Browsing by Author "Karakaya,M."

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A Case Study on Measuring Course Learning Outcomes in Software Engineering Education;
(CEUR-WS, 2017) Karakaya,M.; Turhan,C.; Yazici,A.; Energy Systems Engineering; Computer Engineering; Software Engineering; 06. School Of Engineering; 01. Atılım University
One of the most critical outcomes in the MÜDEK accreditation system which plays an important role in the evaluation and assessment of the quality of education in higher education institutions is evaluating how much students have achieved the course outcomes. This study aims to compare two methods utilized to measure course outcomes in the Software Engineering department of Atilim University. In the most general sense, these two methods are based on indirect and direct measurement techniques. The results of the analysis show a high degree of differentiation between the direct measurement and indirect measurement results. In light of the results obtained, some recommendations have been offered for course outcome assessment methods to be developed in the scope of MÜDEK accreditation.
Deep Learning and Current Trends in Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2018) Bostan,A.; Ekin,C.; Sengul,G.; Karakaya,M.; Tirkes,G.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Academic 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. © 2018 IEEE.
Deep Learning and Current Trends in Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2018) Bostan,A.; Ekin,C.; Sengul,G.; Karakaya,M.; Tirkes,G.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Academic 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. © 2018 IEEE.
Citation - Scopus: 2
Detecting Errors in Automatic Image Captioning by Deep Learning;
(Institute of Electrical and Electronics Engineers Inc., 2021) Karakaya,M.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Automatic tagging of images is an important researcli topic in tlie field of image processing. Anotlier area similar to this is the automatic generation of picture captions. In this study, a deep learning model that automatically tags the pictures is used to detect errors in image captions. As a result of the initial experiments, it is observed that the proposed system can find up to 80% of the errors in the image captions. © 2021 IEEE
Citation - Scopus: 4
Effect of Pso Tuned P, Pd, and Pid Controllers on the Stability of a Quadrotor
(Institute of Electrical and Electronics Engineers Inc., 2019) Ustunkok,T.; Karakaya,M.; Software Engineering; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Many popular quadrotor controllers are based on PID controllers. This study compares the behavior of a quadrotor when its controller is the proportional (P) only, proportional (P) and derivative (D), and all terms of the PID controller which is tuned by a Particle Swarm Optimization (PSO) implementation. A P, PD, and PID controller integrated quadrotor model is used with realistic parameters while conducting experiments in simulation. Our goal is to find out if it is worth to use PID or some of its terms is enough to get a stable system. According to the preliminary results of the experiments, the statistical difference of results shows that PID is better than both P and PD for the given model. © 2019 IEEE.
Citation - Scopus: 2
Image Tag Refinement With Self Organizing Maps
(Institute of Electrical and Electronics Engineers Inc., 2019) Ustunkok,T.; Acar,O.C.; Karakaya,M.; Software Engineering; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Nowadays, data sharing has become faster than ever. This speed demands novel search methods. Most popular way of accessing the data is to search its tag. Therefore, creating tags, captions from an image is a research area that gains reputation rapidly. In this study, we aim to refine image captions by utilizing Self Organizing Maps. We extract image and caption pairs as feature vectors and then cluster those vectors. Vectors with similar content clustered close to each other. With the help of those clusters, we hope to get some relevant tags that do not exist in the original tags. We performed extensive experiments and presented our initial results. According to these results, the proposed model performs reasonably well with a 54% precision score. Finally, we conclude our work by providing a list of future work. © 2019 IEEE.
Citation - Scopus: 1
An Iot Application for Locating Victims Aftermath of an Earthquake
(Institute of Electrical and Electronics Engineers Inc., 2017) Karakaya,M.; Şengül,G.; Gökçay,E.; Software Engineering; Computer Engineering; 06. School Of Engineering; 01. Atılım University
This 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. © 2017 IEEE.
Citation - Scopus: 1
Ontology-Supported Enterprise Architecture Analysis
(Institute of Electrical and Electronics Engineers Inc., 2017) Uysal,M.P.; Karakaya,M.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Today, 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. © 2017 IEEE.
