Abstract: The English competence of Thai people was examined
in the context of knowledge of English in everyday life for Small and
Medium Entrepreneurs (SMEs), and also integrated with Second
language acquisition (SLA) students’ classroom. Second language
acquisition was applied to the results of the questionnaires and
interview forms. Levels of the need on English used for SME
entrepreneurs in Thailand, satisfaction on joining the street classroom
project were shown to be significantly high for some certain language
functions and satisfaction. Finding suggests that the language
functions on etiquette for professional use is essential and useful
because lesson learned can be used in the real situation for their
career. Implications for the climate of the street classroom are
discussed.
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: Unmanned Aircraft Systems (UAS) become
indispensable parts of modern airpower as force multiplier. One of
the main advantages of UAS is long endurance. UAS have to take
extra payloads to accomplish different missions but these payloads
decrease endurance of aircraft because of increasing drag. There are
continuing researches to increase the capability of UAS. There are
some vertical thermal air currents, which can cause climb and
increase endurance, in nature. Birds and gliders use thermals to gain
altitude with no effort. UAS have wide wings which can use
thermals like birds and gliders. Thermal regions, which is area of
2000-3000 meter (1 NM), exist all around the world. It is natural and
infinite source. This study analyses if thermal regions can be adopted
and implemented as an assistant tool for UAS route planning. First
and second part of study will contain information about the thermal
regions and current applications about UAS in aviation and climbing
performance with a real example. Continuing parts will analyze the
contribution of thermal regions to UAS endurance. Contribution is
important because planning declaration of UAS navigation rules will
be in 2015.
Abstract: The concept of technology as well as itself has
evolved continuously over time, such that, nowadays, this concept is
still marked by myths and realities. Even the concept of science is
frequently misunderstood as technology. In this way, this paper
presents different forms of interpretation of the concept of technology
in the course of history, as well as the social and cultural aspects
associated with it, through an analysis made by means of insights
from sociological studies of science and technology and its multiple
relations with society. Through the analysis of contents, the paper
presents a classification of how technology is interpreted in the social
sphere and search channel efforts to show how a broader
understanding can contribute to better interpretations of how
scientific and technological development influences the environment
in which we operate. The text also presents a particular point of view
for the interpretation of the concept from the analysis throughout the
whole work.
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: Doxorubicin, also known as Adriamycin, is an
anthracycline class of drug used in cancer chemotherapy. It is used in
the treatment of non-Hodgkin’s lymphoma, multiple myeloma, acute
leukemia, breast cancer, lung cancer, endometrium cancer and ovary
cancers. It functions via intercalating DNA and ultimately killing
cancer cells. The major side effects of doxorubicin are hair loss,
myelosuppression, nausea & vomiting, oesophagitis, diarrhea, heart
damage and liver dysfunction. The minor modifications in the
structure of compound exhibit large variation in the biological
activity, has prompted us to carry out the synthesis of sulfonamide
derivatives. Sulfonamide is an important feature with broad spectrum
of biological activity such as antiviral, antifungal, diuretics, antiinflammatory,
antibacterial and anticancer activities. Structure of the
synthesized compound N-(1-methyl-2-oxo-2-N-methyl anilinoethyl)
benzene sulfonamide confirmed by proton nuclear magnetic
resonance (1H NMR),13C NMR, Mass and FTIR spectroscopic tools
to assure the position of all protons and hence stereochemistry of the
molecule. Further we have reported the binding potential of
synthesized sulfonamide analogues in comparison to doxorubicin
drug using Auto Dock 4.2 software. Computational binding energy
(B.E.) and inhibitory constant (Ki) has been evaluated for the
synthesized compound in comparison of doxorubicin against Poly
(dA-dT).Poly (dA-dT) and Poly (dG-dC).Poly (dG-dC) sequences.
The in vitro cytotoxic study against human breast cancer cell lines
confirms the better anticancer activity of the synthesized compound
over currently in use anticancer drug doxorubicin. The IC50 value of
the synthesized compound is 7.12 μM whereas for doxorubicin is 7.2
μM.
