Abstract: Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.
Abstract: The changes in quality properties and nutritional
components in two fermented mugworts (Artemisia capillaries
Thumberg, Artemisiaeasiaticae Nakai) were characterized followed
by the rapid pattern analysis of volatile flavor compounds by Electric
Nose based on SAW(Surface Acoustic Wave) sensor in GC system.
There were remarkable decreases in the pH and small changes in the
total soluble solids after fermentation. The L (lightness) and b
(yellowness) values in Hunter's color system were shown to be
decreased, whilst the a (redness) value was increased by fermentation.
The HPLC analysis demonstrated that total amino acids were
increased in quantity and the essential amino acids were contained
higher in A. asiaticaeNakai than in A. capillaries Thumberg. While
the total polyphenol contents were not affected by fermentation, the
total sugar contents were dramatically decreased. Scopoletinwere
highly abundant in A. capillarisThumberg, however, it was not
detected in A. asiaticaeNakai. Volatile flavor compounds by Electric
Nose showed that the intensity of several peaks were increased much
and seven additional flavor peaks were newly produced after
fermentation. The flavor differences of two mugworts were clearly
distinguished from the image patterns of VaporPrintTM which indicate
that the fermentation enables the two mugworts to have subtle flavor
differences.
Abstract: The present study is concerned with the effect of
exciting boundary layer on cooling process in a gas-turbine blades.
The cooling process is numerically investigated. Observations show
cooling the first row of moving or stable blades leads to increase
their life-time. Results show that minimum temperature in cooling
line with exciting boundary layer is lower than without exciting.
Using block in cooling line of turbines' blade causes flow pattern and
stability in boundary layer changed that causes increase in heat
transfer coefficient. Results show at the location of block,
temperature of turbines' blade is significantly decreased. The k-ε
turbulence model is used.
Abstract: In this note, some properties of potentially powerpositive sign patterns are established, and all the potentially powerpositive sign patterns of order ≤ 3 are classified completely.
Abstract: In the recent years multimedia traffic and in particular
VoIP services are growing dramatically. We present a new algorithm
to control the resource utilization and to optimize the voice codec
selection during SIP call setup on behalf of the traffic condition
estimated on the network path.
The most suitable methodologies and the tools that perform realtime
evaluation of the available bandwidth on a network path have
been integrated with our proposed algorithm: this selects the best
codec for a VoIP call in function of the instantaneous available
bandwidth on the path. The algorithm does not require any explicit
feedback from the network, and this makes it easily deployable over
the Internet. We have also performed intensive tests on real network
scenarios with a software prototype, verifying the algorithm
efficiency with different network topologies and traffic patterns
between two SIP PBXs.
The promising results obtained during the experimental validation
of the algorithm are now the basis for the extension towards a larger
set of multimedia services and the integration of our methodology
with existing PBX appliances.
Abstract: Physical urban form is recognized to be the media for
human transactions. It directly influences the travel demand of people
in a specific urban area and the amount of energy used for
transportation. Distorted, sprawling form often creates sustainability
problems in urban areas. It is declared in EU strategic planning
documents that compact urban form and mixed land use pattern must
be given the main focus to achieve better sustainability in urban
areas, but the methods to measure and compare these characteristics
are still not clear.
This paper presents the simple methods to measure the spatial
characteristics of urban form by analyzing the location and
distribution of objects in an urban environment. The extended CA
(cellular automata) model is used to simulate urban development
scenarios.
Abstract: The crossed cube is one of the most notable variations of hypercube, but some properties of the former are superior to those of the latter. For example, the diameter of the crossed cube is almost the half of that of the hypercube. In this paper, we focus on the problem embedding a Hamiltonian cycle through an arbitrary given edge in the crossed cube. We give necessary and sufficient condition for determining whether a given permutation with n elements over Zn generates a Hamiltonian cycle pattern of the crossed cube. Moreover, we obtain a lower bound for the number of different Hamiltonian cycles passing through a given edge in an n-dimensional crossed cube. Our work extends some recently obtained results.
