Abstract: On-line (near infrared) spectroscopy is widely used to support the operation of complex process systems. Information extracted from spectral database can be used to estimate unmeasured product properties and monitor the operation of the process. These techniques are based on looking for similar spectra by nearest neighborhood algorithms and distance based searching methods. Search for nearest neighbors in the spectral space is an NP-hard problem, the computational complexity increases by the number of points in the discrete spectrum and the number of samples in the database. To reduce the calculation time some kind of indexing could be used. The main idea presented in this paper is to combine indexing and visualization techniques to reduce the computational requirement of estimation algorithms by providing a two dimensional indexing that can also be used to visualize the structure of the spectral database. This 2D visualization of spectral database does not only support application of distance and similarity based techniques but enables the utilization of advanced clustering and prediction algorithms based on the Delaunay tessellation of the mapped spectral space. This means the prediction has not to use the high dimension space but can be based on the mapped space too. The results illustrate that the proposed method is able to segment (cluster) spectral databases and detect outliers that are not suitable for instance based learning algorithms.
Abstract: Subcritical water extraction was investigated as a
novel and alternative technology in the food and pharmaceutical
industry for the separation of Mannitol from olive leaves and its
results was compared with those of Soxhlet extraction. The effects of
temperature, pressure, and flow rate of water and also momentum
and mass transfer dimensionless variables such as Reynolds and
Peclet Numbers on extraction yield and equilibrium partition
coefficient were investigated. The 30-110 bars, 60-150°C, and flow
rates of 0.2-2 mL/min were the water operating conditions. The
results revealed that the highest Mannitol yield was obtained at
100°C and 50 bars. However, extraction of Mannitol was not
influenced by the variations of flow rate. The mathematical modeling
of experimental measurements was also investigated and the model is
capable of predicting the experimental measurements very well. In
addition, the results indicated higher extraction yield for the
subcritical water extraction in contrast to Soxhlet method.
Abstract: An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.
Abstract: In this paper, an extended method of the directionally constrained minimization of power (DCMP) algorithm for broadband signals is proposed. The DCMP algorithm is one of the useful techniques of extracting a target signal from observed signals of a microphone array system. In the DCMP algorithm, output power of the microphone array is minimized under a constraint of constant responses to directions of arrival (DOAs) of specific signals. In our algorithm, by limiting the directional constraint to the perpendicular direction to the sensor array system, the calculating time is reduced.
Abstract: It is important for an autonomous mobile robot to know
where it is in any time in an indoor environment. In this paper, we
design a relative self-localization algorithm. The algorithm compare
the interest point in two images and compute the relative displacement
and orientation to determent the posture. Firstly, we use the SURF
algorithm to extract the interest points of the ceiling. Second, in order
to reduce amount of calculation, a replacement SURF is used to extract
orientation and description of the interest points. At last, according to
the transformation of the interest points in two images, the relative
self-localization of the mobile robot will be estimated greatly.
Abstract: We present a novel scheme to recognize isolated speech
signals using certain statistical parameters derived from those signals.
The determination of the statistical estimates is based on extracted
signal information rather than the original signal information in
order to reduce the computational complexity. Subtle details of
these estimates, after extracting the speech signal from ambience
noise, are first exploited to segregate the polysyllabic words from
the monosyllabic ones. Precise recognition of each distinct word is
then carried out by analyzing the histogram, obtained from these
information.
Abstract: Text Mining is an important step of Knowledge
Discovery process. It is used to extract hidden information from notstructured
o semi-structured data. This aspect is fundamental because
much of the Web information is semi-structured due to the nested
structure of HTML code, much of the Web information is linked,
much of the Web information is redundant. Web Text Mining helps
whole knowledge mining process to mining, extraction and
integration of useful data, information and knowledge from Web
page contents.
In this paper, we present a Web Text Mining process able to
discover knowledge in a distributed and heterogeneous multiorganization
environment. The Web Text Mining process is based on
flexible architecture and is implemented by four steps able to
examine web content and to extract useful hidden information
through mining techniques. Our Web Text Mining prototype starts
from the recovery of Web job offers in which, through a Text Mining
process, useful information for fast classification of the same are
drawn out, these information are, essentially, job offer place and
skills.
Abstract: An experimental investigation was performed on pulp
liquid flow in straight ducts with a square cross section. Fully
developed steady flow was visualized and the fiber concentration was
obtained using a light-section method developed by the author et al.
The obtained results reveal quantitatively, in a definite form, the
distribution of the fiber concentration. From the results and
measurements of pressure loss, it is found that the flow characteristics
of pulp liquid in ducts can be classified into five patterns. The
relationships among the distributions of mean and fluctuation of fiber
concentration, the pressure loss and the flow velocity are discussed,
and then the features for each pattern are extracted. The degree of
nonuniformity of the fiber concentration, which is indicated by the
standard deviation of its distribution, is decreased from 0.3 to 0.05
with an increase in the velocity of the tested pulp liquid from 0.4 to
0.8%.
