Browsing by Author "Tumuklu Ozyer, G.T."

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Balancing İnverted Pendulum Using Reinforcement Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2016) Özakar, R.; Tumuklu Ozyer, G.T.; Ozyer, B.
With the advancements in technology, robots has become systems that can learn and achieve complex behaviors in real life with the help of machine learning algorithms. Among those algorithms, reinforcement learning algorithms are widely used in robotics to teach the systems by trials and errors. In this work, our goal is to use the two different reinforcement algorithms, Q-learning and Adaptive Heuristic Critic (AHC) algorithm, on well-known cart-pole balancing problem and examine the performance results. We used Box2d physics engine simulator to simulate the cart-pole model and the environment. Observing the experimental results, AHC algorithm was able to balance the system for more step counts than Q-learning algorithm. © 2016 IEEE.
Recognizing Self-Stimulatory Behaviours for Autism Spectrum Disorders
(Institute of Electrical and Electronics Engineers Inc., 2020) Kacdioglu, S.; Ozyer, B.; Tumuklu Ozyer, G.T.
Autism spectrum disorder (ASD) is a neurobiological disorder that some symptoms such as deficit of social interaction and communication, limited and repeated behavior are observed in patients. Repetitive behaviors are signicant clues for diagnosis of ASD. These repetitive behaviors, which is called self-stimulating behaviors, are described as flapping arms like wings, shaking head back and forth, and spinning around itself. Physicians should observe and examine these selfstimulating behaviors by interacting with children for a long time that makes it difficult in early diagnosis of ASD. In this paper, the self-stimulating behaviors of ASD children are examined using deep learning algorithms. For this purpose, a new video dataset recorded by parents in daily environment without being dependent on the hospital environment are created. Video features are extracted using 3DCNN and ConvLSTM deep learning algorithms. Softmax regression is applied as a classifier. As a result of the experiments performed, 75,93 % accuracy is obtained even if the videos are recorded in the daily environment. © 2020 IEEE.
Skelresnet: Transfer Learning Approach for Skeleton-Based Action Recognition
(Institute of Electrical and Electronics Engineers Inc., 2024) Kiliç, U.; Öztimur Karadaǧ, Ö.Ö.; Tumuklu Ozyer, G.T.
Skeleton-based action recognition is an increasingly popular research area in computer vision that analyzes the spatial configuration and temporal dynamics of human action. Learning distinctive spatial and temporal features for skeleton-based action recognition is one of the main challenges in this field. For this purpose, various deep learning methods such as CNN, RNN, GCN and Transformer have been used in the literature. Although these methods can achieve high performance, they require high computational costs and large datasets due to their complexity. Transfer learning is an approach that can be used to overcome this problem. In transfer learning, a pre-trained model can be fine-tuned for a new task. In this way, the computational cost can be reduced and high performance can be achieved with less data. In this study, SkelResNet architecture is designed based on the pre-trained ResNet101 model. Four different image representations were created using skeletal data to meet the input requirements of the SkelResNet architecture. Experimental studies have shown that SkelResNet outperforms CNN-based methods in the existing literature in action recognition. © 2024 IEEE.