Abstract: Data mining idea is mounting rapidly in admiration
and also in their popularity. The foremost aspire of data mining
method is to extract data from a huge data set into several forms that
could be comprehended for additional use. The data mining is a
technology that contains with rich potential resources which could be
supportive for industries and businesses that pay attention to collect
the necessary information of the data to discover their customer’s
performances. For extracting data there are several methods are
available such as Classification, Clustering, Association,
Discovering, and Visualization… etc., which has its individual and
diverse algorithms towards the effort to fit an appropriate model to
the data. STATISTICA mostly deals with excessive groups of data
that imposes vast rigorous computational constraints. These results
trials challenge cause the emergence of powerful STATISTICA Data
Mining technologies. In this survey an overview of the STATISTICA
software is illustrated along with their significant features.
Abstract: The Algeria by its location offers a rich and diverse
vegetation. A large number of aromatic and medicinal plants grow
spontaneously. The interest in these plants has continued to grow in
recent years. Their particular properties due to the essential oil
fraction can be utilized to treat microbial infections. To this end, and
in the context of the valuation of the Algerian flora, we became
interested in the species of the family Lamiaceae which is one of the
most used as a global source of spices. The plant on which we have
based our choice is a species of sage "Salvia officinalis" from the
Isser localized region within the province of Boumerdes. This work
focuses on the study of the antimicrobial activity of essential oil
extracted from the leaves of Salvia officinalis. The extraction is
carried out by essential oil hydrodistillation and reveals a yield of
1.06℅. The study of the antimicrobial activity of the essential oil by
the method of at aromatogramme shown that Gram positive bacteria
are most susceptible (Staphylococcus aureus and Bacillus subtilis)
with a strong inhibition of growth. The yeast Candida albicans
fungus Aspergillus niger and have shown moderately sensitive.
Abstract: Ionic liquids consisting of a phosphonium cationic
moiety and a saccharinate anion are synthesized and compared with
their precursors, phosphonium chlorides, in reference to their
extraction efficiency towards L-lactic acid. On the base of
measurements of the acid and the water partitioning in the
equilibrium biphasic systems, the molar ratios between acid, water
and ionic liquid are estimated which allows to deduce the lactic acid
extractive pathway. The effect of a salting-out addition that
strengthens hydrophobicity in both phases is studied in view to reveal
the best biphasic system with respect to IL low toxicity and high
extraction efficiency.
Abstract: Array-based gene expression analysis is a powerful
tool to profile expression of genes and to generate information on
therapeutic effects of new anti-cancer compounds. Anti-apoptotic
effect of thymoquinone was studied in MCF7 breast cancer cell line
using gene expression profiling with cDNA microarray. The purity
and yield of RNA samples were determined using RNeasyPlus Mini
kit. The Agilent RNA 6000 NanoLabChip kit evaluated the quantity
of the RNA samples. AffinityScript RT oligo-dT promoter primer
was used to generate cDNA strands. T7 RNA polymerase was used to
convert cDNA to cRNA. The cRNA samples and human universal
reference RNA were labelled with Cy-3-CTP and Cy-5-CTP,
respectively. Feature Extraction and GeneSpring softwares analysed
the data. The single experiment analysis revealed involvement of 64
pathways with up-regulated genes and 78 pathways with downregulated
genes. The MAPK and p38-MAPK pathways were
inhibited due to the up-regulation of PTPRR gene. The inhibition of
p38-MAPK suggested up-regulation of TGF-ß pathway. Inhibition of
p38-MAPK caused up-regulation of TP53 and down-regulation of
Bcl2 genes indicating involvement of intrinsic apoptotic pathway.
Down-regulation of CARD16 gene as an adaptor molecule regulated
CASP1 and suggested necrosis-like programmed cell death and
involvement of caspase in apoptosis. Furthermore, down-regulation
of GPCR, EGF-EGFR signalling pathways suggested reduction of
ER. Involvement of AhR pathway which control cytochrome P450
and glucuronidation pathways showed metabolism of Thymoquinone.
The findings showed differential expression of several genes in
apoptosis pathways with thymoquinone treatment in estrogen
receptor-positive breast cancer cells.
Abstract: Despite the highly touted benefits, emerging
technologies have unleashed pervasive concerns regarding unintended
and unforeseen social impacts. Thus, those wishing to create safe and
socially acceptable products need to identify such side effects and
mitigate them prior to the market proliferation. Various methodologies
in the field of technology assessment (TA), namely Delphi, impact
assessment, and scenario planning, have been widely incorporated in
such a circumstance. However, literatures face a major limitation in
terms of sole reliance on participatory workshop activities. They
unfortunately missed out the availability of a massive untapped data
source of futuristic information flooding through the Internet. This
research thus seeks to gain insights into utilization of futuristic data,
future-oriented documents from the Internet, as a supplementary
method to generate social impact scenarios whilst capturing
perspectives of experts from a wide variety of disciplines. To this end,
network analysis is conducted based on the social keywords extracted
from the futuristic documents by text mining, which is then used as a
guide to produce a comprehensive set of detailed scenarios. Our
proposed approach facilitates harmonized depictions of possible
hazardous consequences of emerging technologies and thereby makes
decision makers more aware of, and responsive to, broad qualitative
uncertainties.
