Karakaya, Kasım Murat

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
No research topics data found.

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
1
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
4
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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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.
No records found in other affiliations.
Scholarly Output

44

Articles

20

Views / Downloads

32/53

Supervised MSc Theses

4

Supervised PhD Theses

0

WoS Citation Count

142

Scopus Citation Count

206

Patents

0

Projects

0

WoS Citations per Publication

3.23

Scopus Citations per Publication

4.68

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 of Publication: search.filters.isAuthorOfPublication.93f27ee1-19eb-42dc-b4eb-a3cc7dc4b057

Scholarly Output Search Results

Now showing 1 - 10 of 44
  • Article
    Citation - WoS: 50
    Citation - Scopus: 71
    Deep Learning Based Fall Detection Using Smartwatches for Healthcare Applications
    (Elsevier Sci Ltd, 2022) Sengul, Gokhan; Karakaya, Murat; Misra, Sanjay; Abayomi-Alli, Olusola O.; Damasevicius, Robertas
    We implement a smart watch-based system to predict fall detection. We differentiate fall detection from four common daily activities: sitting, squatting, running, and walking. Moreover, we separate falling into falling from a chair and falling from a standing position. We develop a mobile application that collects the acceleration and gyroscope sensor data and transfers them to the cloud. In the cloud, we implement a deep learning algorithm to classify the activity according to the given classes. To increase the number of data samples available for training, we use the Bica cubic Hermite interpolation, which allows us to improve the accuracy of the neural network. The 38 statistical data features were calculated using the rolling update approach and used as input to the classifier. For activity classification, we have adopted the bi-directional long short-term memory (BiLSTM) neural network. The results demonstrate that our system can detect falling with an accuracy of 99.59% (using leave-one-activityout cross-validation) and 97.35% (using leave-one-subject-out cross-validation) considering all activities. When considering only binary classification (falling vs. all other activities), perfect accuracy is achieved.
  • Conference Object
  • Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Deadline-Aware Energy-Efficient Query Scheduling in Wireless Sensor Networks With Mobile Sink
    (Hindawi Ltd, 2013) Karakaya, Murat
    Mobile sinks are proposed to save sensor energy spent for multihop communication in transferring data to a base station (sink) in Wireless Sensor Networks. Due to relative low speed of mobile sinks, these approaches are mostly suitable for delay-tolerant applications. In this paper, we study the design of a query scheduling algorithmfor query-based data gathering applications using mobile sinks. However, these kinds of applications are sensitive to delays due to specified query deadlines. Thus, the proposed scheduling algorithm aims to minimize the number of missed deadlines while keeping the level of energy consumption at the minimum.
  • Conference Object
    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.
    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.
  • Master Thesis
    Kontrollü Çok Konulu Metin Üretimi için Yeni Bir Derin Öğrenme Yaklaşımı
    (2022) Çağlayan, Cansen; Karakaya, Kasım Murat
    One of the most important tasks in the Controllable Text Generation (CTG) domain is to create topic-controlled texts. In this study, we propose and design three different approaches, and conduct extensive experiments on them to observe the performance of the controlled multi-topic reviews generated in Turkish. In the first approach, we generate controlled multi-topic text using a single-layer GPT language model by incorporating several control techniques. To control the language model, we first add topic information to the sequential input, as a second technique we add the automatically extracted keywords for each topic to the sequential input in addition to the first technique. The last technique that we propose is a novel sampling strategy. We propose to use a topic selection classifier that enables the next token selection according to the probability of the selected tokens being on the desired topic. Then, we apply these approaches to a more advanced language model, the multi-layer GPT, and interpret the results. In addition to these experiments, we compare three different deep learning text classification models in order to create a reliable multi-topic review classifier.
  • Master Thesis
    Havada Kalış Süresi Kısıtlı Seyir Halindeki Gemiye Konuşlu İnsansız Hava Aracının Hedef En Çoklaması
    (2015) Savuran, Halil; Karakaya, Kasım Murat
    Bu tez seyir halindeki bir gemi üzerine konuşlu, menzil kısıtlı bir insansız hava aracının (İHA) hedef en çoklaması problemi için bir çözüm yaklaşımı önermektedir. Bu problem, Araç Rotalama Probleminin (ARP) menzil kapasitesi ve depo mobilitesi kısıtlarıyla genişletilmesiyle modellenmekte ve bu özgün genişletme çalışma boyunca Kapasite kısıtlı Mobil Depo ARP (K-MoDARP) olarak tanımlanmaktadır. Bu problemin doğasının dikte ettiği özgün çözüm gereksinimi probleme özel kısıtlar için uyarlanmış bir genetik algoritma (GA-KMoD) ile karşılanmaktadır. Tezde, çalışmanın amacı, problem tanımı ve önerilen çözümün geliştirme ve uygulaması anlatılmıştır. Ayrıca, önerilen GA-KMoD'un performansı farklı problem kıstasları için yoğun benzetim testleri vasıtasıyla değerlendirilmiştir. GA-KMoD'un ürettiği rotaların kalitesi alternatif yöntemlerle üretilen sonuçlarla karşılaştırılmıştır. Deneysel sonuçlar, önerilen çözüm yönteminin alternatif yöntemlere göre olan üstünlüğünü net bir şekilde ortaya koymaktadır.
