Areas of Interest
Artificial Intelligence, Neural Networks, Data Analytics, Meta-learning, Data Fusion
Personal Research Links
Google Scholar https://scholar.google.com/citations?user=OzUKGD0AAAAJ
Orcid: https://orcid.org/0000-0003-2405-3978
Github: https://github.com/ozggultekin
Education
Doctoral Degree (Ph.D.) (2019 – Continues)
Eskisehir Osmangazi University, Graduate School of Natural and Applied Science, Computer Engineering, Turkey (CGPA: 4.00 / 4.00)
Bachelor’s Degree (2016 – 2019)
Eskisehir Osmangazi University, Faculty of Engineering and Architecture, Department of Computer Engineering, Turkey (CGPA: 3.96 / 4.00)
Master’s Degree (2010 – 2011)
İhsan Dogramaci Bilkent University, Institute of Educational Sciences, Computer and Instructional Technology Teacher Education, Turkey (CGPA: 3.62 / 4.00)
Bachelor’s Degree (2005 – 2010)
İhsan Dogramaci Bilkent University, Faculty of Education, Department of Computer and Instructional Technology Teacher Education, Turkey (CGPA: 3.51 / 4.00)
Rewards and Achievements
TÜBİTAK 2211-A National Ph.D. Scholarship Program (2020 – 2024)
Highest Ranked Student – Department of Computer Engineering (2018-2019 Academic Year)
TÜBİTAK 2209-B – Industry Oriented Research Project Support Program for Undergraduate Students (2018/3)
Selected Publications
Gültekin, Ö., Çinar, E., Özkan, K., & Yazıcı, A. (2022). A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images. Neural Computing and Applications, 34(6), 4803-4812. Doi: https://doi.org/10.1007/s00521-021-06668-2
Gültekin, Ö., Cinar, E., Özkan, K., & Yazıcı, A. (2022). Multisensory data fusion-based deep learning approach for fault diagnosis of an industrial autonomous transfer vehicle. Expert Systems with Applications, 200, 117055. Doi: https://doi.org/10.1016/j.eswa.2022.117055
Gültekin, Ö., Cinar, E., Özkan, K., & Yazıcı, A. (2022). Real-time fault detection and condition monitoring for industrial autonomous transfer vehicles utilizing edge artificial intelligence. Sensors, 22(9), 3208. Doi: https://doi.org/10.3390/s22093208