Abstract: In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.
Abstract: In this paper we are to find the optimum multiwavelet for compression of electrocardiogram (ECG) signals and then, selecting it for using with SPIHT codec. At present, it is not well known which multiwavelet is the best choice for optimum compression of ECG. In this work, we examine different multiwavelets on 24 sets of ECG data with entirely different characteristics, selected from MIT-BIH database. For assessing the functionality of the different multiwavelets in compressing ECG signals, in addition to known factors such as Compression Ratio (CR), Percent Root Difference (PRD), Distortion (D), Root Mean Square Error (RMSE) in compression literature, we also employed the Cross Correlation (CC) criterion for studying the morphological relations between the reconstructed and the original ECG signal and Signal to reconstruction Noise Ratio (SNR). The simulation results show that the Cardinal Balanced Multiwavelet (cardbal2) by the means of identity (Id) prefiltering method to be the best effective transformation. After finding the most efficient multiwavelet, we apply SPIHT coding algorithm on the transformed signal by this multiwavelet.
Abstract: Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.
Abstract: The Principal component regression (PCR) is a
combination of principal component analysis (PCA) and multiple linear regression (MLR). The objective of this paper is to revise the
use of PCR in shortwave near infrared (SWNIR) (750-1000nm) spectral analysis. The idea of PCR was explained mathematically and
implemented in the non-destructive assessment of the soluble solid
content (SSC) of pineapple based on SWNIR spectral data. PCR achieved satisfactory results in this application with root mean
squared error of calibration (RMSEC) of 0.7611 Brix°, coefficient of determination (R2) of 0.5865 and root mean squared error of crossvalidation
(RMSECV) of 0.8323 Brix° with principal components
(PCs) of 14.
Abstract: Prediction of highly non linear behavior of suspended
sediment flow in rivers has prime importance in the field of water
resources engineering. In this study the predictive performance of
two Artificial Neural Networks (ANNs) namely, the Radial Basis
Function (RBF) Network and the Multi Layer Feed Forward (MLFF)
Network have been compared. Time series data of daily suspended
sediment discharge and water discharge at Pari River was used for
training and testing the networks. A number of statistical parameters
i.e. root mean square error (RMSE), mean absolute error (MAE),
coefficient of efficiency (CE) and coefficient of determination (R2)
were used for performance evaluation of the models. Both the models
produced satisfactory results and showed a good agreement between
the predicted and observed data. The RBF network model provided
slightly better results than the MLFF network model in predicting
suspended sediment discharge.
Abstract: In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
Abstract: Understanding patient factors related to physical activity behavior is important in the management of Type 2 Diabetes. This study applied the Theory of Planned Behavior model to understand physical activity behavior among sampled Type 2 diabetics in Kenya. The study was conducted within the diabetic clinic at Kisii Level 5 Hospital and adopted sequential mixed methods design beginning with qualitative phase and ending with quantitative phase. Qualitative data was analyzed using grounded theory analysis method. Structural equation modeling using maximum likelihood was used to analyze quantitative data. The common fit indices revealed that the theory of planned behavior fitted the data acceptably well among the Type 2 diabetes and within physical activity behavior {¤ç2 = 213, df = 84, n=230, p = .061, ¤ç2/df = 2.53; TLI = .97; CFI =.96; RMSEA (90CI) = .073(.029, .08)}. This theory proved to be useful in understanding physical activity behavior among Type 2 diabetics.
Abstract: In this paper we are to find the optimum
multiwavelet for compression of electrocardiogram (ECG)
signals. At present, it is not well known which multiwavelet is
the best choice for optimum compression of ECG. In this
work, we examine different multiwavelets on 24 sets of ECG
data with entirely different characteristics, selected from MITBIH
database. For assessing the functionality of the different
multiwavelets in compressing ECG signals, in addition to
known factors such as Compression Ratio (CR), Percent Root
Difference (PRD), Distortion (D), Root Mean Square Error
(RMSE) in compression literature, we also employed the
Cross Correlation (CC) criterion for studying the
morphological relations between the reconstructed and the
original ECG signal and Signal to reconstruction Noise Ratio
(SNR). The simulation results show that the cardbal2 by the
means of identity (Id) prefiltering method to be the best
effective transformation.
Abstract: In the oil and gas industry, energy prediction can help
the distributor and customer to forecast the outgoing and incoming
gas through the pipeline. It will also help to eliminate any
uncertainties in gas metering for billing purposes. The objective of
this paper is to develop Neural Network Model for energy
consumption and analyze the performance model. This paper
provides a comprehensive review on published research on the
energy consumption prediction which focuses on structures and the
parameters used in developing Neural Network models. This paper is
then focused on the parameter selection of the neural network
prediction model development for energy consumption and analysis
on the result. The most reliable model that gives the most accurate
result is proposed for the prediction. The result shows that the
proposed neural network energy prediction model is able to
demonstrate an adequate performance with least Root Mean Square
Error.
