Abstract: This paper discusses the effectiveness of the EEG signal
for human identification using four or less of channels of two different
types of EEG recordings. Studies have shown that the EEG signal
has biometric potential because signal varies from person to person
and impossible to replicate and steal. Data were collected from 10
male subjects while resting with eyes open and eyes closed in 5
separate sessions conducted over a course of two weeks. Features
were extracted using the wavelet packet decomposition and analyzed
to obtain the feature vectors. Subsequently, the neural networks
algorithm was used to classify the feature vectors. Results show that,
whether or not the subjects- eyes were open are insignificant for a 4–
channel biometrics system with a classification rate of 81%. However,
for a 2–channel system, the P4 channel should not be included if data
is acquired with the subjects- eyes open. It was observed that for 2–
channel system using only the C3 and C4 channels, a classification
rate of 71% was achieved.
Abstract: An attempt has been made several times to identify
and discuss the U.S. experience on the formation of political nation in
political science. The purpose of this research paper is to identify the
main aspects of the formation of civic identity in the United States
and Kazakhstan, through the identification of similarities and
differences that can get practical application in making decisions of
national policy issues in the context of globalization, as well as to
answer the questions “What should unite the citizens of Kazakhstan
to the nation?" and “What should be the dominant identity: civil or
ethnic (national) one?"
Can Kazakhstan being multiethnic country like America, adopt its
experience in the formation of a civic nation? Since it is believed that
the “multi-ethnic state of the population is a characteristic feature of
most modern countries in the world," it states that “inter-ethnic
integration is one of the most important aspects of the problem of
forming a new social community (metaetnic - Kazakh people,
Kazakh nation" [1].
Abstract: Increase in using internet makes some problems that
one of them is "internet anxiety". Internet anxiety is a type of anxious
that people may feel during surfing internet or using internet for their
educational purpose, blogging or streaming to digital libraries. The
goal of this study is evaluating of internet anxiety among the
management students. In this research Ealy's internet anxiety
questionnaire, consists of positive and negative items, is completed
by 310 participants. According to the findings, about 64.7% of them
were equal or below to mean anxiety score (50). The distribution of
internet anxiety scores was normal and there was no meaningful
difference between men-s and women's anxiety level in this sample.
Results also showed that there is no meaningful difference of internet
anxiety level between different fields of study in Management. This
evaluation will help managers to perform gap analysis between the
existent level and the desired one. Future work would be providing
techniques for abating human anxiety while using internet via human
computer interaction techniques.
Abstract: A generalized Digital Modulation Identification algorithm for adaptive demodulator has been developed and presented in this paper. The algorithm developed is verified using wavelet Transform and histogram computation to identify QPSK and QAM with GMSK and M–ary FSK modulations. It has been found that the histogram peaks simplifies the procedure for identification. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB and 12 dB for GMSK and QPSK respectively. When SNR is above 5 dB the throughput of the proposed algorithm is more than 97.8%. The receiver operating characteristics (ROC) has been computed to measure the performance of the proposed algorithm and the analysis shows that the probability of detection (Pd) drops rapidly when SNR is 5 dB and probability of false alarm (Pf) is smaller than 0.3. The performance of the proposed algorithm has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.
Abstract: Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.
Abstract: Approximate tandem repeats in a genomic sequence are
two or more contiguous, similar copies of a pattern of nucleotides.
They are used in DNA mapping, studying molecular evolution
mechanisms, forensic analysis and research in diagnosis of inherited
diseases. All their functions are still investigated and not well
defined, but increasing biological databases together with tools for
identification of these repeats may lead to discovery of their specific
role or correlation with particular features. This paper presents a new
approach for finding approximate tandem repeats in a given sequence,
where the similarity between consecutive repeats is measured using
the Hamming distance. It is an enhancement of a method for finding
exact tandem repeats in DNA sequences based on the Burrows-
Wheeler transform.
Abstract: Brucellosis is a zoonotic disease; its symptoms and appearances are not exclusive in human and its traditional diagnosis is based on culture, serological methods and conventional PCR. For more sensitive, specific detection and differentiation of Brucella spp., the real time PCR method is recommended. This research has performed to determine the presence and prevalence of Brucella spp. and differentiation of Brucella abortus and Brucella melitensis in house mouse (Mus musculus) in west of Iran. A TaqMan analysis and single-step PCR was carried out in total 326 DNA of Mouse's spleen samples. From the total number of 326 samples, 128 (39.27%) gave positive results for Brucella spp. by conventional PCR, also 65 and 32 out of the 128 specimens were positive for B. melitensis, B. abortus, respectively. These results indicate a high presence of this pathogen in this area and that real time PCR is considerably faster than current standard methods for identification and differentiation of Brucella species. To our knowledge, this study is the first prevalence report of direct identification and differentiation of B. abortus and B. melitensis by real time PCR in mouse tissue samples in Iran.
