Abstract: Polymer Electrolyte Membrane Fuel Cell (PEMFC) is
such a time-vary nonlinear dynamic system. The traditional linear
modeling approach is hard to estimate structure correctly of PEMFC
system. From this reason, this paper presents a nonlinear modeling of
the PEMFC using Neural Network Auto-regressive model with
eXogenous inputs (NNARX) approach. The multilayer perception
(MLP) network is applied to evaluate the structure of the NNARX
model of PEMFC. The validity and accuracy of NNARX model are
tested by one step ahead relating output voltage to input current from
measured experimental of PEMFC. The results show that the obtained
nonlinear NNARX model can efficiently approximate the dynamic
mode of the PEMFC and model output and system measured output
consistently.
Abstract: In this work, neural networks methods MLP type were
applied to a database from an array of six sensors for the detection of
three toxic gases. The choice of the number of hidden layers and the
weight values are influential on the convergence of the learning
algorithm. We proposed, in this article, a mathematical formula to
determine the optimal number of hidden layers and good weight
values based on the method of back propagation of errors. The results
of this modeling have improved discrimination of these gases and
optimized the computation time. The model presented here has
proven to be an effective application for the fast identification of
toxic gases.
Abstract: A relationship between face and signature biometrics
is established in this paper. A new approach is developed to predict
faces from signatures by using artificial intelligence. A multilayer
perceptron (MLP) neural network is used to generate face details
from features extracted from signatures, here face is the physical
biometric and signatures is the behavioural biometric. The new
method establishes a relationship between the two biometrics and
regenerates a visible face image from the signature features.
Furthermore, the performance efficiencies of our new technique are
demonstrated in terms of minimum error rates compared to published
work.
Abstract: In this research work, neural networks were applied to
classify two types of hip joint implants based on the relative hip joint
implant side speed and three components of each ground reaction
force. The condition of walking gait at normal velocity was used and
carried out with each of the two hip joint implants assessed. Ground
reaction forces’ kinetic temporal changes were considered in the first
approach followed but discarded in the second one. Ground reaction
force components were obtained from eighteen patients under such
gait condition, half of which had a hip implant type I-II, whilst the
other half had the hip implant, defined as type III by Orthoload®.
After pre-processing raw gait kinetic data and selecting the time
frames needed for the analysis, the ground reaction force components
were used to train a MLP neural network, which learnt to distinguish
the two hip joint implants in the abovementioned condition. Further
to training, unknown hip implant side and ground reaction force
components were presented to the neural networks, which assigned
those features into the right class with a reasonably high accuracy for
the hip implant type I-II and the type III. The results suggest that
neural networks could be successfully applied in the performance
assessment of hip joint implants.
Abstract: The study investigated the implementation of the
Neural Network (NN) techniques for prediction of the loading of Cu
ions onto clinoptilolite. The experimental design using analysis of
variance (ANOVA) was chosen for testing the adequacy of the
Neural Network and for optimizing of the effective input parameters
(pH, temperature and initial concentration). Feed forward, multi-layer
perceptron (MLP) NN successfully tracked the non-linear behavior of
the adsorption process versus the input parameters with mean squared
error (MSE), correlation coefficient (R) and minimum squared error
(MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed
that NN modeling techniques could effectively predict and simulate
the highly complex system and non-linear process such as ionexchange.
Abstract: Several meteorological parameters were used for the
prediction of monthly average daily global solar radiation on
horizontal using recurrent neural networks (RNNs). Climatological
data and measures, mainly air temperature, humidity, sunshine
duration, and wind speed between 1995 and 2007 were used to design
and validate a feed forward and recurrent neural network based
prediction systems. In this paper we present our reference system
based on a feed-forward multilayer perceptron (MLP) as well as the
proposed approach based on an RNN model. The obtained results
were promising and comparable to those obtained by other existing
empirical and neural models. The experimental results showed the
advantage of RNNs over simple MLPs when we deal with time series
solar radiation predictions based on daily climatological data.
Abstract: In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion detection system (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw dataset for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle component analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. This optimal feature subset is used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.
Abstract: In this paper, application of artificial neural networks
in typical disease diagnosis has been investigated. The real procedure
of medical diagnosis which usually is employed by physicians was
analyzed and converted to a machine implementable format. Then
after selecting some symptoms of eight different diseases, a data set
contains the information of a few hundreds cases was configured and
applied to a MLP neural network. The results of the experiments and
also the advantages of using a fuzzy approach were discussed as
well. Outcomes suggest the role of effective symptoms selection and
the advantages of data fuzzificaton on a neural networks-based
automatic medical diagnosis system.
