Abstract: Development of a method to estimate gene functions is
an important task in bioinformatics. One of the approaches for the
annotation is the identification of the metabolic pathway that genes are
involved in. Since gene expression data reflect various intracellular
phenomena, those data are considered to be related with genes’
functions. However, it has been difficult to estimate the gene function
with high accuracy. It is considered that the low accuracy of the
estimation is caused by the difficulty of accurately measuring a gene
expression. Even though they are measured under the same condition,
the gene expressions will vary usually. In this study, we proposed a
feature extraction method focusing on the variability of gene
expressions to estimate the genes' metabolic pathway accurately. First,
we estimated the distribution of each gene expression from replicate
data. Next, we calculated the similarity between all gene pairs by KL
divergence, which is a method for calculating the similarity between
distributions. Finally, we utilized the similarity vectors as feature
vectors and trained the multiclass SVM for identifying the genes'
metabolic pathway. To evaluate our developed method, we applied the
method to budding yeast and trained the multiclass SVM for
identifying the seven metabolic pathways. As a result, the accuracy
that calculated by our developed method was higher than the one that
calculated from the raw gene expression data. Thus, our developed
method combined with KL divergence is useful for identifying the
genes' metabolic pathway.
Abstract: The performance and analysis of speech recognition
system is illustrated in this paper. An approach to recognize the
English word corresponding to digit (0-9) spoken by 2 different
speakers is captured in noise free environment. For feature extraction,
speech Mel frequency cepstral coefficients (MFCC) has been used
which gives a set of feature vectors from recorded speech samples.
Neural network model is used to enhance the recognition
performance. Feed forward neural network with back propagation
algorithm model is used. However other speech recognition
techniques such as HMM, DTW exist. All experiments are carried
out on Matlab.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: The aim of research was to define the relations
between volatile compounds, some parameters (pH, titratable acidity
(TA), total soluble solid (TSS), lactic acid bacteria count) and
consumer preference of commercial fermented milks. These relations
tend to be used for controlling and developing new fermented milk
product. Three leading commercial brands of fermented milks in
Thailand were evaluated by consumers (n=71) using hedonic scale
for four attributes (sweetness, sourness, flavour, and overall liking),
volatile compounds using headspace-solid phase microextraction
(HS-SPME) GC-MS, pH, TA, TSS and LAB count. Then the
relations were analyzed by principal component analysis (PCA). The
PCA data showed that all of four attributes liking scores were related
to each other. They were also related to TA, TSS and volatile
compounds. The related volatile compounds were mainly on
fermented produced compounds including acetic acid, furanmethanol,
furfural, octanoic acid and the volatiles known as artificial fruit
flavour (beta pinene, limonene, vanillin, and ethyl vanillin). These
compounds were provided the information about flavour addition in
commercial fermented milk in Thailand.
Abstract: ESPRIT-TLS method appears a good choice for high
resolution fault detection in induction machines. It has a very high
effectiveness in the frequency and amplitude identification.
Contrariwise, it presents a high computation complexity which
affects its implementation in real time fault diagnosis. To avoid this
problem, a Fast-ESPRIT algorithm that combined the IIR band-pass
filtering technique, the decimation technique and the original
ESPRIT-TLS method was employed to enhance extracting accurately
frequencies and their magnitudes from the wind stator current with
less computation cost. The proposed algorithm has been applied to
verify the wind turbine machine need in the implementation of an online,
fast, and proactive condition monitoring. This type of remote
and periodic maintenance provides an acceptable machine lifetime,
minimize its downtimes and maximize its productivity. The
developed technique has evaluated by computer simulations under
many fault scenarios. Study results prove the performance of Fast-
ESPRIT offering rapid and high resolution harmonics recognizing
with minimum computation time and less memory cost.