Citation - Scopus: 7
Parking space occupancy detection using deep learning methods;
(Institute of Electrical and Electronics Engineers Inc., 2018) Akinci,F.C.; Karakaya,M.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
This 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 eflicent 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. © 2018 IEEE.
Citation - WoS: 1
Citation - Scopus: 2
Ss-Mla: a Semisupervised Method for Multi-Label Annotation of Remotely Sensed Images
(SPIE, 2021) Üstünkök,T.; Karakaya,M.; Software Engineering; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Recent technological advancements in satellite imagery have increased the production of remotely sensed images. Therefore, developing efficient methods for annotating these images has gained popularity. Most of the current state-of-the-art methods are based on supervised machine learning techniques. We propose a method called semisupervised multi-label annotizer (SS-MLA) that adapts vector-quantized temporal associative memory to annotate remotely sensed images. One of the advantages of SS-MLA over the supervised methods is that it extracts features not only from the given sample but also from similar samples that are previously seen without using an explicit attention mechanism. Thus SS-MLA enhances the learning efficiency of the training process. We conduct extensive performance comparisons with five different methods in the literature over four datasets. The comparison results indicate the success of the proposed method over the existing ones: SS-MLA generates the best results in 7 out of 11 comparisons. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).
Citation - Scopus: 5
Topic-Controlled Text Generation
(Institute of Electrical and Electronics Engineers Inc., 2021) Çağlayan,C.; Karakaya,M.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Today, the text generation subject in the field of Natural Language Processing (NLP) has gained a lot of importance. In particular, the quality of the text generated with the emergence of new transformer-based models has reached high levels. In this way, controllable text generation has become an important research area. There are various methods applied for controllable text generation, but since these methods are mostly applied on Recurrent Neural Network (RNN) based encoder decoder models, which were used frequently, studies using transformer-based models are few. Transformer-based models are very successful in long sequences thanks to their parallel working ability. This study aimed to generate Turkish reviews on the desired topics by using a transformer-based language model. We used the method of adding the topic information to the sequential input. We concatenated input token embedding and topic embedding (control) at each time step during the training. As a result, we were able to create Turkish reviews on the specified topics. © 2021 IEEE
Citation - Scopus: 2
An Undergraduate Curriculum for Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2018) Tirkes,G.; Ekin,C.C.; Engul,G.; Bostan,A.; Karakaya,M.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Deep 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. © 2018 IEEE.
An Undergraduate Curriculum for Deep Learning
(Institute of Electrical and Electronics Engineers Inc., 2018) Tirkes,G.; Ekin,C.C.; Engul,G.; Bostan,A.; Karakaya,M.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
Deep 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. © 2018 IEEE.
Yazilim Mühendisliǧi Alaninda Yayimlanan Tez ve Makalelerin Swebok'a Göre Degerlendirilmesi ve Yazilim Mühendisliǧi Eǧitiminin Iyilestirilmesi için Öneriler
(CEUR-WS, 2016) Karakaya,M.; Karakaya, Kasım Murat; Yazici,A.; Yazıcı, Ali; Karakaya, Kasım Murat; Yazıcı, Ali; Computer Engineering; Software Engineering; Software Engineering; Computer Engineering; Software Engineering; 06. School Of Engineering; 01. Atılım University
[No abstract available]
Yazilim Mühendisligi Egitiminde Bitirme Projesinin Yürütülmesinde İki Farkli Yöntemin Ögrenci Bakis Açisiyla Degerlendirilmesi
(CEUR-WS, 2015) Karakaya,M.; Bostan,A.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
[No abstract available]
Yazilim Mühendisliʇi Eʇitiminde Bitirme Projesi Dersinin Öʇrenci Bakiş Açisiyla Deʇerlendirilmesi
(CEUR-WS, 2013) Karakaya,M.; Bostan,A.; Computer Engineering; 06. School Of Engineering; 01. Atılım University
[No abstract available]