  • Article
    Harmanlanmış Öğrenme Ortamlarında Sosyal Medya Kullanımının Öğrenci Doyumu Üzerine Etkisi
    (2018) Eryılmaz, Meltem; Karakaya, Kasım Murat
    Bu deneysel araştırmanın amacı sosyal medya kullanımının harmanlanmış öğrenmeortamlarında öğrenci doyumu üzerindeki etkisini belirlemektir. Araştırma Ankara’daki birüniversitede okuyan ve bilgisayara giriş dersi alan 109 öğrenci üzerinde uygulanmıştır.Uygulama sürecinde öğrenciler dersi haftada bir kez yüz yüze geri kalanı çevirimiçi olmaküzere harmanlanmış öğrenme yaklaşımı ile almışlardır. Çevirimiçi öğrenme ortamı dersmateryallerinin, forum, sınav, ders notu, resim ve video destekli ders özetlerinin paylaşıldığıbir ortam olarak tasarlanmıştır. Araştırmada, çevirimiçi öğrenme ortamlarını desteklemeküzere tasarlanmış bir sosyal medya ortamı sunan Course Networking (CN) adlı sistemkullanılmıştır. Seçilen dersin ilk 7 haftasında ders sadece Moodle sistemi ile desteklenirken,8nci haftadan sonra CN sistemi kullanıma açılmıştır. 7nci ve 15nci haftalarda yapılan ölçek veanket uygulamaları ile öğrencilerin bu dersten aldıkları tatmin duygusu ve harmanlanmışöğrenme ortamlarında sosyal medya kullanımının öğrencilerin algıları üzerine etkileriincelenmiştir.
  • Article
    Using Bluetooth Low Energy Beacons for Indoor Localization
    (International Journal of Intelligent Systems and Applications in Engineering, 2017) Şengül, Gökhan; Karakaya, Murat
    Bluetooth Low Energy (BLE) Beacons gain high popularity due to their low consumption of energy and, thereby, long lifetime. Using the BLE protocol, these devices emit advertisement packets at fixed intervals for a short duration. Indoor localization solutions aim to provide an accurate, low cost estimate of sub-room indoor positioning. There are various techniques proposed for this purpose. BLE Beacons are good hardware candidates to assist the creation of such indoor localization solutions. Given the exact position of BLE Beacons, one can attempt to estimate a receiver position according to the received signal power. In this work, we investigated the success of such an indoor localization approach employing multiple BLE Beacons and two different estimation techniques. The results of the experiments indicate that employing multiple BLE Beacons increases the success of prediction techniques considerably.
  • Article
    A Smart Classroom Application: Monitoring and Reporting Attendance Automatically Using Smart Devices
    (International Journal of Scientific Research in Information Systems and Engineering, 2017) Şengül, Gökhan; Karakaya, Murat; Bostan, Atila
    For recording attendance in a classroom, generally instructors collect signatures of the attendees. Then, at the end of the semester, those signatures need to be counted and reported. This process causes waste of time and effort for both instructors and attendees. Besides this process is very error prone. Moreover, in crowded classes, there could be some misuses of this process. In this study, a smart classroom application is proposed and developed in order to monitor the attendance of the students in a classroom environment. In the design, a low-energy Bluetooth device is located at each classroom. Identification number (ID) of the low-energy Bluetooth device and the name/number of the classroom that the device is located are matched and stored in a central database. In addition to this information, the name of the courses given in that classroom and their time tables are also stored in the central database. Thus, in the database, the weekly course schedule of the classrooms is available. In addition to this central database infrastructure, a mobile application is developed that can run on both in mobile phones and smart watches. The users first install the application on their own smart devices. Whenever an attendee enters to a classroom, the smart device and its application interacts with the low-energy Bluetooth device. The student’s identification number (Student ID: SID), the identification number (ID) of the low-energy Bluetooth device located at the class, the day and time of the interaction are sent to the central database by the smart device. Using this information, the name of the attendee and the courses that he/she attended are matched using the SID of the attendee, the ID of the low-energy Bluetooth device, the day and time of the interaction. Those matching information are also stored in the central database. The records in the central database are used to create any automatic reports, i.e. the attendance status, the time and duration of the attendance, and the classroom (course) of the record. The advantage of the proposed system is that it is a fully automatic system that records the presence of the students, generates automatic attendance reports, does not require any extra device except installing a mobile application onto smart phones or smart watches of the student, and can be deployed with a low budget. The proposed system is tested in real classroom environment and it is proven to be operational.
  • 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.