Person: ŞAHİNGÖZ, ÖZGÜR KORAY
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ŞAHİNGÖZ
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ÖZGÜR KORAY
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Publication Open Access Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini(Bitlis Eren Üniversitesi Rektörlüğü, 2020) Şişmanoğlu, Gözde; Koçer, Furkan; Önde, Mehmet ali; ŞAHİNGÖZ, ÖZGÜR KORAYSon yıllarda, bilgisayarların donanımındaki teknolojik gelişmeler ve makine öğrenme tekniklerindeki gelişmeler nedeniyle, "Büyük Veri" ve "Paralel İşleme" kullanımı olmak üzere problem çözmek için iki artan yaklaşım vardır. Özellikle GPU'lar gibi çok çekirdekli bilgi işlem aygıtlarında paralel olarak gerçekleştirilebilen Derin Öğrenme algoritmalarının ortaya çıkmasıyla, bu yaklaşımlarla birçok gerçek dünya problemleri çözülebilmektedir. Derin öğrenme modelleri eğitildikleri veri ile sınıflandırma, regresyon analizi ve zaman serilerinde tahmin gibi uygulamalarda büyük başarılar göstermektedir. Bu modellerin finansal piyasadaki en aktif uygulama alanlarından biri özellikle borsada işlem gören hisse senetlerinin tahmini işlemleridir. Bu alanda amaç, pazardaki değişim süreci hakkındaki hisse senedinin önceki günlük verilerine bakarak kısa veya uzun vadeli gelecekteki değerini tahmin etmeye çalışmaktır. Bu çalışmada, LSTM, GRU ve BLSTM isimli 3 farklı derin öğrenme modeli kullanılarak bir hisse senedi tahmin sistemi geliştirilip, kullanılan modeller arasında karşılaştırmalı bir analiz yapıldı. Spekülatif hareketlerden uzak olması için veri seti olarak 1968'den 2018'e kadar olan New York Borsası'ndan hisse senedinin zaman serisi değerlerini kullanıldı. Spesifik olarakta IBM hisse senedi ile test çalışmaları yapıldı. Deneysel sonuçlar BLSTM modelinin 5 günlük girdi verileriyle eğitilmesi ile %63,54 lük bir yönsel doğruluk değerine ulaşıldığını göstermektedir.Publication Embargo A Comparative Study of Smoothing a Vehicle s Trajectory which is Calculated by an Evolutionary Algorithm(2016-06) Buran, Bayram Ali; Çağlar, Süleyman Hikmet; ŞAHİNGÖZ, ÖZGÜR KORAY; 214903; 243931; 114368Determining a vehicle’s trajectory is a complex and hard to solve type problem in the literature and it is identified as a NP-Hard optimization problem which is studied in different engineering disciplines such as computer, electrical and industrial engineering. It has been observed that such complex problems can be solved by using various approaches and lots of them are focused on the usage of Evolutionary Algorithms especially in case of a large number of controls points which are needed to be visited. Although these algorithms provide near optimal solutions, in the real world, vehicles are not able to follow this determined path (trajectory) without any deviation. Because vehicles are moving objects and each one moves with a certain speed. Therefore it is impossible for a vehicle to make a sharp turn after visiting control points. These vehicles need to make smoothed turns over these points. Therefore there will be a certain difference between the calculated path and the real path. It is needed to determine the real path by using necessary mathematical solutions for smoothing these paths. To ensure the motion continuity of vehicles, they need to follow paths determined according to a certain criterion. In this study, the most common smoothing methods which are used to ensure these continuities (Bezier, B-Spline and Dubins) have been compared and it is aimed to show the different approaches in an application area of path planning problems as a comparative study.Publication Restricted Detection of Phishing Websites by Using Machine Learning-Based URL Analysis(Institute of Electrical and Electronics Engineers Inc., 2020) Korkmaz, Mehmet; ŞAHİNGÖZ, ÖZGÜR KORAY; Diri, BanuIn recent years, with the increasing use of mobile devices, there is a growing trend to move almost all real-world operations to the cyberworld. Although this makes easy our daily lives, it also brings many security breaches due to the anonymous structure of the Internet. Used antivirus programs and firewall systems can prevent most of the attacks. However, experienced attackers target on the weakness of the computer users by trying to phish them with bogus webpages. These pages imitate some popular banking, social media, e-commerce, etc. sites to steal some sensitive information such as, user-ids, passwords, bank account, credit card numbers, etc. Phishing detection is a challenging