Abstract: In recent years with the rapid development of Internet and the Web, more and more web applications have been deployed in many fields and organizations such as finance, military, and government. Together with that, hackers have found more subtle ways to attack web applications. According to international statistics, SQL Injection is one of the most popular vulnerabilities of web applications. The consequences of this type of attacks are quite dangerous, such as sensitive information could be stolen or authentication systems might be by-passed. To mitigate the situation, several techniques have been adopted. In this research, a security solution is proposed using Artificial Neural Network to protect web applications against this type of attacks. The solution has been experimented on sample datasets and has given promising result. The solution has also been developed in a prototypic web application firewall called ANNbWAF.
Abstract: Computation of facility location problem for every
location in the country is not easy simultaneously. Solving the
problem is described by using cluster computing. A technique is to
design parallel algorithm by using local search with single swap
method in order to solve that problem on clusters. Parallel
implementation is done by the use of portable parallel programming,
Message Passing Interface (MPI), on Microsoft Windows Compute
Cluster. In this paper, it presents the algorithm that used local search
with single swap method and implementation of the system of a
facility to be opened by using MPI on cluster. If large datasets are
considered, the process of calculating a reasonable cost for a facility
becomes time consuming. The result shows parallel computation of
facility location problem on cluster speedups and scales well as
problem size increases.
Abstract: Cognitive Science appeared about 40 years ago,
subsequent to the challenge of the Artificial Intelligence, as common
territory for several scientific disciplines such as: IT, mathematics,
psychology, neurology, philosophy, sociology, and linguistics. The
new born science was justified by the complexity of the problems
related to the human knowledge on one hand, and on the other by the
fact that none of the above mentioned sciences could explain alone
the mental phenomena. Based on the data supplied by the
experimental sciences such as psychology or neurology, models of
the human mind operation are built in the cognition science. These
models are implemented in computer programs and/or electronic
circuits (specific to the artificial intelligence) – cognitive systems –
whose competences and performances are compared to the human
ones, leading to the psychology and neurology data reinterpretation,
respectively to the construction of new models. During these
processes if psychology provides the experimental basis, philosophy
and mathematics provides the abstraction level utterly necessary for
the intermission of the mentioned sciences.
The ongoing general problematic of the cognitive approach
provides two important types of approach: the computational one,
starting from the idea that the mental phenomenon can be reduced to
1 and 0 type calculus operations, and the connection one that
considers the thinking products as being a result of the interaction
between all the composing (included) systems. In the field of
psychology measurements in the computational register use classical
inquiries and psychometrical tests, generally based on calculus
methods. Deeming things from both sides that are representing the
cognitive science, we can notice a gap in psychological product
measurement possibilities, regarded from the connectionist
perspective, that requires the unitary understanding of the quality –
quantity whole. In such approach measurement by calculus proves to
be inefficient. Our researches, deployed for longer than 20 years,
lead to the conclusion that measuring by forms properly fits to the
connectionism laws and principles.
Abstract: The ever-growing usage of aspect-oriented
development methodology in the field of software engineering
requires tool support for both research environments and industry. So
far, tool support for many activities in aspect-oriented software
development has been proposed, to automate and facilitate their
development. For instance, the AJaTS provides a transformation
system to support aspect-oriented development and refactoring. In
particular, it is well established that the abstract interpretation of
programs, in any paradigm, pursued in static analysis is best served
by a high-level programs representation, such as Control Flow Graph
(CFG). This is why such analysis can more easily locate common
programmatic idioms for which helpful transformation are already
known as well as, association between the input program and
intermediate representation can be more closely maintained.
However, although the current researches define the good concepts
and foundations, to some extent, for control flow analysis of aspectoriented
programs but they do not provide a concrete tool that can
solely construct the CFG of these programs. Furthermore, most of
these works focus on addressing the other issues regarding Aspect-
Oriented Software Development (AOSD) such as testing or data flow
analysis rather than CFG itself. Therefore, this study is dedicated to
build an aspect-oriented control flow graph construction tool called
AJcFgraph Builder. The given tool can be applied in many software
engineering tasks in the context of AOSD such as, software testing,
software metrics, and so forth.
