Abstract: This manuscript presents, palmprint recognition by
combining different texture extraction approaches with high accuracy.
The Region of Interest (ROI) is decomposed into different frequencytime
sub-bands by wavelet transform up-to two levels and only the
approximate image of two levels is selected, which is known as
Approximate Image ROI (AIROI). This AIROI has information of
principal lines of the palm. The Competitive Index is used as the
features of the palmprint, in which six Gabor filters of different
orientations convolve with the palmprint image to extract the orientation
information from the image. The winner-take-all strategy
is used to select dominant orientation for each pixel, which is
known as Competitive Index. Further, PCA is applied to select highly
uncorrelated Competitive Index features, to reduce the dimensions of
the feature vector, and to project the features on Eigen space. The
similarity of two palmprints is measured by the Euclidean distance
metrics. The algorithm is tested on Hong Kong PolyU palmprint
database. Different AIROI of different wavelet filter families are also
tested with the Competitive Index and PCA. AIROI of db7 wavelet
filter achievs Equal Error Rate (EER) of 0.0152% and Genuine
Acceptance Rate (GAR) of 99.67% on the palm database of Hong
Kong PolyU.
Abstract: The recent development of Information and Communication Technology (ICT) enables new ways of "democratic" decision-making such as a page-ranking system, which estimates the importance of a web page based on indirect trust on that page shared by diverse group of unorganized individuals. These kinds of "democracy" have not been acclaimed yet in the world of real politics. On the other hand, a large amount of data about personal relations including trust, norms of reciprocity, and networks of civic engagement has been accumulated in a computer-readable form by computer systems (e.g., social networking systems). We can use these relations as a new type of social capital to construct a new democratic decision-making system based on a delegation network. In this paper, we propose an effective decision-making support system, which is based on empowering someone's vote whom you trust. For this purpose, we propose two new techniques: the first is for estimating entire vote distribution from a small number of votes, and the second is for estimating active voter choice to promote voting using a delegation network. We show that these techniques could increase the voting ratio and credibility of the whole decision by agent-based simulations.
Abstract: Martingale model diagnostic for assessing the fit of logistic regression model to recurrent events data are studied. One way of assessing the fit is by plotting the empirical standard deviation of the standardized martingale residual processes. Here we used another diagnostic plot based on martingale residual covariance. We investigated the plot performance under several types of model misspecification. Clearly the method has correctly picked up the wrong model. Also we present a test statistic that supplement the inspection of the two diagnostic. The test statistic power agrees with what we have seen in the plots of the estimated martingale covariance.
Abstract: This study applied the Theory of Planned Behavior
model in predicting dietary behavior among Type 2 diabetics in a
Kenyan environment. The study was conducted for three months
within the diabetic clinic at Kisii Hospital in Nyanza Province in
Kenya and adopted sequential mixed methods design combing both
qualitative and quantitative phases. Qualitative data was analyzed
using grounded theory analysis method. Structural equation modeling
using maximum likelihood was used to analyze quantitative data.
The results based on the common fit indices revealed that the theory
of planned behavior fitted the data acceptably well among the Type 2
diabetes and within dietary behavior {χ2 = 223.3, df = 77, p = .02,
χ2/df = 2.9, n=237; TLI = .93; CFI =.91; RMSEA (90CI) = .090(.039,
.146)}. This implies that the Theory of Planned Behavior holds and
forms a framework for promoting dietary practice among Type 2
diabetics.
Abstract: This paper describes an experimental investigation of
the drying behavior and conditions of rosehip in a convective
cyclone-type dryer. Drying experiments were conducted at air inlet
temperatures of 50, 60 and 70 o C and air velocities of 0.5, 1 and 1.5
ms–1. The parametric values obtained from the experiments were
fitted to the Newton mathematical models. Consequently, the drying
model developed by Newton model showed good agreement with the
data obtained from the experiments. Concluding, it was obtained that;
(i) the temperature is the major effect on the drying process, (ii) air
velocity has low effect on the drying of rosehip, (iii) the C-vitamin is
observed to change according to the temperature, moisture, drying
time and flow types. The changing ratio is found to be in the range of
0.70-0.74.
Abstract: Electrophysiological signals were recorded from primary cultures of dissociated rat cortical neurons coupled to Micro-Electrode Arrays (MEAs). The neuronal discharge patterns may change under varying physiological and pathological conditions. For this reason, we developed a new burst detection method able to identify bursts with peculiar features in different experimental conditions (i.e. spontaneous activity and under the effect of specific drugs). The main feature of our algorithm (i.e. Burst On Hurst), based on the auto-similarity or fractal property of the recorded signal, is the independence from the chosen spike detection method since it works directly on the raw data.
