Abstract: There are so many databases of various fields of life sciences available online. To find well-used databases, a survey to measure life science database citation frequency in scientific literatures is done. The survey is done by measuring how many scientific literatures which are available on PubMed Central archive cited a specific life science database. This paper presents and discusses the results of the survey.
Abstract: The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.
Abstract: The need to have standards has always been a priority
of all the disciplines in the world. Today, standards such as XML and
USB are trying to create a universal interface for their respective
areas. The information regarding every family in the discipline
addressed, must have a lot in common, known as Metadata. A lot of
work has been done in specific domains such as IEEE LOM and
MPEG-7 but they do not appeal to the universality of creating
Metadata for all entities, where we take an entity (object) as, not
restricted to Software Terms. This paper tries to address this problem
of universal Metadata Definition which may lead to increase in
precision of search.
Abstract: Facial features are frequently used to represent local
properties of a human face image in computer vision applications. In
this paper, we present a fast algorithm that can extract the facial
features online such that they can give a satisfying representation of a
face image. It includes one step for a coarse detection of each facial
feature by AdaBoost and another one to increase the accuracy of the
found points by Active Shape Models (ASM) in the regions of interest.
The resulted facial features are evaluated by matching with artificial
face models in the applications of physiognomy. The distance measure
between the features and those in the fate models from the database is
carried out by means of the Hausdorff distance. In the experiment, the
proposed method shows the efficient performance in facial feature
extractions and online system of physiognomy.
Abstract: According to the governmental data, the cases of oral
cancers doubled in the past 10 years. This had brought heavy burden to
the patients- family, the society, and the country. The literature
generally evidenced the betel nut contained particular chemicals that
can cause oral cancers. Research in Taiwan had also proofed that 90
percent of oral cancer patients had experience of betel nut chewing. It
is thus important to educate the betel-nut hobbyists to cease such a
hazardous behavior. A program was then organized to establish
several training classes across different areas specific to help ceasing
this particular habit. Purpose of this research was to explore the
attitude and intention toward ceasing betel-nut chewing before and
after attending the training classes. 50 samples were taken from a
ceasing class with average age at 45 years old with high school
education (54%). 74% of the respondents were male in service or
agricultural industries. Experiences in betel-nut chewing were 5-20
years with a dose of 1-20 pieces per day. The data had shown that 60%
of the respondents had cigarette smoking habit, and 30% of the
respondents were concurrently alcoholic dependent. Research results
indicated that the attitude, intentions, and the knowledge on oral
cancers were found significant different between before and after
attendance. This provided evidence for the effectiveness of the training
class. However, we do not perform follow-up after the class.
Noteworthy is the test result also shown that participants who were
drivers as occupation, or habitual smokers or alcoholic dependents
would be less willing to quit the betel-nut chewing. The test results
indicated as well that the educational levels and the type of occupation
may have significant impacts on an individual-s decisions in taking
betel-nut or substance abuse.
Abstract: One of the processes of slope that occurs every year in Iran and some parts of world and cause a lot of criminal and financial harms is called landslide. They are plenty of method to stability landslide in soil and rock slides. The use of the best method with the least cost and in the shortest time is important for researchers. In this research, determining the best method of stability is investigated by using of Decision Support systems. DSS is made for this purpose and was used (for Hasan Salaran area in Kurdistan). Field study data from topography, slope, geology, geometry of landslide and the related features was used. The related data entered decision making managements programs (DSS) (ALES).Analysis of mass stability indicated the instability potential at present. Research results show that surface and sub surface drainage the best method of stabilizing. Analysis of stability shows that acceptable increase in security coefficient is a consequence of drainage.
Abstract: This is an application research presenting the
improvement of production quality using the six sigma solutions and
the analyses of benefit-cost ratio. The case of interest is the
production of tile-concrete. Such production has faced with the
problem of high nonconforming products from an inappropriate
surface coating and had low process capability based on the strength
property of tile. Surface coating and tile strength are the most critical
to quality of this product. The improvements followed five stages of
six sigma solutions. After the improvement, the production yield was
improved to 80% as target required and the defective products from
coating process was remarkably reduced from 29.40% to 4.09%. The
process capability based on the strength quality was increased from
0.87 to 1.08 as customer oriented. The improvement was able to save
the materials loss for 3.24 millions baht or 0.11 million dollars. The
benefits from the improvement were analyzed from (1) the reduction
of the numbers of non conforming tile using its factory price for
surface coating improvement and (2) the materials saved from the
increment of process capability. The benefit-cost ratio of overall
improvement was high as 7.03. It was non valuable investment in
define, measure, analyses and the initial of improve stages after that
it kept increasing. This was due to there were no benefits in define,
measure, and analyze stages of six sigma since these three stages
mainly determine the cause of problem and its effects rather than
improve the process. The benefit-cost ratio starts existing in the
improve stage and go on. Within each stage, the individual benefitcost
ratio was much higher than the accumulative one as there was an
accumulation of cost since the first stage of six sigma. The
consideration of the benefit-cost ratio during the improvement
project helps make decisions for cost saving of similar activities
during the improvement and for new project. In conclusion, the
determination of benefit-cost ratio behavior through out six sigma
implementation period provides the useful data for managing quality
improvement for the optimal effectiveness. This is the additional
outcome from the regular proceeding of six sigma.