Abstract: Bio-chips are used for experiments on genes and
contain various information such as genes, samples and so on. The
two-dimensional bio-chips, in which one axis represent genes and the
other represent samples, are widely being used these days. Instead of
experimenting with real genes which cost lots of money and much
time to get the results, bio-chips are being used for biological
experiments. And extracting data from the bio-chips with high
accuracy and finding out the patterns or useful information from such
data is very important. Bio-chip analysis systems extract data from
various kinds of bio-chips and mine the data in order to get useful
information. One of the commonly used methods to mine the data is
classification. The algorithm that is used to classify the data can be
various depending on the data types or number characteristics and so
on. Considering that bio-chip data is extremely large, an algorithm that
imitates the ecosystem such as the ant algorithm is suitable to use as an
algorithm for classification. This paper focuses on finding the
classification rules from the bio-chip data using the Ant Colony
algorithm which imitates the ecosystem. The developed system takes
in consideration the accuracy of the discovered rules when it applies it
to the bio-chip data in order to predict the classes.
Abstract: Knowledge Discovery of Databases (KDD) is the
process of extracting previously unknown but useful and significant
information from large massive volume of databases. Data Mining is
a stage in the entire process of KDD which applies an algorithm to
extract interesting patterns. Usually, such algorithms generate huge
volume of patterns. These patterns have to be evaluated by using
interestingness measures to reflect the user requirements.
Interestingness is defined in different ways, (i) Objective measures
(ii) Subjective measures. Objective measures such as support and
confidence extract meaningful patterns based on the structure of the
patterns, while subjective measures such as unexpectedness and
novelty reflect the user perspective. In this report, we try to brief the
more widely spread and successful subjective measures and propose
a new subjective measure of interestingness, i.e. shocking.
Abstract: The utilization of renewable energy sources in electric
power systems is increasing quickly because of public apprehensions
for unpleasant environmental impacts and increase in the energy
costs involved with the use of conventional energy sources. Despite
the application of these energy sources can considerably diminish the
system fuel costs, they can also have significant influence on the
system reliability. Therefore an appropriate combination of the
system reliability indices level and capital investment costs of system
is vital. This paper presents a hybrid wind/photovoltaic plant, with
the aim of supplying IEEE reliability test system load pattern while
the plant capital investment costs is minimized by applying a hybrid
particle swarm optimization (PSO) / harmony search (HS) approach,
and the system fulfills the appropriate level of reliability.
Abstract: This study investigated the pattern and seasonal index of influenza cases in Thailand. Our results showed that southern Thailand had the highest influenza incidence among the four regions of Thailand (i.e. north, northeast, central and southern Thailand). The influenza pattern in southern Thailand was similar to that of northeastern Thailand. Seasonal index values of influenza cases in Thailand were higher in the hot season than in the wet season. Influenza cases started to increase at the beginning of the hot season (April), reached a maximum in August, rapidly declined in the middle of the wet season and reached the lowest value in December. Seasonal index values for northern Thailand differed from other regions of Thailand.
Abstract: Knowledge Discovery in Databases (KDD) is the process of extracting previously unknown, hidden and interesting patterns from a huge amount of data stored in databases. Data mining is a stage of the KDD process that aims at selecting and applying a particular data mining algorithm to extract an interesting and useful knowledge. It is highly expected that data mining methods will find interesting patterns according to some measures, from databases. It is of vital importance to define good measures of interestingness that would allow the system to discover only the useful patterns. Measures of interestingness are divided into objective and subjective measures. Objective measures are those that depend only on the structure of a pattern and which can be quantified by using statistical methods. While, subjective measures depend only on the subjectivity and understandability of the user who examine the patterns. These subjective measures are further divided into actionable, unexpected and novel. The key issues that faces data mining community is how to make actions on the basis of discovered knowledge. For a pattern to be actionable, the user subjectivity is captured by providing his/her background knowledge about domain. Here, we consider the actionability of the discovered knowledge as a measure of interestingness and raise important issues which need to be addressed to discover actionable knowledge.