Abstract: Effect of biopesticide from wood vinegar and
extracted substances from 3 medicinal plants such as: non taai yak
(Stemona tuberosa Lour), boraphet (Tinospora crispa Mier) and
derris (Derris elliptica Roxb) were tested on the age five years of
pomelo. The selected pomelo was carried out for insects’ pest control
and its quality. The experimental site was located at farmer’s orchard
in Phichit Province, Thailand. This study was undertaken during the
drought season (December to March). The extracted from plants and
wood vinegar were evaluated in 6 treatments: 1) water as control; 2)
wood vinegar; 3) S. tuberosa Lour; 4) T. crispa Mier; 5) D. elliptica
Roxb; 6) mixed (wood vinegar + S. tuberosa Lour + T. crispa Mier +
D. elliptica Roxb). The experiment was RCB with 6 treatments and 3
replications per treatment. The results showed that T. crispa Mier was
the highest effectiveness for reduction population of thrips
(Scirtothrips dorsalis Hood) and citrus leaf miner (Phyllocnistis
citrella Stainton) at 14.10 and 15.37 respectively, followed by
treatment of mixed, D. elliptica Roxb, S. tuberosa Lour and wood
vinegar with significance different. Additionally, T. crispa Mier
promoted the high quality of harvested pomelo in term of thickness
of skin at 12.45 mm and S. tuberosa Lour gave the high quality of the
pomelo in term of firmness (276.5 kg/cm2) and brix (11.0%).
Abstract: This study investigates the cleaning performance of
high intensity 360 kHz frequency on removal of nano-dimensional
and sub-micron particles from various surfaces, uniformity of the
cleaning tank and run to run variation of cleaning process. The
uniformity of the cleaning tank was measured by two different
methods i.e. 1. ppbTM meter and 2. Liquid Particle Counting (LPC)
technique. The result indicates that the energy was distributed more
uniformly throughout the entire cleaning vessel even at the corners
and edges of the tank when megasonic sweeping technology is
applied. The result also shows that rinsing the parts with 360 kHz
frequency at final rinse gives lower particle counts, hence higher
cleaning efficiency as compared to other frequencies. When
megasonic sweeping technology is applied each piezoelectric
transducers will operate at their optimum resonant frequency and
generates stronger acoustic cavitational force and higher acoustic
streaming velocity. These combined forces are helping to enhance the
particle removal and at the same time improve the overall cleaning
performance. The multiple extractions study was also carried out for
various frequencies to measure the cleaning potential and asymptote
value.
Abstract: In this paper, we propose an automatic verification
technology of software patches for user virtual environments on IaaS
Cloud to decrease verification costs of patches. In these days, IaaS
services have been spread and many users can customize virtual
machines on IaaS Cloud like their own private servers. Regarding to
software patches of OS or middleware installed on virtual machines,
users need to adopt and verify these patches by themselves. This task
increases operation costs of users. Our proposed method replicates
user virtual environments, extracts verification test cases for user
virtual environments from test case DB, distributes patches to virtual
machines on replicated environments and conducts those test cases
automatically on replicated environments. We have implemented the
proposed method on OpenStack using Jenkins and confirmed the
feasibility. Using the implementation, we confirmed the effectiveness
of test case creation efforts by our proposed idea of 2-tier abstraction
of software functions and test cases. We also evaluated the automatic
verification performance of environment replications, test cases
extractions and test cases conductions.
Abstract: The web’s increased popularity has included a huge
amount of information, due to which automated web page
classification systems are essential to improve search engines’
performance. Web pages have many features like HTML or XML
tags, hyperlinks, URLs and text contents which can be considered
during an automated classification process. It is known that Webpage
classification is enhanced by hyperlinks as it reflects Web page
linkages. The aim of this study is to reduce the number of features to
be used to improve the accuracy of the classification of web pages. In
this paper, a novel feature selection method using an improved
Particle Swarm Optimization (PSO) using principle of evolution is
proposed. The extracted features were tested on the WebKB dataset
using a parallel Neural Network to reduce the computational cost.