Abstract: Cytogenetic analysis still remains the gold standard method for prenatal diagnosis of trisomy 21 (Down syndrome, DS). Nevertheless, the conventional cytogenetic analysis needs live cultured cells and is too time-consuming for clinical application. In contrast, molecular methods such as FISH, QF-PCR, MLPA and quantitative Real-time PCR are rapid assays with results available in 24h. In the present study, we have successfully used a novel MGB TaqMan probe-based real time PCR assay for rapid diagnosis of trisomy 21 status in Down syndrome samples. We have also compared the results of this molecular method with corresponding results obtained by the cytogenetic analysis. Blood samples obtained from DS patients (n=25) and normal controls (n=20) were tested by quantitative Real-time PCR in parallel to standard G-banding analysis. Genomic DNA was extracted from peripheral blood lymphocytes. A high precision TaqMan probe quantitative Real-time PCR assay was developed to determine the gene dosage of DSCAM (target gene on 21q22.2) relative to PMP22 (reference gene on 17p11.2). The DSCAM/PMP22 ratio was calculated according to the formula; ratio=2 -ΔΔCT. The quantitative Real-time PCR was able to distinguish between trisomy 21 samples and normal controls with the gene ratios of 1.49±0.13 and 1.03±0.04 respectively (p value
Abstract: Software project effort estimation is frequently seen
as complex and expensive for individual software engineers.
Software production is in a crisis. It suffers from excessive costs.
Software production is often out of control. It has been suggested that
software production is out of control because we do not measure.
You cannot control what you cannot measure. During last decade, a
number of researches on cost estimation have been conducted. The
metric-set selection has a vital role in software cost estimation
studies; its importance has been ignored especially in neural network
based studies. In this study we have explored the reasons of those
disappointing results and implemented different neural network
models using augmented new metrics. The results obtained are
compared with previous studies using traditional metrics. To be able
to make comparisons, two types of data have been used. The first
part of the data is taken from the Constructive Cost Model
(COCOMO'81) which is commonly used in previous studies and the
second part is collected according to new metrics in a leading
international company in Turkey. The accuracy of the selected
metrics and the data samples are verified using statistical techniques.
The model presented here is based on Multi-Layer Perceptron
(MLP). Another difficulty associated with the cost estimation studies
is the fact that the data collection requires time and care. To make a
more thorough use of the samples collected, k-fold, cross validation
method is also implemented. It is concluded that, as long as an
accurate and quantifiable set of metrics are defined and measured
correctly, neural networks can be applied in software cost estimation
studies with success
Abstract: There are a lot of extensions made to the classic model of multi-layer perceptron (MLP). A notable amount of them has been designed to hasten the learning process without considering the quality of generalization. The paper proposes a new MLP extension based on exploiting topology of the input layer of the network. Experimental results show the extended model to improve upon generalization capability in certain cases. The new model requires additional computational resources to compare to the classic model, nevertheless the loss in efficiency isn-t regarded to be significant.
Abstract: In this paper, a second order autoregressive (AR)
model is proposed to discriminate alcoholics using single trial
gamma band Visual Evoked Potential (VEP) signals using 3 different
classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN),
Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear
Discriminant (LD). Electroencephalogram (EEG) signals were
recorded from alcoholic and control subjects during the presentation
of visuals from Snodgrass and Vanderwart picture set. Single trial
VEP signals were extracted from EEG signals using Elliptic filtering
in the gamma band spectral range. A second order AR model was
used as gamma band VEP exhibits pseudo-periodic behaviour and
second order AR is optimal to represent this behaviour. This
circumvents the requirement of having to use some criteria to choose
the correct order. The averaged discrimination errors of 2.6%, 2.8%
and 11.9% were given by LD, MLP-BP and SFA classifiers. The
high LD discrimination results show the validity of the proposed
method to discriminate between alcoholic subjects.