Abstract: Antibacterial activity of Plumeria alba (Frangipani)
petals methanolic extracts were evaluated against Escherichia coli,
Proteus vulgaris,Staphylococcus aureus, Klebsiella pneumoniae,
Pseudomonas aeruginosa, Staphylococcus saprophyticus,
Enterococcus faecalis and Serratia marcescens by using disk
diffusion method. Concentration extracts (80 %) showed the highest
inhibition zone towards Escherichia coli (14.3 mm). Frangipani
extract also showed high antibacterial activity against
Staphylococcus saprophyticus, Proteus vulgaris and Serratia
marcescens, but not more than the zones of the positive control used.
Comparison between two broad specrum antibiotics to frangipani
extracts showed that the 80 % concentration extracts produce the
same zone of inhibition as Streptomycin. Frangipani extracts showed
no bacterial activity towards Klebsiella pneumoniae, Pseudomonas
aeruginosa and Enterococcus faecalis. There are differences in the
sensitivity of different bacteria to frangipani extracts, suggesting that
frangipani-s potency varies between these bacteria. The present
results indicate that frangipani showed significant antibacterial
activity especially to Escherichia coli.
Abstract: This work discusses an innovative methodology for
deployment of service quality characteristics. Four groups of organizational features that may influence the quality of services are identified: human resource, technology, planning, and organizational
relationships. A House of Service Quality (HOSQ) matrix is built to
extract the desired improvement in the service quality characteristics
and to translate them into a hierarchy of important organizational
features. The Mean Square Error (MSE) criterion enables the
pinpointing of the few essential service quality characteristics to be
improved as well as selection of the vital organizational features. The
method was implemented in an engineering supply enterprise and
provides useful information on its vital service dimensions.
Abstract: Narratives are invaluable assets of human lives. Due to
the distinct features of narratives, they are useful for supporting human
reasoning processes. However, many useful narratives become
residuals in organizations or human minds nowadays. Researchers
have contributed effort to investigate and improve narrative generation
processes. This paper attempts to contemplate essential components in
narratives and explore a computational approach to acquire and extract
knowledge to generate narratives. The methodology and significant
benefit for decision support are presented.
Abstract: In this paper a data miner based on the learning
automata is proposed and is called LA-miner. The LA-miner extracts
classification rules from data sets automatically. The proposed
algorithm is established based on the function optimization using
learning automata. The experimental results on three benchmarks
indicate that the performance of the proposed LA-miner is
comparable with (sometimes better than) the Ant-miner (a data miner
algorithm based on the Ant Colony optimization algorithm) and CNZ
(a well-known data mining algorithm for classification).
Abstract: The extract of milk thistle contains a mix of flavonolignans termed silymarine.. In order to analysis influence of growth regulators, genotype, explant and subculture on the accumulation of flavonolignans, a study was carried out by using two genotype (Budakalszi and Noor abad moghan cultivars), cotyledon and hypocotyle explants, solid media of MS supplemented by different combinations of two growth regulators; Kinetin (0.1, 1 mg/l) and 2,4-D (1, 2 mg/l). Seeds of the plant were germinated in MS media whitout growth regulators in growth chamber at 26°C and darkness condition. In order to callus induction, the culture media was supplemented whit different concentrations of 2,4-D and kinetin. Calli obtained from explants were sub-cultured four times into the fresh media of the first experiment. flavonoides was extracted from calli in four subcultures. The flavonoid components were determined by high- performance liquid choromatography (HPLC) and separated into Taxifolin, Silydianin+Silychristin, Silybin A+B and Isosilybin A+B. Results showed that with increasing callus age, increased accumulation of silybin A+B, but reduced Isosilybin A+B content. Highest accumulation of Taxifolin was observed at first calli. Calli produced from cotyledon explant of Budakalszi cultivar were superior for Silybin A+B, where calli from hypocotyl explant produced higher amount of Taxifolin and Silydianin+Silychristin. The best cultivar for Silymarin production in this study was Budakalszi cultivar. High amount of SBN A+B and TXF were obtained from hypocotil explant.
Abstract: This paper describes a novel and effective approach to content-based image retrieval (CBIR) that represents each image in the database by a vector of feature values called “Standard deviation of mean vectors of color distribution of rows and columns of images for CBIR". In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. This paper describes the approach that uses contents as feature vector for retrieval of similar images. There are several classes of features that are used to specify queries: colour, texture, shape, spatial layout. Colour features are often easily obtained directly from the pixel intensities. In this paper feature extraction is done for the texture descriptor that is 'variance' and 'Variance of Variances'. First standard deviation of each row and column mean is calculated for R, G, and B planes. These six values are obtained for one image which acts as a feature vector. Secondly we calculate variance of the row and column of R, G and B planes of an image. Then six standard deviations of these variance sequences are calculated to form a feature vector of dimension six. We applied our approach to a database of 300 BMP images. We have determined the capability of automatic indexing by analyzing image content: color and texture as features and by applying a similarity measure Euclidean distance.