Abstract: Large-scale data stream analysis has become one of
the important business and research priorities lately. Social networks
like Twitter and other micro-blogging platforms hold an enormous
amount of data that is large in volume, velocity and variety.
Extracting valuable information and trends out of these data would
aid in a better understanding and decision-making. Multiple analysis
techniques are deployed for English content. Moreover, one of the
languages that produce a large amount of data over social networks
and is least analyzed is the Arabic language. The proposed paper is a
survey on the research efforts to analyze the Arabic content in
Twitter focusing on the tools and methods used to extract the
sentiments for the Arabic content on Twitter.
Abstract: Biofuels production has come forth as a future
technology to combat the problem of depleting fossil fuels. Bio-based
ethanol production from enzymatic lignocellulosic biomass
degradation serves an efficient method and catching the eye of
scientific community. High cost of the enzyme is the major obstacle
in preventing the commercialization of this process. Thus main
objective of the present study was to optimize composition of
medium components for enhancing cellulase production by newly
isolated strain of Bacillus tequilensis. Nineteen factors were taken
into account using statistical Plackett-Burman Design. The significant
variables influencing the cellulose production were further employed
in statistical Response Surface Methodology using Central
Composite Design for maximizing cellulase production. The
optimum medium composition for cellulase production was: peptone
(4.94 g/L), ammonium chloride (4.99 g/L), yeast extract (2.00 g/L),
Tween-20 (0.53 g/L), calcium chloride (0.20 g/L) and cobalt chloride
(0.60 g/L) with pH 7, agitation speed 150 rpm and 72 h incubation at
37oC. Analysis of variance (ANOVA) revealed high coefficient of
determination (R2) of 0.99. Maximum cellulase productivity of 11.5
IU/ml was observed against the model predicted value of 13 IU/ml.
This was found to be optimally active at 60oC and pH 5.5.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: The inherent skin patterns created at the joints in the
finger exterior are referred as finger knuckle-print. It is exploited to
identify a person in a unique manner because the finger knuckle print
is greatly affluent in textures. In biometric system, the region of
interest is utilized for the feature extraction algorithm. In this paper,
local and global features are extracted separately. Fast Discrete
Orthonormal Stockwell Transform is exploited to extract the local
features. Global feature is attained by escalating the size of Fast
Discrete Orthonormal Stockwell Transform to infinity. Two features
are fused to increase the recognition accuracy. A matching distance is
calculated for both the features individually. Then two distances are
merged mutually to acquire the final matching distance. The
proposed scheme gives the better performance in terms of equal error
rate and correct recognition rate.
Abstract: The aim of this paper is to perform experimental
modal analysis (EMA) of reinforced concrete (RC) square slabs.
EMA is the process of determining the modal parameters (Natural
Frequencies, damping factors, modal vectors) of a structure from a
set of frequency response functions FRFs (curve fitting). Although,
experimental modal analysis (or modal testing) has grown steadily in
popularity since the advent of the digital FFT spectrum analyzer in
the early 1970’s, studying all types of members and materials using
such method have not yet been well documented. Therefore, in this
work, experimental tests were conducted on RC square slab
specimens of dimensions 600mm x 600mmx 40mm. Experimental
analysis was based on freely supported boundary condition.
Moreover, impact testing as a fast and economical means of finding
the modes of vibration of a structure was used during the
experiments. In addition, Pico Scope 6 device and MATLAB
software were used to acquire data, analyze and plot Frequency
Response Function (FRF). The experimental natural frequencies
which were extracted from measurements exhibit good agreement
with analytical predictions. It is showed that EMA method can be
usefully employed to investigate the dynamic behavior of RC slabs.
Abstract: Generally the natural environment is made up of air,
water and soil. The release of emission of industrial waste into
anyone of the components of the environment causes pollution.