Abstract: A set of Artificial Neural Network (ANN) based methods
for the design of an effective system of speech recognition of
numerals of Assamese language captured under varied recording
conditions and moods is presented here. The work is related to
the formulation of several ANN models configured to use Linear
Predictive Code (LPC), Principal Component Analysis (PCA) and
other features to tackle mood and gender variations uttering numbers
as part of an Automatic Speech Recognition (ASR) system in
Assamese. The ANN models are designed using a combination of
Self Organizing Map (SOM) and Multi Layer Perceptron (MLP)
constituting a Learning Vector Quantization (LVQ) block trained in a
cooperative environment to handle male and female speech samples
of numerals of Assamese- a language spoken by a sizable population
in the North-Eastern part of India. The work provides a comparative
evaluation of several such combinations while subjected to handle
speech samples with gender based differences captured by a microphone
in four different conditions viz. noiseless, noise mixed, stressed
and stress-free.
Abstract: In this article, we propose a methodology for the
characterization of the suspended matter along Algiers-s bay. An
approach by multi layers perceptron (MLP) with training by back
propagation of the gradient optimized by the algorithm of Levenberg
Marquardt (LM) is used. The accent was put on the choice of the
components of the base of training where a comparative study made
for four methods: Random and three alternatives of classification by
K-Means. The samples are taken from suspended matter image,
obtained by analytical model based on polynomial regression by
taking account of in situ measurements. The mask which selects the
zone of interest (water in our case) was carried out by using a multi
spectral classification by ISODATA algorithm. To improve the
result of classification, a cleaning of this mask was carried out using
the tools of mathematical morphology. The results of this study
presented in the forms of curves, tables and of images show the
founded good of our methodology.
Abstract: Cosmic showers, from their places of origin in space,
after entering earth generate secondary particles called Extensive Air
Shower (EAS). Detection and analysis of EAS and similar High
Energy Particle Showers involve a plethora of experimental setups
with certain constraints for which soft-computational tools like
Artificial Neural Network (ANN)s can be adopted. The optimality
of ANN classifiers can be enhanced further by the use of Multiple
Classifier System (MCS) and certain data - dimension reduction
techniques. This work describes the performance of certain data
dimension reduction techniques like Principal Component Analysis
(PCA), Independent Component Analysis (ICA) and Self Organizing
Map (SOM) approximators for application with an MCS formed
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN). The data inputs are
obtained from an array of detectors placed in a circular arrangement
resembling a practical detector grid which have a higher dimension
and greater correlation among themselves. The PCA, ICA and SOM
blocks reduce the correlation and generate a form suitable for real
time practical applications for prediction of primary energy and
location of EAS from density values captured using detectors in a
circular grid.
Abstract: This paper presents the prediction of kidney
dysfunction using different neural network (NN) approaches. Self
organization Maps (SOM), Probabilistic Neural Network (PNN) and
Multi Layer Perceptron Neural Network (MLPNN) trained with Back
Propagation Algorithm (BPA) are used in this study. Six hundred and
sixty three sets of analytical laboratory tests have been collected from
one of the private clinical laboratories in Baghdad. For each subject,
Serum urea and Serum creatinin levels have been analyzed and tested
by using clinical laboratory measurements. The collected urea and
cretinine levels are then used as inputs to the three NN models in
which the training process is done by different neural approaches.
SOM which is a class of unsupervised network whereas PNN and
BPNN are considered as class of supervised networks. These
networks are used as a classifier to predict whether kidney is normal
or it will have a dysfunction. The accuracy of prediction, sensitivity
and specificity were found for each type of the proposed networks
.We conclude that PNN gives faster and more accurate prediction of
kidney dysfunction and it works as promising tool for predicting of
routine kidney dysfunction from the clinical laboratory data.
Abstract: Ability of accurate and reliable location estimation in
indoor environment is the key issue in developing great number of
context aware applications and Location Based Services (LBS).
Today, the most viable solution for localization is the Received
Signal Strength (RSS) fingerprinting based approach using wireless
local area network (WLAN). This paper presents two RSS
fingerprinting based approaches – first we employ widely used
WLAN based positioning as a reference system and then investigate
the possibility of using GSM signals for positioning. To compare
them, we developed a positioning system in real world environment,
where realistic RSS measurements were collected. Multi-Layer
Perceptron (MLP) neural network was used as the approximation
function that maps RSS fingerprints and locations. Experimental
results indicate advantage of WLAN based approach in the sense of
lower localization error compared to GSM based approach, but GSM
signal coverage by far outreaches WLAN coverage and for some
LBS services requiring less precise accuracy our results indicate that
GSM positioning can also be a viable solution.
Abstract: This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines.
Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the
dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the
effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.
Abstract: Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.
Abstract: Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.