A
Adnan Khashman
Researcher at International University, Cambodia
Publications - 76
Citations - 1745
Adnan Khashman is an academic researcher from International University, Cambodia. The author has contributed to research in topics: Artificial neural network & Pattern recognition (psychology). The author has an hindex of 21, co-authored 75 publications receiving 1460 citations. Previous affiliations of Adnan Khashman include Near East University & University of Nicosia.
Papers
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Deep learning in vision-based static hand gesture recognition
TL;DR: This work proposes applying deep learning to the problem of hand gesture recognition for the whole 24 hand gestures obtained from the Thomas Moeslund's gesture recognition database and shows that more biologically inspired and deep neural networks are capable of learning the complex hand gesture classification task with lower error rates.
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Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes
TL;DR: A credit risk evaluation system that uses supervised neural network models based on the back propagation learning algorithm to decide whether to approve or reject a credit application is described.
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A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients
TL;DR: Experimental results show that the addition of the two novel emotional parameters improves the performance of the neural network yielding higher recognition rates and faster recognition time.
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Credit risk evaluation using neural networks: Emotional versus conventional models
TL;DR: Experimental results suggest that both emotional and conventional neural models can be used effectively for credit risk evaluations, however the emotional models outperform their conventional counterparts in decision making speed and accuracy, making them ideal for implementation in fast automatic processing of credit applications.
Journal Article
Image compression using neural networks and haar wavelet
Adnan Khashman,Kamil Dimililer +1 more
TL;DR: It is suggested that a neural network could be trained to recognize an optimum ratio for Haar wavelet compression of an image upon presenting the image to the network.