problem, and many different solutions are proposed in the market as a blacklist, rule-based detection, anomaly-based detection, etc. In the literature, it is seen that current works tend on the use of machine learning-based anomaly detection due to its dynamic structure, especially for catching the 'zero-day' attacks. In this paper, we proposed a machine learning-based phishing detection system by using eight different algorithms to analyze the URLs, and three different datasets to compare the results with other works. The experimental results depict that the proposed models have an outstanding performance with a success rate.Publication Restricted Intelligent Ambulance Management System in Smart Cities(Institute of Electrical and Electronics Engineers Inc., 2020) Akça, Tugay; ŞAHİNGÖZ, ÖZGÜR KORAY; Koçyiğit, Emre; Tozal, MücahidAccording to the United Nations' expectation, the total population of the cities will be doubled in the next three decades. This accelerating growth causes crucial problems in the main components of both traditional cities and smart cities. To increase the living quality of the residence in smart cities, enabling a clean, healthy, and sustainable environment are the major fields for the smart cities' managers and directors. One of the main infrastructures of the smart city is identified as smart health, which can be enabled with the use of modern technologies such as Internet of Things, especially for accessing the patients when they need help. In this Project, a smart ambulance management system is proposed in a smart city environment. If a patient needs an ambulance, the operator finds the nearest ambulance and direct it to the patient. The coordinates of ambulances are dynamically traced by the system, and Google Maps, as a third-party service, is used in order to calculate the shortest path to the casualty. After reaching to the patient, the expert (doctor or nurse) investigates the situation and finds the best available hospital by the proposed system. The experimental results showed that the proposed system finds the best solution in an acceptable ×.Publication Restricted Genetic Algorithm Based Optimized Waste Collection in Smart Cities(Institute of Electrical and Electronics Engineers Inc., 2020) Özmen, Mehmet; Şahin, Hasan; ŞAHİNGÖZ, ÖZGÜR KORAYIn recent years, the concept of smarts cities emerged to cope with the growth that cities around the world are facing. There are lots of problem areas in smart cities such as smart education, health, buildings, shopping, traffic management, etc. Waste management is one complex and effective problems of urbanization that is needed to be solved in smart cities. Route planning for waste collection and garbage trucks is a known issue in waste management. In this project, a genetic algorithm is proposed to address the problem of waste collection route using a truck fleet. The algorithm was tested in a simplified real state in single area and proved to be applicable to real-world scenarios based solely on the actual data of waste collection of cities.Publication Metadata only Deep Learning in Intrusion Detection Systems(2018) Demir, Önder; BAYDOĞMUŞ, GÖZDE KARATAŞ; ŞAHİNGÖZ, ÖZGÜR KORAY; 110942; 170651; 214903Publication Open Access A Clustering Approach for Intrusion Detection with Big Data Processing on Parallel Computing Platform(Bajece (İstanbul Teknik Üniversitesi), 2019) REİS, B.; KAYA, S. B.; ŞAHİNGÖZ, ÖZGÜR KORAYIn recent years there is a growing number of attacks in the computer networks. Therefore, the use of a prevention mechanism is an inevitable need for security admins. Although firewalls are preferred as the first layer of protection, it is not sufficient for preventing lots of the attacks, especially from the insider attacks. Intrusion Detection Systems (IDSs) have emerged as an effective solution to these types of attacks. For increasing the efficiency of the IDS system, a dynamic solution, which can adapt itself and can detect new types of intrusions with a dynamic structure by the use of learning algorithms is mostly preferred. In previous years, some machine learning approaches are implemented in lots of IDSs. In the current position of artificial intelligence, most of the learning systems are transferred with the use of Deep Learning approaches due to its flexibility and the use of Big Data with high accuracy. In this paper, we propose a clustered approach to detect the intrusions in a network. Firstly, the system is trained with