Abstract: Organizational innovation favors technological
innovation, but does it also influence technological innovation
persistence? This article investigates empirically the pattern of
technological innovation persistence and tests the potential impact of
organizational innovation using firm-level data from three waves of
the French Community Innovation Surveys. Evidence shows a
positive effect of organizational innovation on technological
innovation persistence, according to various measures of
organizational innovation. Moreover, this impact is more significant
for complex innovators (i.e., those who innovate in both products and
processes). These results highlight the complexity of managing
organizational practices with regard to the firm-s technological
innovation. They also add to comprehension of the drivers of
innovation persistence, through a focus on an often forgotten
dimension of innovation in a broader sense.
Abstract: Machine Translation (MT 3) of English text to its Urdu equivalent is a difficult challenge. Lot of attempts has been made, but a few limited solutions are provided till now. We present a direct approach, using an expert system to translate English text into its equivalent Urdu, using The Unicode Standard, Version 4.0 (ISBN 0-321-18578-1) Range: 0600–06FF. The expert system works with a knowledge base that contains grammatical patterns of English and Urdu, as well as a tense and gender-aware dictionary of Urdu words (with their English equivalents).
Abstract: Mining Sequential Patterns in large databases has become
an important data mining task with broad applications. It is
an important task in data mining field, which describes potential
sequenced relationships among items in a database. There are many
different algorithms introduced for this task. Conventional algorithms
can find the exact optimal Sequential Pattern rule but it takes a
long time, particularly when they are applied on large databases.
Nowadays, some evolutionary algorithms, such as Particle Swarm
Optimization and Genetic Algorithm, were proposed and have been
applied to solve this problem. This paper will introduce a new kind
of hybrid evolutionary algorithm that combines Genetic Algorithm
(GA) with Particle Swarm Optimization (PSO) to mine Sequential
Pattern, in order to improve the speed of evolutionary algorithms
convergence. This algorithm is referred to as SP-GAPSO.
Abstract: Luneberg lens is a new generation of antennas that is
developed in the last few years and inserts itself strongly in
Microwaves, Communications and Telescopes area. The idea of this
research is to improve the radiation pattern by decreasing the side
lobes and increasing the main lobe. The new design is proposed to
work in the X-band. The simulated result and analysis are presented.
Abstract: Type 2 diabetes mellitus (T2DM) is a complex
metabolic disorder that characterized by the presence of high glucose
in blood that cause from insulin resistance and insufficiency due to
deterioration β-cell Langerhans functions. T2DM is commonly
caused by the combination of inherited genetic variations as well as
our own lifestyle. Metallothionein (MT) is a known cysteine-rich
protein responsible in helping zinc homeostasis which is important in
insulin signaling and secretion as well as protection our body from
reactive oxygen species (ROS). MT scavenged ROS and free
radicals in our body happen to be one of the reasons of T2DM and its
complications. The objective of this study was to investigate the
association of MT1A and MT2A polymorphisms between T2DM and
control subjects among Malay populations. This study involved 150
T2DM and 120 Healthy individuals of Malay ethnic with mixed
genders. The genomic DNA was extracted from buccal cells and
amplified for MT1A and MT2A loci; the 347bp and 238bp banding
patterns were respectively produced by mean of the Polymerase
Chain Reaction (PCR). The PCR products were digested with Mlucl
and Tsp451 restriction enzymes respectively and producing
fragments lengths of (158/189/347bp) and (103/135/238bp)
respectively. The ANOVA test was conducted and it shown that there
was a significant difference between diabetic and control subjects for
age, BMI, WHR, SBP, FPG, HBA1C, LDL, TG, TC and family
history with (P0.05). The genotype
frequency for AA, AG and GG of MT1A polymorphisms was 72.7%,
22.7% and 4.7% in cases and 15%, 55% and 30% in control
respectively. As for MT2A, genotype frequency of GG, GC and CC
was 42.7%, 27.3% and 30% in case and 5%, 40% and 55% for
control respectively. Both polymorphisms show significant difference
between two investigated groups with (P=0.000). The Post hoc test
was conducted and shows a significant difference between the
genotypes within each polymorphism (P=0. 000). The MT1A and
MT2A polymorphisms were believed to be the reliable molecular
markers to distinguish the T2DM subjects from healthy individuals in
Malay populations.