Abstract: This work has been carried out in order to provide an understanding of the physical behaviors of the flow variation of pressure and temperature in a vortex tube. A computational fluid dynamics model is used to predict the flow fields and the associated temperature separation within a Ranque–Hilsch vortex tube. The CFD model is a steady axisymmetric model (with swirl) that utilizes the standard k-ε turbulence model. The second–order numerical schemes, was used to carry out all the computations. Vortex tube with a circumferential inlet stream and an axial (cold) outlet stream and a circumferential (hot) outlet stream was considered. Performance curves (temperature separation versus cold outlet mass fraction) were obtained for a specific vortex tube with a given inlet mass flow rate. Simulations have been carried out for varying amounts of cold outlet mass flow rates. The model results have a good agreement with experimental data.
Abstract: Instead of traditional (nominal) classification we investigate
the subject of ordinal classification or ranking. An enhanced
method based on an ensemble of Support Vector Machines (SVM-s)
is proposed. Each binary classifier is trained with specific weights
for each object in the training data set. Experiments on benchmark
datasets and synthetic data indicate that the performance of our
approach is comparable to state of the art kernel methods for
ordinal regression. The ensemble method, which is straightforward
to implement, provides a very good sensitivity-specificity trade-off
for the highest and lowest rank.
Abstract: The study identified the sources of production
inefficiency of the farming sector in district Faisalabad in the Punjab
province of Pakistan. Data Envelopment Analysis (DEA) technique
was utilized at farm level survey data of 300 farmers for the year
2009. The overall mean efficiency score was 0.78 indicating 22
percent inefficiency of the sample farmers. Computed efficiency
scores were then regressed on farm specific variables using Tobit
regression analysis. Farming experience, education, access to
farming credit, herd size and number of cultivation practices showed
constructive and significant effect on the farmer-s technical
efficiency.
Abstract: In the semiconductor manufacturing process, large
amounts of data are collected from various sensors of multiple
facilities. The collected data from sensors have several different characteristics
due to variables such as types of products, former processes
and recipes. In general, Statistical Quality Control (SQC) methods
assume the normality of the data to detect out-of-control states of
processes. Although the collected data have different characteristics,
using the data as inputs of SQC will increase variations of data,
require wide control limits, and decrease performance to detect outof-
control. Therefore, it is necessary to separate similar data groups
from mixed data for more accurate process control. In the paper,
we propose a regression tree using split algorithm based on Pearson
distribution to handle non-normal distribution in parametric method.
The regression tree finds similar properties of data from different
variables. The experiments using real semiconductor manufacturing
process data show improved performance in fault detecting ability.
Abstract: The aim of this article is to assess the existing
business models used by the banks operating in the CEE countries in
the time period from 2006 till 2011.
In order to obtain research results, the authors performed
qualitative analysis of the scientific literature on bank business
models, which have been grouped into clusters that consist of such
components as: 1) capital and reserves; 2) assets; 3) deposits, and 4)
loans.
In their turn, bank business models have been developed based on
the types of core activities of the banks, and have been divided into
four groups: Wholesale, Investment, Retail and Universal Banks.
Descriptive statistics have been used to analyse the models,
determining mean, minimal and maximal values of constituent
cluster components, as well as standard deviation. The analysis of
the data is based on such bank variable indices as Return on Assets
(ROA) and Return on Equity (ROE).
Abstract: If organizations like Mellat Bank want to identify its
customer market completely to reach its specified goals, it can
segment the market to offer the product package to the right segment.
Our objective is to offer a segmentation model for Iran banking
market in Mellat bank view. The methodology of this project is
combined by “segmentation on the basis of four part-quality
variables" and “segmentation on the basis of different in means".
Required data are gathered from E-Systems and researcher personal
observation. Finally, the research offers the organization that at first
step form a four dimensional matrix with 756 segments using four
variables named value-based, behavioral, activity style, and activity
level, and at the second step calculate the means of profit for every
cell of matrix in two distinguished work level (levels α1:normal
condition and α2: high pressure condition) and compare the segments
by checking two conditions that are 1- homogeneity every segment
with its sub segment and 2- heterogeneity with other segments, and
so it can do the necessary segmentation process. After all, the last
offer (more explained by an operational example and feedback
algorithm) is to test and update the model because of dynamic
environment, technology, and banking system.
