Yazar
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An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things
Nakip, M.; Karakayali, K.; Guzelis, C.; Rodoplu, V. (Institute of Electrical and Electronics Engineers Inc., 2021)We develop a novel end-to-end trainable feature selection-forecasting (FSF) architecture for predictive networks targeted at the Internet of Things (IoT). In contrast with the existing filter-based, wrapper-based and ... -
Learning to move an object by the humanoid robots by using deep reinforcement learning
Aslan, S.N.; Tasci, B.; Ucar, A.; Guzelis, C. (IOS Press, 2021)This paper proposes an algorithm for learning to move the desired object by humanoid robots. In this algorithm, the semantic segmentation algorithm and Deep Reinforcement Learning (DRL) algorithms are combined. The semantic ... -
New convolutional neural network models for efficient object recognition with humanoid robots
Aslan, S.N.; Ucar, A.; Guzelis, C. (Taylor and Francis Inc., 2021)Humanoid robots are expected to manipulate the objects they have not previously seen in real-life environments. Hence, it is important that the robots have the object recognition capability. However, object recognition is ... -
Subspace-Based Emulation of the Relationship between Forecasting Error and Network Performance in Joint Forecasting-Scheduling for the Internet of Things
Nakip, M.; Helva, A.; Guzelis, C.; Rodoplu, V. (IEEE, 2021)We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology, an Artificial Neural Network (ANN) learns ...
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