Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

Browse

Filters

2019 - 201912020 - 20254

Settings

Author: search.filters.author.Coskun, MustafaWoS Q: search.filters.wosQuartile.N/A

Search Results

Now showing 1 - 5 of 5
  • Conference Object
    Citation - Scopus: 1
    Traffic 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, Mustafa
    While 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, Mustafa
    Assigning 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 Kuleli
    Predicting 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.
  • Conference Object
    Expanding Label Sets for Graph Convolutional Networks
    (Springer International Publishing AG, 2025) Coskun, Mustafa; Grama, Ananth; Bakir-Gungor, Burcu; Koyuturk, Mehmet
    In recent years, Graph Convolutional Networks (GCNs) and their variants have been widely utilized in learning tasks that involve graphs. These tasks include recommendation systems, node classification, among many others. In node classification problem, the input is a graph in which the edges represent the association between pairs of nodes, multi-dimensional feature vectors are associated with the nodes, and some of the nodes in the graph have "known" labels. The objective is to predict the labels of the nodes that are not labeled, using the nodes' features, in conjunction with graph topology. While GCNs have been successfully applied to this problem, the caveats that they inherit from traditional deep learning models pose significant challenges to broad utilization of GCNs in node classification. One such caveat is that training a GCN requires a large number of labeled training instances, which is often not the case in realistic settings. To remedy this requirement, state-of-the-art methods leverage network diffusion-based approaches to propagate labels across the network before training GCNs. However, these approaches ignore the tendency of the network diffusion methods in biasing proximity with centrality, resulting in the propagation of labels to the nodes that are well-connected in the graph. To address this problem, here we present an alternate approach, namely LExiCoL, which extrapolates node labels in GCNs in the following three steps: (i) clustering of the network to identify communities, (ii) use of network diffusion algorithms to quantify the proximity of each node to the communities, thereby obtaining a low-dimensional topological profile for each node, (iii) comparing these topological profiles to identify nodes that are most similar to the labeled nodes. Testing on three large-scale real-world networks, we systematically evaluate the performance of the proposed algorithm and show that our approach outperforms existing methods for wide ranges of parameter values.
  • Conference Object
    Citation - Scopus: 21
    Assessing Employee Attrition Using Classifications Algorithms
    (Association for Computing Machinery, 2020-05-15) Ozdemir, Fatma; Cos¸kun, Mustafa; Gezer, Cengiz; Güngör, Vehbi Çağrı; Coskun, Mustafa; Cagri Gungor, V.
    Employees leave an organization when other organizations offer better opportunities than their current organizations. Continuity and sustenance and even completion of jobs are crucial issues for the companies not to suffer financial losses. Especially if the talented employees, who are at critical positions in the companies, leave the job, it becomes difficult for the organizations to maintain their businesses. Today, organizations would like to predict attrition of their employees and plan and prepare for it. However, the HR departments of organizations are not advanced enough to make such predictions in a handcrafted manner. For this reason, organizations are looking for new systems or methods that automatize the prediction of employee attrition utilizing data mining methods. In this study, we use IBM HR data set and apply different classification methods, such as Support Vector Machine (SVM), Random Forest, J48, LogitBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naive Bayes, Bagging, AdaBoost, Logistic Regression, to predict the employee attrition. Different from exiting studies, we systematically evaluate our findings with various classification metrics, such as F-measure, Area Under Curve, accuracy, sensitivity, and specificity. We observe that data mining methods can be useful for predicting the employee attrition. © 2022 Elsevier B.V., All rights reserved.