Abstract: Many supervised induction algorithms require discrete
data, even while real data often comes in a discrete
and continuous formats. Quality discretization of continuous
attributes is an important problem that has effects on speed,
accuracy and understandability of the induction models. Usually,
discretization and other types of statistical processes are applied
to subsets of the population as the entire population is practically
inaccessible. For this reason we argue that the discretization
performed on a sample of the population is only an estimate of
the entire population. Most of the existing discretization methods,
partition the attribute range into two or several intervals using
a single or a set of cut points. In this paper, we introduce a
technique by using resampling (such as bootstrap) to generate
a set of candidate discretization points and thus, improving the
discretization quality by providing a better estimation towards
the entire population. Thus, the goal of this paper is to observe
whether the resampling technique can lead to better discretization
points, which opens up a new paradigm to construction of
soft decision trees.
Abstract: The medical studies often require different methods
for parameters selection, as a second step of processing, after the
database-s designing and filling with information. One common
task is the selection of fields that act as risk factors using wellknown
methods, in order to find the most relevant risk factors and
to establish a possible hierarchy between them. Different methods
are available in this purpose, one of the most known being the
binary logistic regression. We will present the mathematical
principles of this method and a practical example of using it in the
analysis of the influence of 10 different psychiatric diagnostics
over 4 different types of offences (in a database made from 289
psychiatric patients involved in different types of offences).
Finally, we will make some observations about the relation
between the risk factors hierarchy established through binary
logistic regression and the individual risks, as well as the results of
Chi-squared test. We will show that the hierarchy built using the
binary logistic regression doesn-t agree with the direct order of risk
factors, even if it was naturally to assume this hypothesis as being
always true.
Abstract: Attachment theory focuses on the bond that develops between child and caretaker and the consequences that this bond has on the childs future relationships. Adolescents attempt to define their identity by experiencing various risky behaviors. The first aim of the study was whether risk taking behavior differs according to attachment styles. The second was to examine risk taking behavior differences according to gender. The third aim of this study was to examine attachment X gender interaction effect for risk taking behavior. And final was to investigate attachment styles differences according to gender. Data were collected from 218 participants (114 female and 104 male) who are university students. The results of this study showed that attachment styles differentiated by risk taking behavior and males had higher risk taking score than females. It was also found out that there was significant attachment X gender interaction effect for risk taking behavior. And finally, the results showed that attachment styles differentiated according to gender.KeywordsAttachment style, risk taking
Abstract: The market transformation in Kazakhstan during the
last two decades has essentially strengthened a gap between
development of urban and rural areas. Implementation of market
institutes, transition from public financing to paid rendering of social
services, change of forms of financing of social and economic
infrastructure have led to strengthening of an economic inequality of
social groups, including growth of stratification of the city and the
village. Sociological survey of urban and rural households in Almaty
city and villages of Almaty region has been carried out within the
international research project “Livelihoods Strategies of Private
Households in Central Asia: A Rural–Urban Comparison in
Kazakhstan and Kyrgyzstan" (Germany, Kazakhstan, Kyrgyzstan).
The analysis of statistical data and results of sociological research of
urban and rural households allows us to reveal issues of territorial
development, to investigate an availability of medical, educational
and other services in the city and the village, to reveal an evaluation
urban and rural dwellers of living conditions, to compare economic
strategies of households in the city and the village.
Abstract: Curriculum is one of the most important inputs in higher education system and for knowing the strong and weak spots of it we need evaluation. The main purpose of this study was to survey of the curriculum quality of Insurance Management field. Case: University of Allameh Taba Tabaee(according to view point of students,alumni,employer and faculty members).Descriptive statistics (mean, tables, percentages, frequency distribution) and inferential statistics (CHI SQUARE) were used to analyze the data. Six criterions considered for the Quality of curriculum: objectives, content, teaching and learning methods, space and facilities, Time, assessment of learning. objectives, teaching and learning methods criterions was desirable level, content criteria was undesirable level, space and facilities, time and assessment of learning were rather desirable level. The quality of curriculum of insurance management field was relatively desirable level.
Abstract: The special constraints of sensor networks impose a
number of technical challenges for employing them. In this review,
we study the issues and existing protocols in three areas: coverage
and routing. We present two types of coverage problems: to
determine the minimum number of sensor nodes that need to perform
active sensing in order to monitor a certain area; and to decide the
quality of service that can be provided by a given sensor network.
While most routing protocols in sensor networks are data-centric,
there are other types of routing protocols as well, such as
hierarchical, location-based, and QoS-aware. We describe and
compare several protocols in each group. We present several multipath
routing protocols and single-path with local repair routing
protocols, which are proposed for recovering from sensor node
crashes. We also discuss some transport layer schemes for reliable
data transmission in lossy wireless channels.
Abstract: A Ground Control System (GCS), which controls Unmanned Aerial Vehicles (UAVs) and monitors their missionrelated data, is one of the major components of UAVs. In fact, some traditional GCSs were built on an expensive, complicated hardware infrastructure with workstations and PCs. In contrast, a GCS on a portable device – such as an Android phone or tablet – takes advantage of its light-weight hardware and the rich User Interface supported by the Android Operating System. We implemented that kind of GCS and called it Ground System Software (GSS) in this paper. In operation, our GSS communicates with UAVs or other GSS via TCP/IP connection to get mission-related data, visualizes it on the device-s screen, and saves the data in its own database. Our study showed that this kind of system will become a potential instrument in UAV-related systems and this kind of topic will appear in many research studies in the near future.
