Browsing by Author Nguyen, Linh Trung

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  • Authors: Le, Thanh Xuyen; Le, Trung Thanh; Dinh, Van Viet; Tran, Quoc Long; Nguyen, Linh Trung; Nguyen, Duc Thuan (2017)

  • In the clinical diagnosis of epilepsy using electroencephalogram (EEG) data, an accurate automatic epileptic spikes detection system is highly useful and meaningful in that the conventional manual process is not only very tedious and time-consuming, but also subjective since it depends on the knowledge and experience of the doctors. In this paper, motivated by significant advantages and lots of achieved successes of deep learning in data mining, we apply Deep Belief Network (DBN), which is one of the breakthrough models laid the foundation for deep learning, to detect epileptic spikes in EEG data. It is really useful in practice because the promising quality evaluation of the spike de...

  • IEEE%20Xplore%20Abstract%20-%20Fast%20adaptive%20PARAFAC%20decomposition%20algorithm%20with%20linear%20complexity.pdf.jpg
  • Article


  • Authors: Nguyen, Viet Dung; Karim, Abed-Meraim; Nguyen, Linh Trung (2016)

  • We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has linear complexity, good convergence rate and good estimation accuracy. To deal with large-scale problems, a parallel implementation can be applied to reduce both computational complexity and storage. We illustrate the effectiveness of our algorithm in comparison with the state-of-the-art algorithms through simulation experiments.

Browsing by Author Nguyen, Linh Trung

Jump to: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
or enter first few letters:  
Showing results 1 to 2 of 2
  • c33.2.1.pdf.jpg
  • Article


  • Authors: Le, Thanh Xuyen; Le, Trung Thanh; Dinh, Van Viet; Tran, Quoc Long; Nguyen, Linh Trung; Nguyen, Duc Thuan (2017)

  • In the clinical diagnosis of epilepsy using electroencephalogram (EEG) data, an accurate automatic epileptic spikes detection system is highly useful and meaningful in that the conventional manual process is not only very tedious and time-consuming, but also subjective since it depends on the knowledge and experience of the doctors. In this paper, motivated by significant advantages and lots of achieved successes of deep learning in data mining, we apply Deep Belief Network (DBN), which is one of the breakthrough models laid the foundation for deep learning, to detect epileptic spikes in EEG data. It is really useful in practice because the promising quality evaluation of the spike de...

  • IEEE%20Xplore%20Abstract%20-%20Fast%20adaptive%20PARAFAC%20decomposition%20algorithm%20with%20linear%20complexity.pdf.jpg
  • Article


  • Authors: Nguyen, Viet Dung; Karim, Abed-Meraim; Nguyen, Linh Trung (2016)

  • We present a fast adaptive PARAFAC decomposition algorithm with low computational complexity. The proposed algorithm generalizes the Orthonormal Projection Approximation Subspace Tracking (OPAST) approach for tracking a class of third-order tensors which have one dimension growing with time. It has linear complexity, good convergence rate and good estimation accuracy. To deal with large-scale problems, a parallel implementation can be applied to reduce both computational complexity and storage. We illustrate the effectiveness of our algorithm in comparison with the state-of-the-art algorithms through simulation experiments.