Abstract: Data mining incorporates a group of statistical
methods used to analyze a set of information, or a data set. It operates
with models and algorithms, which are powerful tools with the great
potential. They can help people to understand the patterns in certain
chunk of information so it is obvious that the data mining tools have
a wide area of applications. For example in the theoretical chemistry
data mining tools can be used to predict moleculeproperties or
improve computer-assisted drug design. Classification analysis is one
of the major data mining methodologies. The aim of thecontribution
is to create a classification model, which would be able to deal with a
huge data set with high accuracy. For this purpose logistic regression,
Bayesian logistic regression and random forest models were built
using R software. TheBayesian logistic regression in Latent GOLD
software was created as well. These classification methods belong to
supervised learning methods.
It was necessary to reduce data matrix dimension before construct
models and thus the factor analysis (FA) was used. Those models
were applied to predict the biological activity of molecules, potential
new drug candidates.
Abstract: The ability to distinguish missense nucleotide
substitutions that contribute to harmful effect from those that do not
is a difficult problem usually accomplished through functional in
vivo analyses. In this study, instead current biochemical methods, the
effects of missense mutations upon protein structure and function
were assayed by means of computational methods and information
from the databases. For this order, the effects of new missense
mutations in exon 5 of PTEN gene upon protein structure and
function were examined. The gene coding for PTEN was identified
and localized on chromosome region 10q23.3 as the tumor
suppressor gene. The utilization of these methods were shown that
c.319G>A and c.341T>G missense mutations that were recognized in
patients with breast cancer and Cowden disease, could be pathogenic.
This method could be use for analysis of missense mutation in others
genes.
Abstract: Particle detection in very noisy and low contrast images
is an active field of research in image processing. In this article, a
method is proposed for the efficient detection and sizing of subsurface
spherical particles, which is used for the processing of softly fused
Au nanoparticles. Transmission Electron Microscopy is used for
imaging the nanoparticles, and the proposed algorithm has been
tested with the two-dimensional projected TEM images obtained.
Results are compared with the data obtained by transmission optical
spectroscopy, as well as with conventional circular object detection
algorithms.
Abstract: Diffuse viral encephalitis may lack fever and other cardinal signs of infection and hence its distinction from other acute encephalopathic illnesses is challenging. Often, the EEG changes seen routinely are nonspecific and reflect diffuse encephalopathic changes only. The aim of this study was to use nonlinear dynamic mathematical techniques for analyzing the EEG data in order to look for any characteristic diagnostic patterns in diffuse forms of encephalitis.It was diagnosed on clinical, imaging and cerebrospinal fluid criteria in three young male patients. Metabolic and toxic encephalopathies were ruled out through appropriate investigations. Digital EEGs were done on the 3rd to 5th day of onset. The digital EEGs of 5 male and 5 female age and sex matched healthy volunteers served as controls.Two sample t-test indicated that there was no statistically significant difference between the average values in amplitude between the two groups. However, the standard deviation (or variance) of the EEG signals at FP1-F7 and FP2-F8 are significantly higher for the patients than the normal subjects. The regularisation dimension is significantly less for the patients (average between 1.24-1.43) when compared to the normal persons (average between 1.41-1.63) for the EEG signals from all locations except for the Fz-Cz signal. Similarly the wavelet dimension is significantly less (P = 0.05*) for the patients (1.122) when compared to the normal person (1.458). EEGs are subdued in the case of the patients with presence of uniform patterns, manifested in the values of regularisation and wavelet dimensions, when compared to the normal person, indicating a decrease in chaotic nature.
Abstract: This paper proposes a low power SRAM based on
five transistor SRAM cell. Proposed SRAM uses novel word-line
decoding such that, during read/write operation, only selected cell
connected to bit-line whereas, in conventional SRAM (CV-SRAM),
all cells in selected row connected to their bit-lines, which in turn
develops differential voltages across all bit-lines, and this makes
energy consumption on unselected bit-lines. In proposed SRAM
memory array divided into two halves and this causes data-line
capacitance to reduce. Also proposed SRAM uses one bit-line and
thus has lower bit-line leakage compared to CV-SRAM.
Furthermore, the proposed SRAM incurs no area overhead, and has
comparable read/write performance versus the CV-SRAM.
