Abstract: Classification of electroencephalogram (EEG) signals
extracted during mental tasks is a technique that is actively pursued
for Brain Computer Interfaces (BCI) designs. In this paper, we
compared the classification performances of univariateautoregressive
(AR) and multivariate autoregressive (MAR) models
for representing EEG signals that were extracted during different
mental tasks. Multilayer Perceptron (MLP) neural network (NN)
trained by the backpropagation (BP) algorithm was used to classify
these features into the different categories representing the mental
tasks. Classification performances were also compared across
different mental task combinations and 2 sets of hidden units (HU): 2
to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different
mental tasks from 4 subjects were used in the experimental study and
combinations of 2 different mental tasks were studied for each
subject. Three different feature extraction methods with 6th order
were used to extract features from these EEG signals: AR
coefficients computed with Burg-s algorithm (ARBG), AR
coefficients computed with stepwise least square algorithm (ARLS)
and MAR coefficients computed with stepwise least square
algorithm. The best results were obtained with 20 to 100 HU using
ARBG. It is concluded that i) it is important to choose the suitable
mental tasks for different individuals for a successful BCI design, ii)
higher HU are more suitable and iii) ARBG is the most suitable
feature extraction method.
Abstract: Water quality is a subject of ongoing concern.
Deterioration of water quality has initiated serious management
efforts in many countries. This study endeavors to automatically
classify water quality. The water quality classes are evaluated using 6
factor indices. These factors are pH value (pH), Dissolved Oxygen
(DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen
(NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform).
The methodology involves applying data mining
techniques using multilayer perceptron (MLP) neural network
models. The data consisted of 11 sites of canals in Dusit district in
Bangkok, Thailand. The data is obtained from the Department of
Drainage and Sewerage Bangkok Metropolitan Administration
during 2007-2011. The results of multilayer perceptron neural
network exhibit a high accuracy multilayer perception rate at 96.52%
in classifying the water quality of Dusit district canal in Bangkok
Subsequently, this encouraging result could be applied with plan and
management source of water quality.
Abstract: In this paper in consideration of each available
techniques deficiencies for speech recognition, an advanced method
is presented that-s able to classify speech signals with the high
accuracy (98%) at the minimum time. In the presented method, first,
the recorded signal is preprocessed that this section includes
denoising with Mels Frequency Cepstral Analysis and feature
extraction using discrete wavelet transform (DWT) coefficients; Then
these features are fed to Multilayer Perceptron (MLP) network for
classification. Finally, after training of neural network effective
features are selected with UTA algorithm.