Yazar "Ozhan, Erkan" seçeneğine göre listele

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    The Analysis of Corporate Social Responsibility, Identification and Customer Orientation by Structural Equation Modelling and Artificial Intelligence
    (Sage Publications India Pvt Ltd, 2022) Ozhan, Seniz; Ozhan, Erkan; Pritchard, Gamze Yakar
    When the successful businesses of today are examined, it is seen that the main factor in their success is the value they give to the customers rather than the production power. One of the most important factors in ensuring customer satisfaction and loyalty is customer orientation (CO). In this study, it is aimed to investigate the perceived management and customer support for corporate social responsibility, the identification of the employees with the business and the customers and its effect on CO. Another aim of the study is to obtain a model that classifies employee-customer identification (ECI)-CO levels for employees by using artificial intelligence methods not used in previous studies. The research data were obtained from salesperson working in shopping malls in Istanbul. Hypothesis testing with structural equation modelling (SEM) has shown that perceived management and customer support for corporate social responsibility have an impact on employee identification with the business and customers. It has been observed that ECI affects CO, while organizational identification has no significant effect on CO. The structural equation modelling and artificial intelligence findings have empirically demonstrated that high accuracy practical classification models can be obtained and used to detect and solve different marketing problems.
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    The Analysis of Firewall Policy Through Machine Learning and Data Mining
    (Springer, 2017) Ucar, Erdem; Ozhan, Erkan
    Firewalls are primary components for ensuring the network and information security. For this purpose, they are deployed in all commercial, governmental and military networks as well as other large-scale networks. The security policies in an institution are implemented as firewall rules. An anomaly in these rules may lead to serious security gaps. When the network is large and policies are complicated, manual cross-check may be insufficient to detect anomalies. In this paper, an automated model based on machine learning and high performance computing methods is proposed for the detection of anomalies in firewall rule repository. To achieve this, firewall logs are analysed and the extracted features are fed to a set of machine learning classification algorithms including Naive Bayes, kNN, Decision Table and HyperPipes. F-measure, which combines precision and recall, is used for performance evaluation. In the experiments, kNN has shown the best performance. Then, a model based on the F-measure distribution was envisaged. 93 firewall rules were analysed via this model. The model anticipated that 6 firewall rules cause anomaly. These problematic rules were checked against the security reports prepared by experts and each of them are verified to be an anomaly. This paper shows that anomalies in firewall rules can be detected by analysing large scale log files automatically with machine learning methods, which enables avoiding security breaches, saving dramatic amount of expert effort and timely intervention.
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    Automatically Discovering Relevant Images From Web Pages
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Uzun, Erdinc; Ozhan, Erkan; Agun, Hayri Volkan; Yerlikaya, Tarik; Bulus, Halil Nusret
    Web pages contain irrelevant images along with relevant images. The classification of these images is an error-prone process due to the number of design variations of web pages. Using multiple web pages provides additional features that improve the performance of relevant image extraction. Traditional studies use the features extracted from a single web page. However, in this study, we enhance the performance of relevant image extraction by employing the features extracted from different web pages consisting of standard news, galleries, video pages, and link pages. The dataset obtained from these web pages contains 100 different web pages for each 200 online news websites from 58 different countries. For discovering relevant images, the most straightforward approach extracts the largest image on the web page. This approach achieves a 0.451 F-Measure score as a baseline. Then, we apply several machine learning methods using features in this dataset to find the most suitable machine learning method. The best f-Measure score is 0.822 using the AdaBoost classifier. Some of these features have been utilized in previous web data extraction studies. To the best of our knowledge, 15 new features are proposed for the first time in this study for discovering the relevant images. We compare the performance of the AdaBoost classifier on different feature sets. The proposed features improve the f-Measure by 35 percent. Besides, using only the cache feature, which is the most prominent feature, corresponds to 7 percent of this improvement.