Abstract: Investigation of soil properties like Cation Exchange
Capacity (CEC) plays important roles in study of environmental
reaserches as the spatial and temporal variability of this property
have been led to development of indirect methods in estimation of
this soil characteristic. Pedotransfer functions (PTFs) provide an
alternative by estimating soil parameters from more readily available
soil data. 70 soil samples were collected from different horizons of
15 soil profiles located in the Ziaran region, Qazvin province, Iran.
Then, multivariate regression and neural network model (feedforward
back propagation network) were employed to develop a
pedotransfer function for predicting soil parameter using easily
measurable characteristics of clay and organic carbon. The
performance of the multivariate regression and neural network model
was evaluated using a test data set. In order to evaluate the models,
root mean square error (RMSE) was used. The value of RMSE and
R2 derived by ANN model for CEC were 0.47 and 0.94 respectively,
while these parameters for multivariate regression model were 0.65
and 0.88 respectively. Results showed that artificial neural network
with seven neurons in hidden layer had better performance in
predicting soil cation exchange capacity than multivariate regression.
Abstract: Image registration plays an important role in the
diagnosis of dental pathologies such as dental caries, alveolar bone
loss and periapical lesions etc. This paper presents a new wavelet
based algorithm for registering noisy and poor contrast dental x-rays.
Proposed algorithm has two stages. First stage is a preprocessing
stage, removes the noise from the x-ray images. Gaussian filter has
been used. Second stage is a geometric transformation stage.
Proposed work uses two levels of affine transformation. Wavelet
coefficients are correlated instead of gray values. Algorithm has been
applied on number of pre and post RCT (Root canal treatment)
periapical radiographs. Root Mean Square Error (RMSE) and
Correlation coefficients (CC) are used for quantitative evaluation.
Proposed technique outperforms conventional Multiresolution
strategy based image registration technique and manual registration
technique.
Abstract: This paper evaluates the performance of a novel
algorithm for tracking of a mobile node, interms of execution time
and root mean square error (RMSE). Particle Filter algorithm is used
to track the mobile node, however a new technique in particle filter
algorithm is also proposed to reduce the execution time. The
stationary points were calculated through trilateration and finally by
averaging the number of points collected for a specific time, whereas
tracking is done through trilateration as well as particle filter
algorithm. Wi-Fi signal is used to get initial guess of the position of
mobile node in x-y coordinates system. Commercially available
software “Wireless Mon" was used to read the WiFi signal strength
from the WiFi card. Visual Cµ version 6 was used to interact with
this software to read only the required data from the log-file
generated by “Wireless Mon" software. Results are evaluated through
mathematical modeling and MATLAB simulation.
Abstract: This work attempts to improve the permselectivity of poly-ortho-phenylenediamine (PPD) coating for glutamate biosensor applications on Pt microelectrode, using constant potential amperometry and cyclic voltammetry. Percentage permeability of the modified PPD microelectrode was carried out towards hydrogen peroxide (H2O2) and ascorbic acid (AA) whereas permselectivity represents the percentage interference by AA in H2O2 detection. The 50-μm diameter Pt disk microelectrode showed a good permeability value toward H2O2 (95%) and selectivity against AA (0.01%) compared to other sizes of electrode studied here. The electrode was further modified with glutamate oxidase (GluOx) that was immobilized and cross linked with glutaraldehyde (GA, 0.125%), resulting in Pt/PPD/GluOx-GA electrode design. The maximum current density Jmax and apparent Michaelis constant, KM, obtained on Pt/PPD/GluOx-GA electrodes were 48 μA cm-2 and 50 μM, respectively. The linear region slope (LRS) was 0.96 μA cm-2 mM-1. The detection limit (LOD) for glutamate was 3.0 ± 0.6 μM. This study shows a promising glutamate microbiosensor for brain glutamate detection.
Abstract: This study aimed to investigate the influence of selected antecedents, which were tourists’ satisfaction towards attractions in Bangkok, perceived value of the attractions, feelings of engagement with the attractions, acquaintance with the attractions, push factors, pull factors and motivation to seek novelty, on foreign tourist’s loyalty towards tourist attractions in Bangkok. By using multi stage sampling technique, 400 international tourists were sampled. After that, Semi Structural Equation Model was utilized in the analysis stage by LISREL. The Semi Structural Equation Model of the selected antecedents of tourist’s loyalty attractions had a correlation with the empirical data through the following statistical descriptions: Chi- square = 3.43, df = 4, P- value = 0.48893; RMSEA = 0.000; CFI = 1.00; CN = 1539.75; RMR = 0.0022; GFI = 1.00 and AGFI = 0.98. The findings indicated that all antecedents were able together to predict the loyalty of the foreign tourists who visited Bangkok at 73 percent.
Abstract: Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000 .The RMSEP for phenol, resorcinol and catechol with PCranking- ANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml-1 for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples.
