Karakaya, Kasım Murat

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Profile URL
https://hdl.handle.net/20.500.14411/8124
Name Variants
Karakaya, Murat
Karakaya, Kasım Murat
K., Kasim Murat
K.,Kasım Murat
Karakaya,K.M.
Kasim Murat, Karakaya
K., Karakaya
K.,Kasim Murat
Karakaya, Kasim Murat
Kasım Murat, Karakaya
K.M.Karakaya
K.,Karakaya
Karakaya,M.
Karakaya,M.
Job Title
Profesör Doktor
Email Address
murat.karakaya@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
4
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

44

Articles

20

Views / Downloads

30/42

Supervised MSc Theses

4

Supervised PhD Theses

0

WoS Citation Count

142

Scopus Citation Count

205

Patents

0

Projects

0

WoS Citations per Publication

3.23

Scopus Citations per Publication

4.66

Open Access Source

4

Supervised Theses

4

JournalCount
UBMK 2018 - 3rd International Conference on Computer Science and Engineering -- 3rd International Conference on Computer Science and Engineering, UBMK 2018 -- 20 September 2018 through 23 September 2018 -- Sarajevo -- 1435604
2017 IEEE 1st Ukraine Conference on Electrical and Computer Engineering, UKRCON 2017 - Proceedings -- 1st IEEE Ukraine Conference on Electrical and Computer Engineering, UKRCON 2017 -- 29 May 2017 through 2 June 2017 -- Kyiv -- 1317632
Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 15 September 2021 through 17 September 2021 -- Ankara -- 1768262
3rd International Conference on Computer Science and Engineering (UBMK) -- SEP 20-23, 2018 -- Sarajevo, BOSNIA & HERCEG2
1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019 - Proceedings -- 1st International Informatics and Software Engineering Conference, IISEC 2019 -- 6 November 2019 through 7 November 2019 -- Ankara -- 1571112
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Author: Karakaya,M.End date: 2022

Scholarly Output Search Results

Now showing 1 - 10 of 16
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Detecting Errors in Automatic Image Captioning by Deep Learning;
    (Institute of Electrical and Electronics Engineers Inc., 2021) Karakaya,M.
    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
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    Conference Object
    Citation - Scopus: 6
    Topic-Controlled Text Generation
    (Institute of Electrical and Electronics Engineers Inc., 2021) Çağlayan,C.; Karakaya,M.
    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
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    Article
    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.
    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).
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    Conference Object
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    Conference Object
    Yazilim Mühendisliʇi Eʇitiminde Bitirme Projesi Dersinin Öʇrenci Bakiş Açisiyla Deʇerlendirilmesi
    (CEUR-WS, 2013) Karakaya,M.; Bostan,A.
    [No abstract available]
  • Thumbnail Image
    Conference Object
    A Case Study on Measuring Course Learning Outcomes in Software Engineering Education;
    (CEUR-WS, 2017) Karakaya,M.; Turhan,C.; Yazici,A.
    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.
  • Thumbnail Image
    Conference Object
    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.
    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.
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    Conference Object
    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.
    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.
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    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.
    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.
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    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.
    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.