Abstract: This paper proposes a visual cryptography by random
grids scheme with identifiable shares. The method encodes an image
O in two shares that exhibits the following features: (1) each generated
share has the same scale as O, (2) any share singly has noise-like
appearance that reveals no secret information on O, (3) the secrets can
be revealed by superimposing the two shares, (4) folding a share up
can disclose some identification patterns, and (5) both of the secret
information and the designated identification patterns are recognized
by naked eye without any computation. The property to show up
identification patterns on folded shares establishes a simple and
friendly interface for users to manage the numerous shares created by
VC schemes.
Abstract: The aim of the study is to determine the effects of
perceived organizational support on organizational identification. In
accordance with this purpose was applied on 131 family physicians in
Konya. The data obtained by means of the survey method were
analyzed. According to the results of correlation analysis, while
positive relationship between perceived organizational support,
organizational identification and supervisor support was revealed.
Also, with the scope of the research, relationships between these
variables and certain demographic variables were detected.
According to difference analysis results of the research, significant
differences between organizational identification and gender variable
were determined. However, significant differences were not
determined between demographic variables and perceived
organizational support.
Abstract: Contemporary science and technologies largely widen
the gap between the spiritual and rational of the society. Industrial
and technological breakthroughs might radically affect most
processes in the society, thus losing the cultural heritage. The
thinkers recognized the dangers of the decadence in the first place. In
the present article the ways of preserving cultural heritage have been
investigated. Memory has always been a necessary condition for selfidentification,
- continuity is based on this. The authors have
supported the hypothesis that continuity and ethnic memory are the
very mechanisms that preserve cultural heritage. Such problemformulating
will facilitate another, new look at the material, spiritual
and arts spheres of the cultural heritage of numerous ethnic groups.
The fundamental works by major European and Kazakh scientists
have been taken as a basis for the research done.
Abstract: Iris localization is a very important approach in
biometric identification systems. Identification process usually is
implemented in three levels: iris localization, feature extraction, and
pattern matching finally. Accuracy of iris localization as the first step
affects all other levels and this shows the importance of iris
localization in an iris based biometric system. In this paper, we
consider Daugman iris localization method as a standard method,
propose a new method in this field and then analyze and compare the
results of them on a standard set of iris images. The proposed method
is based on the detection of circular edge of iris, and improved by
fuzzy circles and surface energy difference contexts. Implementation
of this method is so easy and compared to the other methods, have a
rather high accuracy and speed. Test results show that the accuracy of
our proposed method is about Daugman method and computation
speed of it is 10 times faster.
Abstract: Microorganisms isolated from water and soil of
Kazakhstan to identify potential high-effective producers of the
arachidonic acid, exhibiting a wide range of physiological activity
and having practical applications were screened. Based on the results
of two independent tests (the test on the sensitivity of the growth
processes of microorganisms to acetylsalicylic acid - an irreversible
inhibitor of PGH-synthase involved in the metabolism of arachidonic
acid and its derivatives, the test for inhibition of peroxidase activity
of membrane-bounding fraction of PGH - synthase by acetylsalicylic
acid) were selected microbial cultures which are potential highproducer
of arachidonic acid. They are characterized by a stable
strong growth in the laboratory conditions. Identification of
microorganism cultures based on morphological, physiological,
biochemical and molecular genetic characteristics was performed.
Abstract: The heterotrophic seedling growth can be defined as a
product of two components: (1) the weight of mobilized seed reserve,
and (2) conversion efficiency of utilized seed reserve to seedling
tissue. The first component can be further divided into (1) initial seed
weight, and (2) the fraction of seed reserve, which is mobilized. The
objective of this study was the identification of the sensitive seedling
growth component(s) in response to drought and salinity stresses.
Two experiments were separately conducted using various salinity
levels (osmotic pressure) of 0, 0.25, 0.50, 0.75, 1, 1.25 and 1.5 MPa
created using NaCl as first experiment and by polyethylene glycol
(drought stress) of 0, 0.2, 0.4, 0.6, 0.8, 1, 1.2 and 1.4 MPa in second
experiment. Seeds of five crops species (Hordeum vulgare, Brassica
napus, Zea mays, Medicago sativa and Medicago scutellata) were
used in each experiment. In both experiments, seedling growth,
fraction of seed reserve utilization and weight of mobilized seed
reserve decreased with increasing drought and salt intensity.
However, drought and salinity stresses had no effect on the
conversion efficiency. It was concluded that the sensitive component
of seedling growth is the weight of mobilized seed reserve.