Abstract: This research was conducted in the Mae Sot
Watershed where located in the Moei River Basin at the Upper
Salween River Basin in Tak Province, Thailand. The Mae Sot
Municipality is the largest urban area in Tak Province and situated in
the midstream of the Mae Sot Watershed. It usually faces flash flood
problem after heavy rain due to poor flood management has been
reported since economic rapidly bloom up in recent years. Its
catchment can be classified as ungauged basin with lack of rainfall
data and no any stream gaging station was reported. It was attached
by most severely flood events in 2013 as the worst studied case for
all those communities in this municipality. Moreover, other problems
are also faced in this watershed, such shortage water supply for
domestic consumption and agriculture utilizations including a
deterioration of water quality and landslide as well. The research
aimed to increase capability building and strengthening the
participation of those local community leaders and related agencies to
conduct better water management in urban area was started by mean
of the data collection and illustration of the appropriated application
of some short period rainfall forecasting model as they aim for better
flood relief plan and management through the hydrologic model
system and river analysis system programs. The authors intended to
apply the global rainfall data via the integrated data viewer (IDV)
program from the Unidata with the aim for rainfall forecasting in a
short period of 7-10 days in advance during rainy season instead of
real time record. The IDV product can be present in an advance
period of rainfall with time step of 3-6 hours was introduced to the
communities. The result can be used as input data to the hydrologic
modeling system model (HEC-HMS) for synthesizing flood
hydrographs and use for flood forecasting as well. The authors
applied the river analysis system model (HEC-RAS) to present flood
flow behaviors in the reach of the Mae Sot stream via the downtown
of the Mae Sot City as flood extents as the water surface level at
every cross-sectional profiles of the stream. Both models of HMS and
RAS were tested in 2013 with observed rainfall and inflow-outflow
data from the Mae Sot Dam. The result of HMS showed fit to the
observed data at the dam and applied at upstream boundary discharge
to RAS in order to simulate flood extents and tested in the field, and
the result found satisfying. The product of rainfall from IDV was fair
while compared with observed data. However, it is an appropriate
tool to use in the ungauged catchment to use with flood hydrograph
and river analysis models for future efficient flood relief plan and
management.
Abstract: Remote sensing plays a vital role in mapping of
resources and monitoring of environments of the earth. In the present
research study, mapping and monitoring of clay siltations occurred in
the Alkhod Dam of Muscat, Sultanate of Oman are carried out using
low-cost multispectral Landsat and ASTER data. The dam is
constructed across the Wadi Samail catchment for ground water
recharge. The occurrence and spatial distribution of siltations in the
dam are studied with five years of interval from the year 1987 of
construction to 2014. The deposits are mainly due to the clay, sand
and silt occurrences derived from the weathering rocks of ophiolite
sequences occurred in the Wadi Samail catchment. The occurrences
of clays are confirmed by minerals identification using ASTER
VNIR-SWIR spectral bands and Spectral Angle Mapper supervised
image processing method. The presence of clays and their spatial
distribution are verified in the field. The study recommends the
technique and the low-cost satellite data to similar region of the
world.
Abstract: This paper introduces an original method of
parametric optimization of the structure for multimodal decisionlevel
fusion scheme which combines the results of the partial solution
of the classification task obtained from assembly of the mono-modal
classifiers. As a result, a multimodal fusion classifier which has the
minimum value of the total error rate has been obtained.
Abstract: Various personality profile tests are used to identify
personality strengths and limits in individuals, helping both
individuals and managers to optimize work and team effort in
organizations. One such test, the Hartman’s personality profile,
emphasizes four driving "core motives" influenced or affected by
both strengths and limitations classified into four colors: Red -
motivated by power; Blue - discipline and loyalty; White - peace; and
Yellow – fun loving. Two shortcomings of Hartman’s personality test
are noted; 1) only one selection for every item / situation allowed and
2) selection of an item / option even if not applicable. A test taker
may be as much nurturing as he is opinionated but since
“opinionated” seems less attractive the individual would likely select
nurturing, causing a misidentification in personality strengths and
limits. Since few individuals have a “strong” personality, it is
difficult to assess their true personality strengths and limits allowing
only one choice or requiring unwanted choices, undermining the
potential of the test. We modified Hartman’s personality profile
allowing test takers to make either multiple choices for any item /
situation or leave them blank if applicable. Sixty-eight participants
(38 males and 30 females), 17 - 49 years old, from countries in Asia,
Europe, N. America, CIS, Africa, Latin America, and Oceania were
included. 58 participants (85.3%) reported the modified test, allowing
multiple / no choices better identified their personality strengths and
limits, while 10 participants (14.7%) expressed the original (one
choice version) was sufficient. The overall results show that our
modified test enhanced the identification and balance of core
personalities’ strengths and limits, aiding test takers, managers and
organizations to better assess individual characteristics, particularly
useful in making task-related, teamwork, and management decisions.