Abstract: This work deals with parameter identification of
permanent magnet motors, a class of ac motor which is particularly
important in industrial automation due to characteristics like
applications high performance, are very attractive for applications
with limited space and reducing the need to eliminate because they
have reduced size and volume and can operate in a wide speed range,
without independent ventilation. By using experimental data and
genetic algorithm we have been able to extract values for both the
motor inductance and the electromechanical coupling constant, which
are then compared to measured and/or expected values.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
Abstract: This paper presents two techniques, local feature
extraction using image spectrum and low frequency spectrum
modelling using GMM to capture the underlying statistical
information to improve the performance of face recognition
system. Local spectrum features are extracted using overlap sub
block window that are mapped on the face image. For each of this
block, spatial domain is transformed to frequency domain using
DFT. A low frequency coefficient is preserved by discarding high
frequency coefficients by applying rectangular mask on the
spectrum of the facial image. Low frequency information is non-
Gaussian in the feature space and by using combination of several
Gaussian functions that has different statistical properties, the best
feature representation can be modelled using probability density
function. The recognition process is performed using maximum
likelihood value computed using pre-calculated GMM components.
The method is tested using FERET datasets and is able to achieved
92% recognition rates.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: Iron is an essential nutrient with limited
bioavailability. Nutritional anemia caused mainly by iron deficiency
is the most recognized nutritional problem in both countries as well
as affluent societies. Rice (Oryza sativa L.) has become the most
important cereal crop for the improvement of human health due to the
starch, protein, oil, and the majority of micronutrients, particularly in
Asian countries. In this study, the iron availability and profile lipid
were evaluated for the extracts from Cibeusi varieties (black rices) of
ancient rice brans.
Results: The quality of K, B, R, E diets groups shows the same
effect on the growth of rats. Hematocrit and MCHC levels of rats fed
K, B, R and E diets were not significantly (P
Abstract: Flow blockages referring to the increase in flow are
being considered as a vital equipment for marine current energy
conversion. However, the shape of these devices will result in
extracted energy under the operation. The present work investigates
the effect of two configurations of a grating, convergent and
divergent that located upstream, to the water flow velocity. The flow
characteristics are studied by Computational Fluid Dynamic
simulation by using the ANSYS Fluent solver for these specified
arrangements of the grating. The results indicate that distinguished
characteristics of flow velocity between “convergent” and
“divergent” grating placements is up to 10% in confined conditions.
Furthermore, the velocity in case of convergent grating is higher
than that of divergent grating.
Abstract: Diminished antioxidant defense or increased
production of reactive oxygen species in the biological system can
result in oxidative stress which may lead to various
neurodegenerative diseases including Alzheimer’s disease (AD).
Microglial activation also contributes to the progression of AD by
producing several proinflammatory cytokines, nitric oxide (NO) and
prostaglandin E2 (PGE2). Oxidative stress and inflammation have
been reported to be possible pathophysiological mechanisms
underlying AD. In addition, the cholinergic hypothesis postulates that
memory impairment in patient with AD is also associated with the
deficit of cholinergic function in the brain. Although a number of
drugs have been approved for the treatment of AD, most of these
synthetic drugs have diverse side effects and yield relatively modest
benefits. Marine algae have great potential in pharmaceutical and
biomedical applications as they are valuable sources of bioactive
properties such as anticoagulation, antimicrobial, antioxidative,
anticancer and anti-inflammatory. Hence, this study aimed to provide
an overview of the properties of Malaysian seaweeds (Padina
australis, Sargassum polycystum and Caulerpa racemosa) in
inhibiting oxidative stress, neuroinflammation and cholinesterase
enzymes. These seaweeds significantly exhibited potent DPPH and
moderate superoxide anion radical scavenging ability (P
Abstract: Due to the rapid increase of Internet, web opinion
sources dynamically emerge which is useful for both potential
customers and product manufacturers for prediction and decision
purposes. These are the user generated contents written in natural
languages and are unstructured-free-texts scheme. Therefore, opinion
mining techniques become popular to automatically process customer
reviews for extracting product features and user opinions expressed
over them. Since customer reviews may contain both opinionated and
factual sentences, a supervised machine learning technique applies
for subjectivity classification to improve the mining performance. In
this paper, we dedicate our work is the task of opinion
summarization. Therefore, product feature and opinion extraction is
critical to opinion summarization, because its effectiveness
significantly affects the identification of semantic relationships. The
polarity and numeric score of all the features are determined by
Senti-WordNet Lexicon. The problem of opinion summarization
refers how to relate the opinion words with respect to a certain
feature. Probabilistic based model of supervised learning will
improve the result that is more flexible and effective.