Deep Neural Network on a Big Data set by accelerating its performance with the use of CUDA architecture. Experimental results show that the proposed system has a very good accuracy rate and low runtime duration with the use of this parallel computation architecture. Additionally, the proposed system needs a relatively small duration for training the system.Publication Open Access Smart home hecurity with the use of WSNs on future intelligent cities(2019-04) Dine, G.; ŞAHİNGÖZ, ÖZGÜR KORAYIn recent years, smart cities use some advanced technologies such as Cyber-Physical Systems and Internet of Things for improving the quality of human life by enabling extra security and communication abilities. In a recent survey of ICMA which is the world's leading association of professional city and county managers, ICMA emphasizes the importance of the safety and security benefits of smart cities by categorizing it as one of the five important motivating factors. Therefore, in this paper, we proposed a new security model for smart buildings and smart homes by using a hot topic research area of Cyber-Physical Systems: WSNs. With the use of this system, it is aimed to construct a smart security system by using a minimum number of sensor nodes with the increased coverage rate for smart buildings and homes.Publication Metadata only Deep learning based classification of malaria from slide images(2019) Kalkan, Soner Can; ŞAHİNGÖZ, ÖZGÜR KORAYAs one of the most life-threatening disease in the tropical and warmer-climate countries, Malaria affects not only animals but also humans who can be infected by only a single bite from a mosquito. Although this disease is wiped out in high-income countries, as a result of traveling people, it can even emerge in all part of the world. World Health Organization announced that more than 400,000 people are expected to die due to this illness. However, it is a curable and preventable disease, if early detection is possible. Traditionally, Pathologists diagnosed this disease manually by using microscope which is a time-consuming process in our computerized world, and this model depends on the experience of the Pathologists, which is a critical problem in rural areas. Therefore, in recent years detection of Malaria using computerized image analysis which is trained using some dynamic learning mechanism has gained increasing importance. In this paper, we proposed an image processing-based Malaria detection system which is trained by deep learning. We used relatively big data for increasing the accuracy of the system, and the reached accuracy showed that the proposed system has an outstanding classification rate that can be used in real-world detection.Publication Open Access Blockchain based secure communication for Iot devices in smart sities(2019-04) Yetiş, R.; ŞAHİNGÖZ, ÖZGÜR KORAYIn smart city technologies we have witnessed advanced technological improvements in small computing devices, which can be connected to the Internet and named as Internet of Thing (IoT) devices, and cooperatively working complex systems. With this increased use of new technologies, the security problem is becoming more and more important because complex systems lead to unpredictable security vulnerabilities, which result in financial and private information losses. As a recently emerged technology, Blockchain was emerged as an alternative solution to security breaches of a different application environment. In contrast to the central structure used by most systems, it is preferred especially in the area of security by its distributed structure and the cryptographic hash algorithm it uses. Today, structures such as Smart Home, Smart City, Smart Environment and Smart Agriculture, which are created by using IoT are seen as active research areas with more security shortages. The reason for the security weakness in these areas arises from the hardware restriction on the IoT devices used. In the proposed system, an authorization system for IoT devices has been tried to be set up by using the distributed node structure of Blockchain system and blocks kept in these nodes. UDP (User Datagram Protocol), which uses a simple communication model without establishing a connection to the minimum protocol mechanism for communication of nodes in the system, was preferred. The communication between the nodes has been encrypted using encryption methods, thus creating a secure environment.
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