Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 1Traffic Light Management Systems Using Reinforcement Learning(Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Can, Sultan Kubra; Thahir, Adam Rizvi; Cos¸kun, Mustafa; Güngör, Vehbi Çağrı; Coskun, MustafaWhile reducing traffic congestion and decrease the number of traffic accidents in the intersections, most of the traffic light management approaches cannot adapt well to fast changing traffic dynamics and growing demands of the intersections with modern world developments. To overcome this problem, adaptive traffic controllers are developed, and detectors and sensors are added to systems to enable adoption and dynamism. Recently, reinforcement learning has shown its capability to learn the dynamics of complex environments, such as urban traffic. Although it was studied in single junction systems, one of the problems was the lack of consistency with how the real world system works. Most of the systems assume that the environment is fully observable or actions would be freely executed using simulators. This study aims to merge usefulness of reinforcement learning methods with real-world traffic constraints. Comparative performance evaluations show that the reinforcement learning algorithm (Advantage Actor-Critic (A2C)) converges well while staying stable under changing traffic dynamics. © 2022 Elsevier B.V., All rights reserved.Conference Object Offer Referees Suggester for the Journal Editors(Institute of Electrical and Electronics Engineers Inc., 2019-06) Cos¸kun, Mustafa; Hacilar, Hilal; Gezer, Cengiz; Güngör, Vehbi Çağrı; Coskun, MustafaAssigning appropriate referees to a journal or conference paper is a vital task for many reasons, including enhancing the journal venue quality and reliance, fair judgement of the papers, and among many others. While assigning the referees to the papers, the editors of a journal venue need to find suitable referees who are both related to field of the given paper and have no conflict of interest with the authors of the paper. Editorial-wise this referee assignment process is implemented in a hand-crafted manner, i.e., the editor needs to find the most suitable referees to the paper via a search engine and manually refines the all unrelated and having conflict of interest authors to the paper authors. Clearly, such a manual referee searching process is tedious and time consuming for the editors.In this paper, we present an alternate automated approach for assigning referees problem using intrinsic random walk with restart proximity measure. In our experiments based on a comprehensive DBLP networks, we show that our approach, called OFFER, significantly outperforms state-of-the-art the random walk with restart based method. © 2021 Elsevier B.V., All rights reserved.Conference Object Linear Vs. Non-Linear Embedding Methods in Recommendation Systems(Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Gurler, Kerem; Cos¸kun, Mustafa; Karagenc, Safak; Orun, Gokhan; Kuleli Pak, Burcu Kuleli; Güngör, Vehbi Çağrı; Coskun, Mustafa; Pak, Burcu KuleliPredicting customer interest in items is very crucial in direct marketing as it can potentially boost sales. Data mining techniques are developed to predict which items a particular user might be interested in based on their purchase history or explicit feedback in form of ratings or comments. Recently, non-linear and linear methods have been developed for this purpose. In this study, we applied Neighborhood based Collaborative Filtering (CF), Matrix Factorization (MF), Singular Value Decomposition (SVD), Neural Graph CF (NGCF) and Light Graph Convolutional Network (LightGCN) on explicit user product rating data which is acquired from the online gaming and mobile entertainment platform called HADI. We compared the results of node embedding methods in terms of Precision@k, Recall@k and NDCG@k values. SVD and LightGCN showed the best test performance and SVD was significantly superior to LightGCN in terms of training speed. To further increase predictive performance of SVD, we have applied classification with Logistic Regression and Deep Random Forest on user and item embeddings created by the SVD. © 2022 Elsevier B.V., All rights reserved.