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: Accurately predicting non-peak traffic is crucial to
daily traffic for all forecasting models. In the paper, least squares
support vector machines (LS-SVMs) are investigated to solve such a
practical problem. It is the first time to apply the approach and analyze
the forecast performance in the domain. For comparison purpose, two
parametric and two non-parametric techniques are selected because of
their effectiveness proved in past research. Having good
generalization ability and guaranteeing global minima, LS-SVMs
perform better than the others. Providing sufficient improvement in
stability and robustness reveals that the approach is practically
promising.
Abstract: Automatic methods of detecting changes through
satellite imaging are the object of growing interest, especially
beca²use of numerous applications linked to analysis of the Earth’s
surface or the environment (monitoring vegetation, updating maps,
risk management, etc...). This work implemented spatial analysis
techniques by using images with different spatial and spectral
resolutions on different dates. The work was based on the principle
of control charts in order to set the upper and lower limits beyond
which a change would be noted. Later, the a contrario approach was
used. This was done by testing different thresholds for which the
difference calculated between two pixels was significant. Finally,
labeled images were considered, giving a particularly low difference
which meant that the number of “false changes” could be estimated
according to a given limit.
Abstract: A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.
Abstract: Although oil-based drilling fluids are of paramount practical and economical interest, they represent a serious source of pollution, once released into the environment as drill cuttings. The aim of this study is to assess the capability of isolated microorganisms to degrade gasoil fuel. The commonly used physicochemical and biodegradation remediation techniques of petroleum contaminated soil were both investigated. The study revealed that natural biodegradation is favorable. Even though, the presence of heavy metals, the moisture level of (8.55%) and nutrient deficiencies put severe constrains on microorganisms- survival ranges inhibiting the biodegradation process. The selected strains were able to degrade the diesel fuel at significantly high rates (around 98%).
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: Accurate loss minimization is the critical component
for efficient electrical distribution power flow .The contribution of
this work presents loss minimization in power distribution system
through feeder restructuring, incorporating DG and placement of
capacitor. The study of this work was conducted on IEEE
distribution network and India Electricity Board benchmark
distribution system. The executed experimental result of Indian
system is recommended to board and implement practically for
regulated stable output.
Abstract: This paper describes the optimization of a complex
dairy farm simulation model using two quite different methods of
optimization, the Genetic algorithm (GA) and the Lipschitz
Branch-and-Bound (LBB) algorithm. These techniques have been
used to improve an agricultural system model developed by Dexcel
Limited, New Zealand, which describes a detailed representation of
pastoral dairying scenarios and contains an 8-dimensional parameter
space. The model incorporates the sub-models of pasture growth and
animal metabolism, which are themselves complex in many cases.
Each evaluation of the objective function, a composite 'Farm
Performance Index (FPI)', requires simulation of at least a one-year
period of farm operation with a daily time-step, and is therefore
computationally expensive. The problem of visualization of the
objective function (response surface) in high-dimensional spaces is
also considered in the context of the farm optimization problem.
Adaptations of the sammon mapping and parallel coordinates
visualization are described which help visualize some important
properties of the model-s output topography. From this study, it is
found that GA requires fewer function evaluations in optimization
than the LBB algorithm.
Abstract: Image-based Rendering(IBR) techniques recently
reached in broad fields which leads to a critical challenge to build up
IBR-Driven visualization platform where meets requirement of high
performance, large bounds of distributed visualization resource
aggregation and concentration, multiple operators deploying and
CSCW design employing. This paper presents an unique IBR-based
visualization dataflow model refer to specific characters of IBR
techniques and then discusses prominent feature of IBR-Driven
distributed collaborative visualization (DCV) system before finally
proposing an novel prototype. The prototype provides a well-defined
three level modules especially work as Central Visualization Server,
Local Proxy Server and Visualization Aid Environment, by which
data and control for collaboration move through them followed the
previous dataflow model. With aid of this triple hierarchy architecture
of that, IBR oriented application construction turns to be easy. The
employed augmented collaboration strategy not only achieve
convenient multiple users synchronous control and stable processing
management, but also is extendable and scalable.
Abstract: The clustering ensembles combine multiple partitions
generated by different clustering algorithms into a single clustering
solution. Clustering ensembles have emerged as a prominent method
for improving robustness, stability and accuracy of unsupervised
classification solutions. So far, many contributions have been done to
find consensus clustering. One of the major problems in clustering
ensembles is the consensus function. In this paper, firstly, we
introduce clustering ensembles, representation of multiple partitions,
its challenges and present taxonomy of combination algorithms.
Secondly, we describe consensus functions in clustering ensembles
including Hypergraph partitioning, Voting approach, Mutual
information, Co-association based functions and Finite mixture
model, and next explain their advantages, disadvantages and
computational complexity. Finally, we compare the characteristics of
clustering ensembles algorithms such as computational complexity,
robustness, simplicity and accuracy on different datasets in previous
techniques.