Abstract: In this paper, we propose a texture feature-based
language identification using wavelet-domain BDIP (block difference
of inverse probabilities) and BVLC (block variance of local
correlation coefficients) features and FFT (fast Fourier transform)
feature. In the proposed method, wavelet subbands are first obtained
by wavelet transform from a test image and denoised by Donoho-s
soft-thresholding. BDIP and BVLC operators are next applied to the
wavelet subbands. FFT blocks are also obtained by 2D (twodimensional)
FFT from the blocks into which the test image is
partitioned. Some significant FFT coefficients in each block are
selected and magnitude operator is applied to them. Moments for each
subband of BDIP and BVLC and for each magnitude of significant
FFT coefficients are then computed and fused into a feature vector. In
classification, a stabilized Bayesian classifier, which adopts variance
thresholding, searches the training feature vector most similar to the
test feature vector. Experimental results show that the proposed
method with the three operations yields excellent language
identification even with rather low feature dimension.
Abstract: The majority of existing predictors for time series are
model-dependent and therefore require some prior knowledge for the
identification of complex systems, usually involving system
identification, extensive training, or online adaptation in the case of
time-varying systems. Additionally, since a time series is usually
generated by complex processes such as the stock market or other
chaotic systems, identification, modeling or the online updating of
parameters can be problematic. In this paper a model-free predictor
(MFP) for a time series produced by an unknown nonlinear system or
process is derived using tracking theory. An identical derivation of the
MFP using the property of the Newton form of the interpolating
polynomial is also presented. The MFP is able to accurately predict
future values of a time series, is stable, has few tuning parameters and
is desirable for engineering applications due to its simplicity, fast
prediction speed and extremely low computational load. The
performance of the proposed MFP is demonstrated using the
prediction of the Dow Jones Industrial Average stock index.
Abstract: Needs of an efficient information retrieval in recent
years in increased more then ever because of the frequent use of
digital information in our life. We see a lot of work in the area of
textual information but in multimedia information, we cannot find
much progress. In text based information, new technology of data
mining and data marts are now in working that were started from the
basic concept of database some where in 1960.
In image search and especially in image identification,
computerized system at very initial stages. Even in the area of image
search we cannot see much progress as in the case of text based
search techniques. One main reason for this is the wide spread roots
of image search where many area like artificial intelligence,
statistics, image processing, pattern recognition play their role. Even
human psychology and perception and cultural diversity also have
their share for the design of a good and efficient image recognition
and retrieval system.
A new object based search technique is presented in this paper
where object in the image are identified on the basis of their
geometrical shapes and other features like color and texture where
object-co-relation augments this search process.
To be more focused on objects identification, simple images are
selected for the work to reduce the role of segmentation in overall
process however same technique can also be applied for other
images.
Abstract: This paper mainly proposes an efficient modified
particle swarm optimization (MPSO) method, to identify a slidercrank
mechanism driven by a field-oriented PM synchronous motor.
In system identification, we adopt the MPSO method to find
parameters of the slider-crank mechanism. This new algorithm is
added with “distance" term in the traditional PSO-s fitness function to
avoid converging to a local optimum. It is found that the comparisons
of numerical simulations and experimental results prove that the
MPSO identification method for the slider-crank mechanism is
feasible.
Abstract: Jayanti-s algorithm is one of the best known abortable mutual exclusion algorithms. This work is an attempt to overcome an already known limitation of the algorithm while preserving its all important properties and elegance. The limitation is that the token number used to assign process identification number to new incoming processes is unbounded. We have used a suitably adapted alternative data structure, in order to completely eliminate the use of token number, in the algorithm.
Abstract: The bridge vibration due to traffic loading has been a
subject of extensive research during the last decades. A number of
these studies are concerned with the effects of the unevenness of
roadways on the dynamic responses of highway bridges. The road
unevenness is often described as a random process that constitutes
of different wavelengths. Thus, the study focuses on examining
the effects of the random description of roadways on the dynamic
response and its variance. A new setting of variance based sensitivity
analysis is proposed and used to identify and quantify the
contributions of the roadway-s wavelengths to the variance of the
dynamic response. Furthermore, the effect of the vehicle-s speed on
the dynamic response is studied.
Abstract: Computer worm detection is commonly performed by
antivirus software tools that rely on prior explicit knowledge of the
worm-s code (detection based on code signatures). We present an
approach for detection of the presence of computer worms based on
Artificial Neural Networks (ANN) using the computer's behavioral
measures. Identification of significant features, which describe the
activity of a worm within a host, is commonly acquired from security
experts. We suggest acquiring these features by applying feature
selection methods. We compare three different feature selection
techniques for the dimensionality reduction and identification of the
most prominent features to capture efficiently the computer behavior
in the context of worm activity. Additionally, we explore three
different temporal representation techniques for the most prominent
features. In order to evaluate the different techniques, several
computers were infected with five different worms and 323 different
features of the infected computers were measured. We evaluated
each technique by preprocessing the dataset according to each one
and training the ANN model with the preprocessed data. We then
evaluated the ability of the model to detect the presence of a new
computer worm, in particular, during heavy user activity on the
infected computers.