Abstract: Livestock is one of the fastest-growing sectors in
agriculture. If carefully managed, have potential opportunities for
economic growth, food sovereignty and food security. In this study
we mainly analyse and compare long-term i.e. for year 2030 climate
variability impact on predicted productivity of meat i.e. beef, mutton
and poultry for the Kingdom of Saudi Arabia w.r.t three factors i.e. i)
climatic-change vulnerability ii) CO2 fertilization and iii) water
scarcity and compare the results with two countries of the region i.e.
Iraq and Yemen. We do the analysis using data from diverse sources,
which was extracted, transformed and integrated before usage. The
collective impact of the three factors had an overall negative effect on
the production of meat for all the three countries, with adverse impact
on Iraq. High similarity was found between CO2 fertilization
(effecting animal fodder) and water scarcity i.e. higher than that
between production of beef and mutton for the three countries
considered. Overall, the three factors do not seem to be favorable for
the three Middle-East countries considered. This points to possibility
of a vegetarian year 2030 based on dependency on indigenous livestock
population.
Abstract: The rhizome of Java grass, Cyperus rotundus was
extracted different organic polar and non-polar solvents and
performed the in vitro antiviral and immunostimulant activities
against White Spot Syndrome Virus (WSSV) and Vibrio harveyi
respectively. Based on the initial screening the ethyl acetate extract of
C. rotundus was strong activities and further it was purified through
silica column chromatography and the fractions were screened again
for antiviral and immunostimulant activity. Among the different
fractions screened against the WSSV and V. harveyi, the fractions, FIII
to FV had strong activities. In order to study the in vivo influence
of C. rotundus, the fractions (F-III to FV) were pooled and delivered
to the F. indicus through artificial feed for 30 days. After the feeding
trail the experimental and control diet fed F. indicus were challenged
with virulent WSSV and studied the survival, molecular diagnosis,
biochemical, haematological, and immunological parameters.
Surprisingly, the pooled fractions (F-IV to FVI) incorporated diets
helped to significantly (P
Abstract: Blueberries are widely valued for their high content in
phenolic compounds with antioxidant activity, and hence beneficial
for the human health. In this way, a study was done to determine the
phenolic composition (total phenols, anthocyanins and tannins) and
antioxidant activity of blueberries from three cultivars (Duke,
Bluecrop, and Ozarkblue) grown in two different Portuguese farms.
Initially two successive extractions were done with methanol
followed by two extractions with aqueous acetone solutions. These
extracts obtained were then used to evaluate the amount of phenolic
compounds and the antioxidant activity. The total phenols were
observed to vary from 4.9 to 8.2 mg GAE/g fresh weight, with
anthocyanin’s contents in the range 1.5-2.8 mg EMv3G/g and tannins
contents in the range 1.5- 3.8 mg/g. The results for antioxidant
activity ranged from 9.3 to 23.2 molTE/g and from 24.7 to 53.4molTE/g, when measured, respectively, by DPPH and ABTS
methods. In conclusion it was observed that, in general, the cultivar
had a visible effect on the phenols present, and furthermore, the
geographical origin showed relevance either in the phenols contents
or the antioxidant activity.
Abstract: Analysis of the properties of coconut (Cocos nucifera)
and its oil was evaluated in this work using standard analytical
techniques. The analyses carried out include proximate composition
of the fruit, extraction of oil from the fruit using different process
parameters and physicochemical analysis of the extracted oil. The
results showed the percentage (%) moisture, crude lipid, crude
protein, ash and carbohydrate content of the coconut as 7.59, 55.15,
5.65, 7.35 and 19.51 respectively. The oil from the coconut fruit was
odourless and yellowish liquid at room temperature (30oC). The
treatment combinations used (leaching time, leaching temperature
and solute: solvent ratio) showed significant differences (P
Abstract: The aim of this work is to build a model based on
tissue characterization that is able to discriminate pathological and
non-pathological regions from three-phasic CT images. With our
research and based on a feature selection in different phases, we are
trying to design a neural network system with an optimal neuron
number in a hidden layer. Our approach consists of three steps:
feature selection, feature reduction, and classification. For each
region of interest (ROI), 6 distinct sets of texture features are
extracted such as: first order histogram parameters, absolute gradient,
run-length matrix, co-occurrence matrix, autoregressive model, and
wavelet, for a total of 270 texture features. When analyzing more
phases, we show that the injection of liquid cause changes to the high
relevant features in each region. Our results demonstrate that for
detecting HCC tumor phase 3 is the best one in most of the features
that we apply to the classification algorithm. The percentage of
detection between pathology and healthy classes, according to our
method, relates to first order histogram parameters with accuracy of
85% in phase 1, 95% in phase 2, and 95% in phase 3.