Simulation results in standard 0.25μm CMOS technology shows in
worst case proposed SRAM has 80% smaller dynamic energy
consumption in each cycle compared to CV-SRAM. Besides, energy
consumption in each cycle of proposed SRAM and CV-SRAM
investigated analytically, the results of which are in good agreement
with the simulation results.
Abstract: Due to the environmental and price issues of current
energy crisis, scientists and technologists around the globe are
intensively searching for new environmentally less-impact form of
clean energy that will reduce the high dependency on fossil fuel.
Particularly hydrogen can be produced from biomass via thermochemical
processes including pyrolysis and gasification due to the
economic advantage and can be further enhanced through in-situ
carbon dioxide removal using calcium oxide. This work focuses on
the synthesis and development of the flowsheet for the enhanced
biomass gasification process in PETRONAS-s iCON process
simulation software. This hydrogen prediction model is conducted at
operating temperature between 600 to 1000oC at atmospheric
pressure. Effects of temperature, steam-to-biomass ratio and
adsorbent-to-biomass ratio were studied and 0.85 mol fraction of
hydrogen is predicted in the product gas. Comparisons of the results
are also made with experimental data from literature. The
preliminary economic potential of developed system is RM 12.57 x
106 which equivalent to USD 3.77 x 106 annually shows economic
viability of this process.
Abstract: Clustering large populations is an important problem
when the data contain noise and different shapes. A good clustering
algorithm or approach should be efficient enough to detect clusters
sensitively. Besides space complexity, time complexity also gains
importance as the size grows. Using hierarchies we developed a new
algorithm to split attributes according to the values they have and
choosing the dimension for splitting so as to divide the database
roughly into equal parts as much as possible. At each node we
calculate some certain descriptive statistical features of the data
which reside and by pruning we generate the natural clusters with a
complexity of O(n).
Abstract: There is an urgent need to develop novel
Mycobacterium tuberculosis (Mtb) drugs that are active against drug
resistant bacteria but, more importantly, kill persistent bacteria. Our
study structured based on integrated analysis of metabolic pathways,
small molecule screening and similarity Search in PubChem
Database. Metabolic analysis approaches based on Unified weighted
used for potent target selection. Our results suggest that pantothenate
synthetase (panC) and and 3-methyl-2-oxobutanoate hydroxymethyl
transferase (panB) as a appropriate drug targets. In our study, we
used pantothenate synthetase because of existence inhibitors. We
have reported the discovery of new antitubercular compounds
through ligand based approaches using computational tools.
Abstract: This paper illustrates the use of a combined neural
network model for classification of electrocardiogram (ECG) beats.
We present a trainable neural network ensemble approach to develop
customized electrocardiogram beat classifier in an effort to further
improve the performance of ECG processing and to offer
individualized health care.
We process a three stage technique for detection of premature
ventricular contraction (PVC) from normal beats and other heart
diseases. This method includes a denoising, a feature extraction and a
classification. At first we investigate the application of stationary
wavelet transform (SWT) for noise reduction of the
electrocardiogram (ECG) signals. Then feature extraction module
extracts 10 ECG morphological features and one timing interval
feature. Then a number of multilayer perceptrons (MLPs) neural
networks with different topologies are designed.
The performance of the different combination methods as well as
the efficiency of the whole system is presented. Among them,
Stacked Generalization as a proposed trainable combined neural
network model possesses the highest recognition rate of around 95%.
Therefore, this network proves to be a suitable candidate in ECG
signal diagnosis systems. ECG samples attributing to the different
ECG beat types were extracted from the MIT-BIH arrhythmia
database for the study.
Abstract: Daily production of information and importance of the sequence of produced data in forecasting future performance of market causes analysis of data behavior to become a problem of analyzing time series. But time series that are very complicated, usually are random and as a result their changes considered being unpredictable. While these series might be products of a deterministic dynamical and nonlinear process (chaotic) and as a result be predictable. Point of Chaotic theory view, complicated systems have only chaotically face and as a result they seem to be unregulated and random, but it is possible that they abide by a specified math formula. In this article, with regard to test of strange attractor and biggest Lyapunov exponent probability of chaos on several foreign exchange rates vs. IRR (Iranian Rial) has been investigated. Results show that data in this market have complex chaotic behavior with big degree of freedom.