714; Koç University Graduate School of Sciences and Engineering; Industrial Engineering; 24956Publication Restricted Analysis of context embeddings in word sense induction(Koç University, 2015) Başkaya, Osman; Yüret, Deniz; 0000-0002-7039-0046; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 179996Publication Restricted Analysis of the relation between mobility signatures and product usage of bank customers on product recommendation systems(Koç University, 2017) Ürküp, Çağan; Salman, Fatma Sibel; 0000-0001-6833-2552; Koç University Graduate School of Sciences and Engineering; Industrial Engineering; 178838
Publication Metadata only Answering spatial density queries under local differential privacy(IEEE-Inst Electrical Electronics Engineers Inc, 2024) ; Department of Computer Engineering; Tire, Ekin; Gürsoy, Mehmet Emre; Department of Computer Engineering; ; Graduate School of Sciences and Engineering; College of Engineering;Spatial density queries are fundamental in many geospatial data analysis and crowdsourcing tasks. However, answering spatial density queries may violate users’privacy by exposing their locations to an untrusted data collector. In this paper, we propose a solution for answering spatial density queries under Local Differential Privacy (LDP), a state-of-the-art privacy protection standard. Our solution consists of four main steps: partitioning, finding sensitivity, user-side noisy response computation, and server-side estimation. For the first step, we propose and analyze three basic partitioning strategies, and based on our analysis, we design an improved strategy called Advanced Partitioning. For the second step, we adapt graph-based modeling of query sets from the centralized DP literature. Advanced Partitioning also leverages and extends this technique by formulating the partitioning problem using vertex coloring. For the third and fourth steps, in addition to adapting two popular LDP protocols (GRR, RAPPOR), we propose a novel extension for the OUE protocol. Our new protocol (OBE) is not only applicable to our problem but can also be used in other LDP problems with bitvector encodings. Finally, we perform an extensive experimental evaluation of different partitioning strategies and protocols using multiple real-world datasets. Results show that Advanced Partitioning and OBE yield the lowest error, demonstrating the superiority of our proposed methods. IEEEPublication Restricted Forecasting simulated retail demand using statistical and data mining techniques(Koç University, 2007) Tunçelli, Ayşe Gül; Ali, Özden Gür; Sayın, Serpil; 0000-0002-9409-4532; 0000-0002-3672-0769; Koç University Graduate School of Sciences and Engineering; Industrial Engineering; 57780; 6755Publication Restricted Generating negative samples with a related task for recommendation(Koç University, 2019) Kızıl, İpek; Akgün, Barış; 0000-0002-4079-6889; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 258784Publication Restricted Hyper-box enclosure method and its application to microarray analysis(Koç University, 2010) Dağlıyan, Onur; Kavaklı, İbrahim Halil; 0000-0001-6624-3505; Koç University Graduate School of Sciences and Engineering; Chemical and Biological Engineering; 40319Publication Metadata only Machine learning methods for alarm prediction in industrial informatics: review and benchmark(Springer Science and Business Media Deutschland Gmbh, 2023) Department of Computer Engineering; Görgülü, Hamza; Özkasap, Öznur; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringAlarm systems are important assets for plant safety and efficiency in a variety of industries, including power and utility, process and manufacturing, oil and gas, and communications. Especially in the process-based industry, alarm systems collect a huge amount of data in the field that requires operators to take action carefully. However, existing industrial alarm systems suffer from poor performance, mostly with alarm overloading and alarm flooding. Therefore, this problem creates an opportunity to implement machine learning models in order to predict upcoming alarms in the industry. In this way, the operators can take the necessary actions automatically while they are using their capacity for other unpredicted alarms. This study provides an overview of alarm prediction methods used in industrial alarm systems with the context of their classification types. In addition, a comparative analysis was conducted between two state-of-the-art deep learning models, namely Long Short-Term Memory (LSTM) and Transformer, through a benchmarking process. The experimental results of both models were evaluated and contrasted to identify their respective strengths and weaknesses. Moreover, this study identifies research gaps in alarm prediction, which can guide future research for better alarm management systems.Publication Restricted MILP based hyper-box enclosure approach to multi-class data classification(Koç University, 2009) Yüksektepe, Fadime Üney; Türkay, Metin; 0000-0003-4769-6714; Koç University Graduate School of Sciences and Engineering; Industrial Engineering and Operations Management; 24956Publication Restricted Optimizing multiple object tracking with graph neural networks on a graphcore IPU(Koç University, 2024) Acar, Mustafa Orkun; Erten, Didem Unat; 0000-0002-2351-0770; Koç University Graduate School of Sciences and Engineering; Computer Science and Engineering; 219274