Industrial pollution significantly threatens the inherent right of
people, to the enjoyment of a safe and secure environment. The aim
of this paper is to assess the effect of environmental pollution and
health risks of residents living near Ewekoro cement factory. The
research made use of IKONOS imagery for Geographical
Information System (GIS) to buffer and extract buildings that are less
than 1km to the factory, within 1km to 5km and above 5km to the
factory. Also questionnaire was used to elicit information on the
socio-economic factors, effect of environmental pollution on
residents and measures adopted to control industrial pollution on the
residents. Findings show that most buildings that fall between less
than 1km and 1km to 5km to the factory have high health risk in the
study area. The study recommended total relocation for the residents
of the study area to reduce health risk problems.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: Speech Segmentation is the measure of the change
point detection for partitioning an input speech signal into regions
each of which accords to only one speaker. In this paper, we apply
two features based on multi-scale product (MP) of the clean speech,
namely the spectral centroid of MP, and the zero crossings rate of
MP. We focus on multi-scale product analysis as an important tool
for segmentation extraction. The MP is based on making the product
of the speech wavelet transform coefficients (WTC). We have
estimated our method on the Keele database. The results show the
effectiveness of our method. It indicates that the two features can find
word boundaries, and extracted the segments of the clean speech.
Abstract: Neurons in the nervous system communicate with
each other by producing electrical signals called spikes. To
investigate the physiological function of nervous system it is essential
to study the activity of neurons by detecting and sorting spikes in the
recorded signal. In this paper a method is proposed for considering
the spike sorting problem which is based on the nonlinear modeling
of spikes using exponential autoregressive model. The genetic
algorithm is utilized for model parameter estimation. In this regard
some selected model coefficients are used as features for sorting
purposes. For optimal selection of model coefficients, self-organizing
feature map is used. The results show that modeling of spikes with
nonlinear autoregressive model outperforms its linear counterpart.
Also the extracted features based on the coefficients of exponential
autoregressive model are better than wavelet based extracted features
and get more compact and well-separated clusters. In the case of
spikes different in small-scale structures where principal component
analysis fails to get separated clouds in the feature space, the
proposed method can obtain well-separated cluster which removes
the necessity of applying complex classifiers.
Abstract: This study was conducted to investigate the effect of
the antioxidant activity of germinated African Yam Bean (AYB) on
oxidative stress markers in alloxan induced diabetic rat. Rats were
randomized into three groups; control, diabetic and germinated AYB
– treated diabetic rats. The Total phenol and flavonoid content and
DPPH radical scavenging activity before and after germination were
investigated. The glucose level, lipid peroxidation and reduced
glutathione of the animals were also determined using standard
technique for four weeks. Germination increased the total phenol,
flavonoid and antioxidant activity of AYB extract by 19.14%,
32.28% and 57.25% respectively. The diabetic rats placed on
germinated AYB diet had a significant decrease in the blood glucose
and lipid peroxidation with a corresponding increase in glutathione
(p
Abstract: Modelling of the earth's surface and evaluation of
urban environment, with 3D models, is an important research topic.
New stereo capabilities of high resolution optical satellites images,
such as the tri-stereo mode of Pleiades, combined with new image
matching algorithms, are now available and can be applied in urban
area analysis. In addition, photogrammetry software packages gained
new, more efficient matching algorithms, such as SGM, as well as
improved filters to deal with shadow areas, can achieve more dense
and more precise results.
This paper describes a comparison between 3D data extracted
from tri-stereo and dual stereo satellite images, combined with pixel
based matching and Wallis filter. The aim was to improve the
accuracy of 3D models especially in urban areas, in order to assess if
satellite images are appropriate for a rapid evaluation of urban
environments.
The results showed that 3D models achieved by Pleiades tri-stereo
outperformed, both in terms of accuracy and detail, the result
obtained from a Geo-eye pair. The assessment was made with
reference digital surface models derived from high resolution aerial
photography. This could mean that tri-stereo images can be
successfully used for the proposed urban change analyses.