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Toplam kayıt 19, listelenen: 1-10
Q Learning Regression Neural Network
(Neural Network World, 2018)
In this work, a Nadaraya-Watson kernel based learning system which owns general regression neural network topology is adapted to Q learning method to evaluate a quick and efficient action selection policy for reinforcement ...
A Multistage Deep Learning Algorithm for Detecting Arrhythmia
(Institute of Electrical and Electronics Engineers, 2018)
Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervised and supervised training. Arrhythmia is a cardiac irregularity caused by a problem of the heart. In this study, a multi-stage ...
Performance Comparision of Different Momentum Techniques on Deep Reinforcement Learning
(Institute of Electrical and Electronics Engineers Inc., 2017)
Increase in popularity of deep convolutional neural networks in many different areas leads to increase in the use of these networks in reinforcement learning. Training a huge deep neural network structure by using simple ...
NODIC: a novel distributed clustering routing protocol in WSNs by using a time-sharing approach for CH election
(Springer, 2016)
Due to the battery limitations, energy-efficient routing is one of the most important issues in WSNs. In this paper, a novel distributed clustering routing protocol (NODIC) is proposed. The algorithm makes three main ...
Manipulating attributes of natural scenes via hallucination
(Association for Computing Machinery, 2019)
In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network that can hallucinate images ...
A new approach to early diagnosis of congestive heart failure disease by using Hilbert-Huang transform
(Elsevier Ireland Ltd., 2016)
Congestive heart failure (CHF) is a degree of cardiac disease occurring as a result of the heart's inability to pump enough blood for the human body. In recent studies, coronary artery disease (CAD) is accepted as the most ...
LU triangularization extreme learning machine in EEG cognitive task classification
(Springer, 2019)
Electroencephalography (EEG) has been used as a promising tool for investigation of brain activity during cognitive processes. The aim of this study is to reveal whether EEG signals can be used for classifying cognitive ...
Orthogonal Extreme Learning Machine Based P300 Visual Event-Related BCI
(Springer, 2015)
Brain Computer Interface (BCI) is a type of human-computer relationship research that directly translates electrical activity of brain into commands that can rule equipment and create novel communication channel for muscular ...
Deep Convolutional Generalized Classifier Neural Network
(Springer, 2020)
Up to date technological implementations of deep convolutional neural networks are at the forefront of many issues, such as autonomous device control, effective image and pattern recognition solutions. Deep neural networks ...
Webcam Based Real-Time Robust Optical Mark Recognition
(Springer, 2015)
This study proposes a robust, low cost, real-time optical mark recognition (OMR) system that uses a webcam and a small OMR form to read hand-marked data from plain paper. The system is designed to read data from any ...