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Machine Learning and Computer Vision Laboratory

Machine Learning and Computer Vision Laboratory

About | Our Team | Publications | Projects | Graduates | Contact

 

About

Machine Learning and Computer Vision (MLCV) research group develops advanced AI, AR/VR, and image processing models to solve critical real-world problems. The group addresses challenges across diverse sectors, including healthcare, agriculture, education, energy, biology, intelligent transportation, and more. Our mission is to convert cutting-edge research into robust, high-impact applications that improve efficiency, safety, and quality of life. Our agile team consists of faculty members, research assistants, and a talented group of postgraduate and undergraduate students, ensuring a dedicated workforce to handle challenging research projects.

Our Team

Meet the researchers driving our lab forward:

Recent Publications

  • Demir, E., & Gunal, S. (2025). Short-term electricity consumption forecasting with deep learning. The Journal of Supercomputing, 81(10), 1108.
  • Gunduz, H., & Gunal, S. (2024). A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet. PeerJ Computer Science, 10, e1970.
  • Sidi, M. L., & Gunal, S. (2023). A purely entity-based semantic search approach for document retrieval. Applied Sciences, 13(18), 10285.
  • Pak, M. Y., & Gunal, S. (2022). A model for cross-domain opinion target extraction in sentiment analysis. Computer Systems Science and Engineering, 42(3), 1215-1239.
  • Gunduz, H., Solak, C. N., & Gunal, S. (2022). Segmentation of diatoms using edge detection and deep learning. Turkish Journal of Electrical Engineering and Computer Sciences, 30(6), 2268-2285.
  • Kilicarslan, M., & Temel, T. (2022). Motion-aware vehicle detection in driving videos. Turkish Journal of Electrical Engineering and Computer Sciences, 30(1), 63-78.
  • Kilicarslan, M., & Zheng, J. Y. (2022). DeepStep: Direct detection of walking pedestrian from motion by a vehicle camera. IEEE Transactions on Intelligent Vehicles, 8(2), 1652-1663.
  • Berkaya, S. K., Gunal, E. S., & Gunal, S. (2021). Deep learning-based classification models for beehive monitoring. Ecological Informatics, 101353.
  • Berkaya, S. K., Sivrikoz, I. A., & Gunal, S. (2020). Classification models for SPECT myocardial perfusion imaging. Computers in Biology and Medicine, 123, 103893.

Recent Projects

  • End-to-End Trainable Novel Spatial-Temporal Deep Learning Architecture for Driving Videos, TUBITAK 3501, PI: M. Kilicarslan, 2025-ongoing.
  • Small and Large Language Models in Open-Ended Exam Evaluation: Zero-Shot Learning Approach, ESTU, PI: S. Gunal, 2025-ongoing.
  • Personalized Course Recommendation System Based on Program Outcomes, ESTU, PI: S. Kaplan Berkaya, 2024-ongoing.
  • Automatic Damage Detection on Images, ESTU, PI: S. Kaplan Berkaya, 2024-ongoing.
  • Detection of Dental Diseases from Medical Images, ESTU, PI: S.Kaplan Berkaya, 2024-ongoing.
  • A New Model for Vehicle Speed ​​Estimation in Spatio-Temporal Images, TUBITAK 1002A, PI: M. Kilicarslan, 2023-2024.
  • Electricity Generation and Consumption Forecasting with Deep Learning Models, ESTU, PI: S. Gunal, 2022-2024.
  • Diagnosis of Coronary Artery Diseases Using Image Processing and Deep Learning Approaches on SPECT Myocardial Perfusion Images, ESTU, PI: S. Kaplan Berkaya, 2021-2023.

Graduates

  • Alper Kesli (MSc, 2025)
  • Ozlem Okur (MSc, 2024)
  • Emrah Demir (MSc, 2024)
  • Alper Onrat (MSc, 2024)
  • Sevval Ezgi Eze (MSc, 2024)
  • Mohamed Lemine Sidi (PhD, 2023)
  • Huseyin Gunduz (PhD, 2022)
  • Muhammet Yasin Pak (PhD, 2021)
  • Jose Luis Sandoval Alaguna (MSc, 2020)
  • Selcan Kaplan Berkaya (PhD, 2020)

Contact

We welcome collaboration and inquiries from prospective institutions and researchers. You may contact our team members for further details.

Address: MLCV Lab, Dept. of Computer Engineering, Faculty of Engineering, Eskisehir Technical University, Eskisehir, Turkiye.

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