Browsing by Author "Gezer, Cengiz"

Now showing 1 - 9 of 9

Citation - Scopus: 21
Assessing Employee Attrition Using Classifications Algorithms
(Association for Computing Machinery, 2020) Ozdemir, Fatma; Cos¸kun, Mustafa; Gezer, Cengiz; Güngör, Vehbi Çağrı
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.
Citation - WoS: 2
Credit Card Fraud Detection With Machine Learning Methods
(IEEE, 2019) Goy, Gokhan; Gezer, Cengiz; Gungor, Vehbi Cagri
With the increase in credit card usage of people, the credit card transactions increase dramatically. It is difficult to identify fraudulent transactions among the vast amount of credit card transactions. Although credit card fraud is limited in number of transactions, it causes serious problems in terms of financial losses for individuals and organizations. Even though large number of studies has been conducted to solve this problem, there is no generally accepted solution. In this paper, a publicly available data set is used. The unbalance problem of the data set was solved by using hybrid sampling methods together. On this data set, comparative performance evaluations have been conducted. Different from other studies, the Area Under the Curve (AUC) metric, which expresses the success in such data sets, has also been used in addition to standard performance metrics. Since it is also important to quickly detect credit card fraud transactions; the running time of different methods is also presented as another performance metric.
Citation - WoS: 1
Citation - Scopus: 3
Data Mining Techniques in Direct Marketing on Imbalanced Data Using Tomek Link Combined With Random Under-Sampling
(Assoc Computing Machinery, 2021) Yilmaz, Umit; Gezer, Cengiz; Aydin, Zafer; Gungor, V. CaGri
Determining the potential customers is very important in direct marketing. Data mining techniques are one of the most important methods for companies to determine potential customers. However, since the number of potential customers is very low compared to the number of non-potential customers, there is a class imbalance problem that significantly affects the performance of data mining techniques. In this paper, different combinations of basic and advanced resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE), Tomek Link, RUS, and ROS were evaluated to improve the performance of customer classification. Different feature selection techniques are used in order the decrease the number of non-informative features from the data such as Information Gain, Gain Ratio, Chi-squared, and Relief. Classification performance was compared and utilized using several data mining techniques, such as LightGBM, XGBoost, Gradient Boost, Random Forest, AdaBoost, ANN, Logistic Regression, Decision Trees, SVC, Bagging Classifier based on ROC AUC and sensitivity metrics. A combination of Tomek Link and Random Under-Sampling as a resampling technique and Chi-squared method as feature selection algorithm showed superior performance among the other combinations. Detailed performance evaluations demonstrated that with the proposed approach, LightGBM, which is a gradient boosting algorithm based on decision tree, gave the best results among the other classifiers with 0.947 sensitivity and 0.896 ROC AUC value.
Email Clustering & Generating Email Templates Based on Their Topics
(Assoc Computing Machinery, 2021) Coskun, Fatih; Gezer, Cengiz; Gungor, V. Cagri
Email templates have a significant impact on users in terms of productivity. Using an email template that is produced successfully is going to transfer the main information with a considerable impression. While the previous studies were focused on the email generation by text-differences in the content of the emails, generated templates based on email topics can provide better productivity for the companies. This article proposes a system, in which user emails are clustered according to the topics of the emails, and introduces an email template generation system that utilizes the sample emails belonging to the formed email clusters. For this purpose, the Enron email dataset has been used and the performance of different text preprocessing and topic modeling algorithms, such as DMM, GPU-DMM, GPU-PDMM, LF-DMM, LDA, LF-LDA, BTM, WNTM, PTM, SATM, have been investigated and compared to determine the most efficient one. After obtaining the email topics, the system shows the examples of the emails representing the selected topics and enables the authorized users to create templates that generalize these topics.
Citation - Scopus: 6
Generating Emergency Evacuation Route Directions Based on Crowd Simulations With Reinforcement Learning
(Institute of Electrical and Electronics Engineers Inc., 2022) Unal, Ahmet Emin; Gezer, Cengiz; Kuleli Pak, Burcu Kuleli; Güngör, Vehbi Çağrı
In an emergency, it is vital to evacuate individuals from the dangerous environments. Emergency evacuation plan-ning ensures that the evacuation is safe and optimal in terms of evacuation time for all of the people in evacuation. To this end, the computer-enabled evacuation simulation systems are used to generate optimal routes for the evacuees. In this paper, a dynamic emergency evacuation route generator has been proposed based on indoor plans of the building and the locations of the evacuees. To generate the optimal routes in real-time, a reinforcement learning algorithm (proximal policy optimization) is presented. Comparative performance results show that the proposed model is successful for evacuating the individuals from the building in different scenarios. © 2022 Elsevier B.V., All rights reserved.
Citation - Scopus: 5
Makine Öğrenmesi Yöntemleri ile Kredi Kartı Sahteciliğinin Tespiti
(Institute of Electrical and Electronics Engineers Inc., 2019) Göy, Gökhan; Gezer, Cengiz; Güngör, Vehbi Çağrı
With the increase in credit card usage of people, the credit card transactions increase dramatically. It is difficult to identify fraudulent transactions among the vast amount of credit card transactions. Although credit card fraud is limited in number of transactions, it causes serious problems in terms of financial losses for individuals and organizations. Even though large number of studies has been conducted to solve this problem, there is no generally accepted solution. In this paper, a publicly available data set is used. The unbalance problem of the data set was solved by using hybrid sampling methods together. On this data set, comparative performance evaluations have been conducted. Different from other studies, the Area Under the Curve (AUC) metric, which expresses the success in such data sets, has also been used in addition to standard performance metrics. Since it is also important to quickly detect credit card fraud transactions; the running time of different methods is also presented as another performance metric. © 2020 Elsevier B.V., All rights reserved.
Offer : Referees Suggester for the Journal Editors
(IEEE, 2019) Coskun, Mustafa; Hacilar, Hilal; Gezer, Cengiz; Gungor, Vehbi Cagri
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.
Offer Referees Suggester for the Journal Editors
(Institute of Electrical and Electronics Engineers Inc., 2019) Cos¸kun, Mustafa; Hacilar, Hilal; Gezer, Cengiz; Güngör, Vehbi Çağrı
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.
Citation - WoS: 30
Citation - Scopus: 46
A Survey on Information Security Threats and Solutions for Machine to Machine (M2M) Communications
(Academic Press inc Elsevier Science, 2017) Tuna, Gurkan; Kogias, Dimitrios G.; Gungor, V. Cagri; Gezer, Cengiz; Taskin, Erhan; Ayday, Erman
Although Machine to Machine (M2M) networks allow the development of new promising applications, the restricted resources of machines and devices in the M2M networks bring several constraints including energy, bandwidth, storage, and computation. Such constraints pose several challenges in the design of M2M networks. Furthermore, some elements that contributed to the rise of M2M applications have caused several new security threats and risks, typically due to the advancements in technology, increasing computing power, declining hardware costs, and freely available software tools. Due to the restricted capabilities of M2M devices, most of the recent research efforts on M2M have focused on computing, resource management, sensing, congestion control and controlling technologies. However, there are few studies on security aspects and there is a need to introduce the threats existing in M2M systems and corresponding solutions. Accordingly, in this paper, after presenting an overview of potential M2M applications, we present a survey of security threats against M2M networks and solutions to prevent or reduce their impact. Then, we investigate security-related challenges and open research issues in M2M networks to provide an insight for future research opportunities. Moreover, we discuss the oneM2M standard, one of the prominent standard initiatives for more secure and smoother M2M networks and the Internet of Things. (C) 2017 Elsevier Inc. All rights reserved.