Abstract: Well-being has been given special emphasis in quality
of life. It involves living a meaningful, life satisfaction, stability and
happiness in life. Well-being also concerns the satisfaction of
physical, psychological, social needs and demands of an individual.
The purpose of this study was to validate three-factor measurement
model of well-being using structural equation modeling (SEM). The
conceptions of well-being measured such dimensions as physical,
psychological and social well-being. This study was done based on a
total sample of 650 adolescents from east-coast of peninsular
Malaysia. The Well-Being Scales which was adapted from [1] was
used in this study. The items were hypothesized a priori to have nonzero
loadings on all dimensions in the model. The findings of the
SEM demonstrated that it is a good fitting model which the proposed
model fits the driving theory; (x2df = 1.268; GFI = .994; CFI = .998;
TLI= .996; p = .255; RMSEA = .021). Composite reliability (CR)
was .93 and average variance extracted (AVE) was 58%. The model
in this study fits with the sample of data and well-being is important
to bring sustainable development to the mainstream.
Abstract: As in today's semiconductor industries test costs can make up to 50 percent of the total production costs, an efficient test error detection becomes more and more important. In this paper, we present a new machine learning approach to test error detection that should provide a faster recognition of test system faults as well as an improved test error recall. The key idea is to learn a classifier ensemble, detecting typical test error patterns in wafer test results immediately after finishing these tests. Since test error detection has not yet been discussed in the machine learning community, we define central problem-relevant terms and provide an analysis of important domain properties. Finally, we present comparative studies reflecting the failure detection performance of three individual classifiers and three ensemble methods based upon them. As base classifiers we chose a decision tree learner, a support vector machine and a Bayesian network, while the compared ensemble methods were simple and weighted majority vote as well as stacking. For the evaluation, we used cross validation and a specially designed practical simulation. By implementing our approach in a semiconductor test department for the observation of two products, we proofed its practical applicability.
Abstract: A concern that researchers usually face in different
applications of Artificial Neural Network (ANN) is determination of
the size of effective domain in time series. In this paper, trial and
error method was used on groundwater depth time series to determine
the size of effective domain in the series in an observation well in
Union County, New Jersey, U.S. different domains of 20, 40, 60, 80,
100, and 120 preceding day were examined and the 80 days was
considered as effective length of the domain. Data sets in different
domains were fed to a Feed Forward Back Propagation ANN with
one hidden layer and the groundwater depths were forecasted. Root
Mean Square Error (RMSE) and the correlation factor (R2) of
estimated and observed groundwater depths for all domains were
determined. In general, groundwater depth forecast improved, as
evidenced by lower RMSEs and higher R2s, when the domain length
increased from 20 to 120. However, 80 days was selected as the
effective domain because the improvement was less than 1% beyond
that. Forecasted ground water depths utilizing measured daily data
(set #1) and data averaged over the effective domain (set #2) were
compared. It was postulated that more accurate nature of measured
daily data was the reason for a better forecast with lower RMSE
(0.1027 m compared to 0.255 m) in set #1. However, the size of input
data in this set was 80 times the size of input data in set #2; a factor
that may increase the computational effort unpredictably. It was
concluded that 80 daily data may be successfully utilized to lower the
size of input data sets considerably, while maintaining the effective
information in the data set.
Abstract: In this paper we present a noise reduction filter for video processing. It is based on the recently proposed two dimensional steering kernel, extended to three dimensions and further augmented to suit the spatial-temporal domain of video processing. Two alternative filters are proposed - the time symmetric kernel and the time asymmetric kernel. The first reduces the noise on single sequences, but to handle the problems at scene shift the asymmetric kernel is introduced. The performance of both are tested on simulated data and on a real video sequence together with the existing steering kernel. The proposed kernels improves the Rooted Mean Squared Error (RMSE) compared to the original steering kernel method on video material.
Abstract: Energy dissipation in drops has been investigated by
physical models. After determination of effective parameters on the
phenomenon, three drops with different heights have been
constructed from Plexiglas. They have been installed in two existing
flumes in the hydraulic laboratory. Several runs of physical models
have been undertaken to measured required parameters for
determination of the energy dissipation. Results showed that the
energy dissipation in drops depend on the drop height and discharge.
Predicted relative energy dissipations varied from 10.0% to 94.3%.
This work has also indicated that the energy loss at drop is mainly
due to the mixing of the jet with the pool behind the jet that causes
air bubble entrainment in the flow. Statistical model has been
developed to predict the energy dissipation in vertical drops denotes
nonlinear correlation between effective parameters. Further an
artificial neural networks (ANNs) approach was used in this paper to
develop an explicit procedure for calculating energy loss at drops
using NeuroSolutions. Trained network was able to predict the
response with R2 and RMSE 0.977 and 0.0085 respectively. The
performance of ANN was found effective when compared to
regression equations in predicting the energy loss.