Abstract: The objective of this paper is to study the electrical
resistivity complexity between field and laboratory measurement, in
order to improve the effectiveness of data interpretation for
geophysical ground resistivity survey. The geological outcrop in
Penang, Malaysia with an obvious layering contact was chosen as the
study site. Two dimensional geoelectrical resistivity imaging were
used in this study to maps the resistivity distribution of subsurface,
whereas few subsurface sample were obtained for laboratory
advance. In this study, resistivity of samples in original conditions is
measured in laboratory by using time domain low-voltage technique,
particularly for granite core sample and soil resistivity measuring set
for soil sample. The experimentation results from both schemes are
studied, analyzed, calibrated and verified, including basis and
correlation, degree of tolerance and characteristics of substance.
Consequently, the significant different between both schemes is
explained comprehensively within this paper.
Abstract: Parallel programming models exist as an abstraction
of hardware and memory architectures. There are several parallel
programming models in commonly use; they are shared memory
model, thread model, message passing model, data parallel model,
hybrid model, Flynn-s models, embarrassingly parallel computations
model, pipelined computations model. These models are not specific
to a particular type of machine or memory architecture. This paper
expresses the model program for concurrent approach to data parallel
model through java programming.
Abstract: This study performs a comparative analysis of the 21 Greek Universities in terms of their public funding, awarded for covering their operating expenditure. First it introduces a DEA/MCDM model that allocates the fund into four expenditure factors in the most favorable way for each university. Then, it presents a common, consensual assessment model to reallocate the amounts, remaining in the same level of total public budget. From the analysis it derives that a number of universities cannot justify the public funding in terms of their size and operational workload. For them, the sufficient reduction of their public funding amount is estimated as a future target. Due to the lack of precise data for a number of expenditure criteria, the analysis is based on a mixed crisp-ordinal data set.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.
Abstract: This paper presents a simplified version of Data Envelopment Analysis (DEA) - a conventional approach to evaluating the performance and ranking of competitive objects characterized by two groups of factors acting in opposite directions: inputs and outputs. DEA with a Perfect Object (DEA PO) augments the group of actual objects with a virtual Perfect Object - the one having greatest outputs and smallest inputs. It allows for obtaining an explicit analytical solution and making a step to an absolute efficiency. This paper develops this approach further and introduces a DEA model with Partially Perfect Objects. DEA PPO consecutively eliminates the smallest relative inputs or greatest relative outputs, and applies DEA PO to the reduced collections of indicators. The partial efficiency scores are combined to get the weighted efficiency score. The computational scheme remains simple, like that of DEA PO, but the advantage of the DEA PPO is taking into account all of the inputs and outputs for each actual object. Firm evaluation is considered as an example.
Abstract: A conventional binding method for low power in a
high-level synthesis mainly focuses on finding an optimal binding for
an assumed input data, and obtains only one binding table. In this
paper, we show that a binding method which uses multiple binding
tables gets better solution compared with the conventional methods
which use a single binding table, and propose a dynamic bus binding
scheme for low power using multiple binding tables. The proposed
method finds multiple binding tables for the proper partitions of an
input data, and switches binding tables dynamically to produce the
minimum total switching activity. Experimental result shows that the
proposed method obtains a binding solution having 12.6-28.9%
smaller total switching activity compared with the conventional
methods.
Abstract: Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and over-fitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.
Abstract: Software estimation accuracy is among the greatest
challenges for software developers. This study aimed at building and
evaluating a neuro-fuzzy model to estimate software projects
development time. The forty-one modules developed from ten
programs were used as dataset. Our proposed approach is compared
with fuzzy logic and neural network model and Results show that the
value of MMRE (Mean of Magnitude of Relative Error) applying
neuro-fuzzy was substantially lower than MMRE applying fuzzy
logic and neural network.
Abstract: MinC plays an important role in bacterial cell division
system by inhibiting FtsZ assembly. However, the molecular
mechanism of the action is poorly understood. E. coli MinC Nterminus
domain was purified and crystallized using 1.4 M sodium
citrate pH 6.5 as a precipitant. X-ray diffraction data was collected
and processed to 2.3 Å from a native crystal. The crystal belonged to
space group P212121, with the unit cell parameters a = 52.7, b = 54.0,
c = 64.7 Å. Assuming the presence of two molecules in the
asymmetric unit, the Matthews coefficient value is 1.94 Å3 Da-1,
which corresponds to a solvent content of 36.5%. The overall
structure of MinCN is observed as a dimer form through anti-parallel
ß-strand interaction.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: Analytical solution of the first-order and third-order
shear deformation theories are developed to study the free vibration
behavior of simply supported functionally graded plates. The
material properties of plate are assumed to be graded in the thickness
direction as a power law distribution of volume fraction of the
constituents. The governing equations of functionally graded plates
are established by applying the Hamilton's principle and are solved
by using the Navier solution method. The influence of side-tothickness
ratio and constituent of volume fraction on the natural
frequencies are studied. The results are validated with the known
data in the literature.
Abstract: The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.
Abstract: In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process