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Conference Object Citation - WoS: 1An Undergraduate Curriculum for Deep Learning(Ieee, 2018) Tirkes, Guzin; Ekin, Cansu Cigdem; Sengul, Gokhan; Bostan, Atila; Karakaya, MuratDeep Learning (DL) is an interesting and rapidly developing field of research which has been currently utilized as a part of industry and in many disciplines to address a wide range of problems, from image classification, computer vision, video games, bioinformatics, and handwriting recognition to machine translation. The starting point of this study is the recognition of a big gap between the sector need of specialists in DL technology and the lack of sufficient education provided by the universities. Higher education institutions are the best environment to provide this expertise to the students. However, currently most universities do not provide specifically designed DL courses to their students. Thus, the main objective of this study is to design a novel curriculum including two courses to facilitate teaching and learning of DL topic. The proposed curriculum will enable students to solve real-world problems by applying DL approaches and gain necessary background to adapt their knowledge to more advanced, industry-specific fields.Conference Object Citation - WoS: 1Parking Space Occupancy Detection Using Deep Learning Methods(Ieee, 2018) Akinci, Fatih Can; Karakaya, MuratThis paper presents an approach for gathering information about the availabilty of the parking lots using Convoltional Neural Network (CNN) for image processing running on an embedded system. By using an efiicent neural network model, we made it possible to use a very low cost embedded system compared to the ones used in previous works on this topic. This efficient model's performance is compared to one of the models that proved its accuracy in image classification competitions. In these tests, we used datasets that has thousands of different images taken from parking lots in different light and weather conditions.Conference Object An Iot Application for Locating Victims Aftermath of an Earthquake(Ieee, 2017) Karakaya, Murat; Sengul, Gokhan; Gokcay, ErhanThis paper presents an Internet of Things (IoT) framework which is specially designed for assisting the research and rescue operations targeted to collapsed buildings aftermath of an earthquake. In general, an IoT network is used to collect and process data from different sources called things. According to the collected data, an IoT system can actuate different mechanisms to react the environment. In the problem at hand, we exploit the IoT capabilities to collect the data about the victims before the building collapses and when it falls down the collected data is processed to generate useful reports which will direct the search and rescue efforts. The proposed framework is tested by a pilot implementation with some simplifications. The initial results and experiences are promising. During the pilot implementation, we observed some issues which are addressed in the proposed IoT framework properly.Conference Object Deep Learning and Current Trends in Machine Learning(Ieee, 2018) Bostan, Atila; Sengul, Gokhan; Tirkes, Guzin; Ekin, Cansu; Karakaya, MuratAcademic interest and commercial attention can be used to identify how much potential a novel technology may have. Since the prospective advantages in it may help solving some problems that are not solved yet or improving the performance of readily available ones. In this study, we have investigated the Web of Science (WOS) indexing service database for the publications on Deep Learning (DL), Machine Learning (ML), Convolutional Neural Networks (CNN), and Image Processing to reveal out the current trend. The figures indicate the strong potential in DL approach especially in image processing domain.Conference Object Citation - WoS: 1Ontology-Supported Enterprise Architecture Analysis(Ieee, 2017) Uysal, Murat Pasa; Karakaya, MuratToday, processing integrated information within and between enterprises is increasingly becoming more and more critical, and so is the implementation and evaluation of an Enterprise Architecture (EA). The review of literature on EA evaluation shows several issues. However, the evaluation of EAs has not attracted sufficient attention, and thus, this research area has not been explored thoroughly yet. We believe that in order to ensure consistency, interoperability and computational inferences among EAs, a complete and holistic approach, rather than monolithic, should be developed. Therefore, in this study, we propose an ontology-supported process model for the evaluation of EAs, and present the implementation details. The main contributions of the present study are the improvements realized in the expressiveness, extensibility, and computable power of EAs, and their evaluation techniques. Although the proposed model requires gathering empirical evidences and investigating applications in concrete cases, the first implications of the proposed model indicates its validity and feasibility, and, hence, the initial results are promising for continuing future studies.