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: Human motion capture has become one of the major
area of interest in the field of computer vision. Some of the major
application areas that have been rapidly evolving include the
advanced human interfaces, virtual reality and security/surveillance
systems. This study provides a brief overview of the techniques and
applications used for the markerless human motion capture, which
deals with analyzing the human motion in the form of mathematical
formulations. The major contribution of this research is that it
classifies the computer vision based techniques of human motion
capture based on the taxonomy, and then breaks its down into four
systematically different categories of tracking, initialization, pose
estimation and recognition. The detailed descriptions and the
relationships descriptions are given for the techniques of tracking and
pose estimation. The subcategories of each process are further
described. Various hypotheses have been used by the researchers in
this domain are surveyed and the evolution of these techniques have
been explained. It has been concluded in the survey that most
researchers have focused on using the mathematical body models for
the markerless motion capture.
Abstract: With increasingly more mobile health applications
appearing due to the popularity of smartphones, the possibility arises
that these data can be used to improve the medical diagnostic process,
as well as the overall quality of healthcare, while at the same time
lowering costs. However, as of yet there have been no reports of a
successful combination of patient-generated data from smartphones
with data from clinical routine. In this paper we describe how these
two types of data can be combined in a secure way without
modification to hospital information systems, and how they can
together be used in a medical expert system for automatic nutritional
classification and triage.
Abstract: This paper presents an efficient fusion algorithm for
iris images to generate stable feature for recognition in unconstrained
environment. Recently, iris recognition systems are focused on real
scenarios in our daily life without the subject’s cooperation. Under
large variation in the environment, the objective of this paper is to
combine information from multiple images of the same iris. The
result of image fusion is a new image which is more stable for further
iris recognition than each original noise iris image. A wavelet-based
approach for multi-resolution image fusion is applied in the fusion
process. The detection of the iris image is based on Adaboost
algorithm and then local binary pattern (LBP) histogram is then
applied to texture classification with the weighting scheme.
Experiment showed that the generated features from the proposed
fusion algorithm can improve the performance for verification system
through iris recognition.
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 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: 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: In this study was monitored the population of the
European Pond Turtle, Emys orbicularis (Linnaeus, 1758) in the area
of Narta Lagoon, Vlora Bay (Albania), from August to October 2014.
A total of 54 individuals of E. orbicularis were studied using
different methodologies. Curved Carapace Length (CCL), Plastron
Length (PL) and Curved Carapace Width (CCW) were measured for
each individual of E. orbicularis and were statistically analyzed. All
captured turtles were separated in seven different size – classes based
on their carapace length (CCL). Each individual of E. orbicularis was
marked by notching the carapace (marginal scutes). Form all
individuals captured resulted that 37 were females (68.5%), 14 males
(25.9%), 3 juveniles (5.5%), while 18 individuals of E. orbicularis
were recaptured for the first and some for the second time.
Abstract: Teachers can play a huge role in encouraging students
to use computers and can affect students’ attitudes towards
computers. So understanding teachers’ beliefs and their use of
computers is an important way to create effective motivational
systems for teachers to use computers in the classroom in an effective
way. A qualitative study (6 focus group) was carried out among
Saudi High school teachers, both male and female, to examine their
attitudes towards computers and to find out their computer skills and
usage. The study showed a gender differences in that females were
less likely to attend computer workshops, females also had less
computer skills, and they have more negative attitudes towards
computers than males. Also the study found that low computer skills
in the classroom made students unlikely to have the lessons presented
using computers. Furthermore, the study found some factors that
effected teachers’ attitudes towards computers. These factors were
computer experience and confidence as much having skills and good
experience in computer use, the role and importance of computers
had become in their life and in teaching as well.