Abstract: Anaerobic Digestion has become a promising
technology for biological transformation of organic fraction of the
municipal solid wastes (MSW). In order to represent the kinetic
behavior of such biological process and thereby to design a reactor
system, development of a mathematical model is essential.
Addressing this issue, a simplistic mathematical model has been
developed for anaerobic digestion of MSW in a continuous flow
reactor unit under homogeneous steady state condition. Upon
simulated hydrolysis, the kinetics of biomass growth and substrate
utilization rate are assumed to follow first order reaction kinetics.
Simulation of this model has been conducted by studying sensitivity
of various process variables. The model was simulated using typical
kinetic data of anaerobic digestion MSW and typical MSW
characteristics of Kolkata. The hydraulic retention time (HRT) and
solid retention time (SRT) time were mainly estimated by varying
different model parameters like efficiency of reactor, influent
substrate concentration and biomass concentration. Consequently,
design table and charts have also been prepared for ready use in the
actual plant operation.
Abstract: The Connection Admission Control (CAC) problem is formulated in this paper as a discrete time optimal control problem. The control variables account for the acceptance/ rejection of new connections and forced dropping of in-progress connections. These variables are constrained to meet suitable conditions which account for the QoS requirements (Link Availability, Blocking Probability, Dropping Probability). The performance index evaluates the total throughput. At each discrete time, the problem is solved as an integer-valued linear programming one. The proposed procedure was successfully tested against suitably simulated data.
Abstract: This paper presents parametric probability density
models for call holding times (CHTs) into emergency call center
based on the actual data collected for over a week in the public
Emergency Information Network (EIN) in Mongolia. When the set of
chosen candidates of Gamma distribution family is fitted to the call
holding time data, it is observed that the whole area in the CHT
empirical histogram is underestimated due to spikes of higher
probability and long tails of lower probability in the histogram.
Therefore, we provide the Gaussian parametric model of a mixture of
lognormal distributions with explicit analytical expressions for the
modeling of CHTs of PSNs. Finally, we show that the CHTs for
PSNs are fitted reasonably by a mixture of lognormal distributions
via the simulation of expectation maximization algorithm. This result
is significant as it expresses a useful mathematical tool in an explicit
manner of a mixture of lognormal distributions.
Abstract: In this paper a combined feature selection method is
proposed which takes advantages of sample domain filtering,
resampling and feature subset evaluation methods to reduce
dimensions of huge datasets and select reliable features. This method
utilizes both feature space and sample domain to improve the process
of feature selection and uses a combination of Chi squared with
Consistency attribute evaluation methods to seek reliable features.
This method consists of two phases. The first phase filters and
resamples the sample domain and the second phase adopts a hybrid
procedure to find the optimal feature space by applying Chi squared,
Consistency subset evaluation methods and genetic search.
Experiments on various sized datasets from UCI Repository of
Machine Learning databases show that the performance of five
classifiers (Naïve Bayes, Logistic, Multilayer Perceptron, Best First
Decision Tree and JRIP) improves simultaneously and the
classification error for these classifiers decreases considerably. The
experiments also show that this method outperforms other feature
selection methods.
Abstract: Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.
Abstract: As one of the big government bank, Bank X is paying attention its performance, so that it can compete. One of them is the existence of organizational culture which recognized with term TIPEC (Trust, Integrity, Professionalism, Costumer Focus, and Excellence). In application of organizational culture, it is needed the existence of employee involvement (job involvement). It can be influenced by various factors, such as Locus of Control. Related to above mentioned, the problems are how employee tendency of Locus of Control, how job involvement, how organizational culture applied by employees and how influence of Locus of Control and job involvement to the organizational culture applied by employees. Researchers collected data with questioner spreading, and respondents number of 30 people. After that, the data were analyzed with SPSS software constructively. The influence of Locus of Control and job involvement to the application of organizational culture was strong, i.e. 58.3%.
Abstract: World has entered in 21st century. The technology of
computer graphics and digital cameras is prevalent. High resolution
display and printer are available. Therefore high resolution images
are needed in order to produce high quality display images and high
quality prints. However, since high resolution images are not usually
provided, there is a need to magnify the original images. One
common difficulty in the previous magnification techniques is that of
preserving details, i.e. edges and at the same time smoothing the data
for not introducing the spurious artefacts. A definitive solution to this
is still an open issue. In this paper an image magnification using
adaptive interpolation by pixel level data-dependent geometrical
shapes is proposed that tries to take into account information about
the edges (sharp luminance variations) and smoothness of the image.
It calculate threshold, classify interpolation region in the form of
geometrical shapes and then assign suitable values inside
interpolation region to the undefined pixels while preserving the
sharp luminance variations and smoothness at the same time.
The results of proposed technique has been compared qualitatively
and quantitatively with five other techniques. In which the qualitative
results show that the proposed method beats completely the Nearest
Neighbouring (NN), bilinear(BL) and bicubic(BC) interpolation. The
quantitative results are competitive and consistent with NN, BL, BC
and others.