Abstract: Data mining is an extraordinarily demanding field referring to extraction of implicit knowledge and relationships, which are not explicitly stored in databases. A wide variety of methods of data mining have been introduced (classification, characterization, generalization...). Each one of these methods includes more than algorithm. A system of data mining implies different user categories,, which mean that the user-s behavior must be a component of the system. The problem at this level is to know which algorithm of which method to employ for an exploratory end, which one for a decisional end, and how can they collaborate and communicate. Agent paradigm presents a new way of conception and realizing of data mining system. The purpose is to combine different algorithms of data mining to prepare elements for decision-makers, benefiting from the possibilities offered by the multi-agent systems. In this paper the agent framework for data mining is introduced, and its overall architecture and functionality are presented. The validation is made on spatial data. Principal results will be presented.
Abstract: This paper presents a novel method for data hiding based on neighborhood pixels information to calculate the number of bits that can be used for substitution and modified Least Significant Bits technique for data embedding. The modified solution is independent of the nature of the data to be hidden and gives correct results along with un-noticeable image degradation. The technique, to find the number of bits that can be used for data hiding, uses the green component of the image as it is less sensitive to human eye and thus it is totally impossible for human eye to predict whether the image is encrypted or not. The application further encrypts the data using a custom designed algorithm before embedding bits into image for further security. The overall process consists of three main modules namely embedding, encryption and extraction cm.
Abstract: The Automatic Speech Recognition (ASR) applied to
Arabic language is a challenging task. This is mainly related to the
language specificities which make the researchers facing multiple
difficulties such as the insufficient linguistic resources and the very
limited number of available transcribed Arabic speech corpora. In
this paper, we are interested in the development of a HMM-based
ASR system for Standard Arabic (SA) language. Our fundamental
research goal is to select the most appropriate acoustic parameters
describing each audio frame, acoustic models and speech recognition
unit. To achieve this purpose, we analyze the effect of varying frame
windowing (size and period), acoustic parameter number resulting
from features extraction methods traditionally used in ASR, speech
recognition unit, Gaussian number per HMM state and number of
embedded re-estimations of the Baum-Welch Algorithm. To evaluate
the proposed ASR system, a multi-speaker SA connected-digits
corpus is collected, transcribed and used throughout all experiments.
A further evaluation is conducted on a speaker-independent continue
SA speech corpus. The phonemes recognition rate is 94.02% which is
relatively high when comparing it with another ASR system
evaluated on the same corpus.
Abstract: This paper proposes a novel feature extraction method,
based on Discrete Wavelet Transform (DWT) and K-L Seperability
(KLS), for the classification of Functional Data (FD). This method
combines the decorrelation and reduction property of DWT and the
additive independence property of KLS, which is helpful to extraction
classification features of FD. It is an advanced approach of the
popular wavelet based shrinkage method for functional data reduction
and classification. A theory analysis is given in the paper to prove the
consistent convergence property, and a simulation study is also done
to compare the proposed method with the former shrinkage ones. The
experiment results show that this method has advantages in improving
classification efficiency, precision and robustness.
Abstract: Utilization of bagasse ash for silica sources is one of
the most common application for agricultural wastes and valuable
biomass byproducts in sugar milling. The high percentage silica
content from bagasse ash was used as silica source for sodium
silicate solution. Different heating temperature, time and acid
treatment were studies for silica extraction. The silica was
characterized using various techniques including X-ray fluorescence,
X-ray diffraction, Scanning electron microscopy, and Fourier
Transform Infrared Spectroscopy method,. The synthesis conditions
were optimized to obtain the bagasse ash with the maximum silica
content. The silica content of 91.57 percent was achieved from
heating of bagasse ash at 600°C for 3 hours under oxygen feeding
and HCl treatment. The result can be used as value added for bagasse
ash utilization and minimize the environmental impact of disposal
problems.
Abstract: In historical science and social science, the influence
of natural disaster upon society is a matter of great interest. In
recent years, some archives are made through many hands for natural
disasters, however it is inefficiency and waste. So, we suppose a
computer system to create a historical natural disaster archive. As
the target of this analysis, we consider newspaper articles. The news
articles are considered to be typical examples that prescribe the
temporal relations of affairs for natural disaster. In order to do this
analysis, we identify the occurrences in newspaper articles by some
index entries, considering the affairs which are specific to natural
disasters, and show the temporal relation between natural disasters.
We designed and implemented the automatic system of “extraction
of the occurrences of natural disaster" and “temporal relation table
for natural disaster."