Abstract: Numerous concrete structures projects are currently running in Libya as part of a US$50 billion government funding. The
quality of concrete used in 20 different construction projects were assessed based mainly on the concrete compressive strength achieved. The projects are scattered all over the country and are at
various levels of completeness. For most of these projects, the
concrete compressive strength was obtained from test results of a
150mm standard cube mold. Statistical analysis of collected concrete
compressive strengths reveals that the data in general followed a
normal distribution pattern. The study covers comparison and assessment of concrete quality aspects such as: quality control, strength range, data standard deviation, data scatter, and ratio of minimum strength to design strength. Site quality control for these projects ranged from very good to poor according to ACI214 criteria [1]. The ranges (Rg) of the strength (max. strength – min. strength) divided by average strength are from (34% to 160%). Data scatter is
measured as the range (Rg) divided by standard deviation () and is
found to be (1.82 to 11.04), indicating that the range is ±3σ.
International construction companies working in Libya follow
different assessment criteria for concrete compressive strength in lieu
of national unified procedure. The study reveals that assessments of
concrete quality conducted by these construction companies usually
meet their adopted (internal) standards, but sometimes fail to meet
internationally known standard requirements. The assessment of
concrete presented in this paper is based on ACI, British standards
and proposed Libyan concrete strength assessment criteria.
Abstract: ZnS nanoparticles of different size have been
synthesized using a colloidal particles method. Zns nanoparticles
prepared with capping agent (mercaptoethanol) then were
characterized using X-ray diffraction (XRD) and UV-Vis
spectroscopy. The particle size of the nanoparticles calculated from
the XRD patterns has been found in the range 1.85-2.44nm.
Absorption spectra have been obtained using UV-Vis
spectrophotometer to find the optical band gap and the obtained
values have been founded to being range 3.83-4.59eV. It was also
found that energy band gap increase with the increase in molar
capping agent solution.
Abstract: Soil erosion is the most serious problem faced at
global and local level. So planning of soil conservation measures has
become prominent agenda in the view of water basin managers. To
plan for the soil conservation measures, the information on soil
erosion is essential. Universal Soil Loss Equation (USLE), Revised
Universal Soil Loss Equation 1 (RUSLE1or RUSLE) and Modified
Universal Soil Loss Equation (MUSLE), RUSLE 1.06, RUSLE1.06c,
RUSLE2 are most widely used conventional erosion estimation
methods. The essential drawbacks of USLE, RUSLE1 equations are
that they are based on average annual values of its parameters and so
their applicability to small temporal scale is questionable. Also these
equations do not estimate runoff generated soil erosion. So
applicability of these equations to estimate runoff generated soil
erosion is questionable. Data used in formation of USLE, RUSLE1
equations was plot data so its applicability at greater spatial scale
needs some scale correction factors to be induced. On the other hand
MUSLE is unsuitable for predicting sediment yield of small and large
events. Although the new revised forms of USLE like RUSLE 1.06,
RUSLE1.06c and RUSLE2 were land use independent and they have
almost cleared all the drawbacks in earlier versions like USLE and
RUSLE1, they are based on the regional data of specific area and
their applicability to other areas having different climate, soil, land
use is questionable. These conventional equations are applicable for
sheet and rill erosion and unable to predict gully erosion and spatial
pattern of rills. So the research was focused on development of nonconventional
(other than conventional) methods of soil erosion
estimation. When these non-conventional methods are combined with
GIS and RS, gives spatial distribution of soil erosion. In the present
paper the review of literature on non- conventional methods of soil
erosion estimation supported by GIS and RS is presented.