Abstract: Orthogonal Frequency Division Multiplexing
(OFDM) is an efficient method of data transmission for high speed
communication systems. However, the main drawback of OFDM
systems is that, it suffers from the problem of high Peak-to-Average
Power Ratio (PAPR) which causes inefficient use of the High Power
Amplifier and could limit transmission efficiency. OFDM consist of
large number of independent subcarriers, as a result of which the
amplitude of such a signal can have high peak values. In this paper,
we propose an effective reduction scheme that combines DCT and
SLM techniques. The scheme is composed of the DCT followed by
the SLM using the Riemann matrix to obtain phase sequences for the
SLM technique. The simulation results show PAPR can be greatly
reduced by applying the proposed scheme. In comparison with
OFDM, while OFDM had high values of PAPR –about 10.4dB our
proposed method achieved about 4.7dB reduction of the PAPR with
low complexities computation. This approach also avoids
randomness in phase sequence selection, which makes it simpler to
decode at the receiver. As an added benefit, the matrices can be
generated at the receiver end to obtain the data signal and hence it is
not required to transmit side information (SI).
Abstract: Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.
Abstract: Chess is one of the indoor games, which improves the
level of human confidence, concentration, planning skills and
knowledge. The main objective of this paper is to help the chess
players to improve their chess openings using data mining
techniques. Budding Chess Players usually do practices by analyzing
various existing openings. When they analyze and correlate
thousands of openings it becomes tedious and complex for them. The
work done in this paper is to analyze the best lines of Blackmar-
Diemer Gambit(BDG) which opens with White D4... using data
mining analysis. It is carried out on the collection of winning games
by applying association rules. The first step of this analysis is
assigning variables to each different sequence moves. In the second
step, the sequence association rules were generated to calculate
support and confidence factor which help us to find the best
subsequence chess moves that may lead to winning position.
Abstract: Determination of nano particle size is substantial since
the nano particle size exerts a significant effect on various properties
of nano materials. Accordingly, proposing non-destructive, accurate
and rapid techniques for this aim is of high interest. There are some
conventional techniques to investigate the morphology and grain size
of nano particles such as scanning electron microscopy (SEM),
atomic force microscopy (AFM) and X-ray diffractometry (XRD).
Vibrational spectroscopy is utilized to characterize different
compounds and applied for evaluation of the average particle size
based on relationship between particle size and near infrared spectra
[1,4] , but it has never been applied in quantitative morphological
analysis of nano materials. So far, the potential application of nearinfrared
(NIR) spectroscopy with its ability in rapid analysis of
powdered materials with minimal sample preparation, has been
suggested for particle size determination of powdered
pharmaceuticals. The relationship between particle size and diffuse
reflectance (DR) spectra in near infrared region has been applied to
introduce a method for estimation of particle size. Back propagation
artificial neural network (BP-ANN) as a nonlinear model was applied
to estimate average particle size based on near infrared diffuse
reflectance spectra. Thirty five different nano TiO2 samples with
different particle size were analyzed by DR-FTNIR spectrometry and
the obtained data were processed by BP- ANN.
Abstract: The Japanese integrative approach to social systems
can be observed in supply chain management as well as in the
relationship between public and private sectors. Both the Lean
Production System and the Developmental State Model are
characterized by efforts towards the achievement of mutual goals,
resulting in initiatives for capacity building which emphasize the
system level. In Brazil, although organizations undertake efforts to
build capabilities at the individual and organizational levels, the
system level is being neglected. Fieldwork data confirmed the findings
of other studies in terms of the lack of integration in supply chain
management in the Brazilian automobile industry. Moreover, due to
the absence of an active role of the Brazilian state in its relationship
with the private sector, automakers are not fully exploiting the
opportunities in the domestic and regional markets. For promoting a
higher level of economic growth as well as to increase the degree of
spill-over of technologies and techniques, a more integrative approach
is needed.
Abstract: Direct conversion of methane to methanol by partial oxidation in a thermal reactor has a poor yield of about 2% which is less than the expected economical yield of about 10%. Conventional thermal catalytic reactors have been proposed to be superseded by plasma reactors as a promising approach, due to strength of the electrical energy which can break C-H bonds of methane. Among the plasma techniques, non-thermal dielectric barrier discharge (DBD) plasma chemical process is one of the most future promising technologies in synthesizing methanol. The purpose of this paper is presenting a brief review of CH4 oxidation with O2 in DBD plasma reactors based on the recent investigations. For this reason, the effect of various parameters of reactor configuration, feed ratio, applied voltage, residence time (gas flow rate), type of applied catalyst, pressure and reactor wall temperature on methane conversion and methanol selectivity are discussed.
Abstract: The problem of exponential stability and periodicity for a class of cellular neural networks (DCNNs) with time-varying delays is investigated. By dividing the network state variables into subgroups according to the characters of the neural networks, some sufficient conditions for exponential stability and periodicity are derived via the methods of variation parameters and inequality techniques. These conditions are represented by some blocks of the interconnection matrices. Compared with some previous methods, the method used in this paper does not resort to any Lyapunov function, and the results derived in this paper improve and generalize some earlier criteria established in the literature cited therein. Two examples are discussed to illustrate the main results.
Abstract: Wind turbines with double output induction
generators can operate at variable speed permitting conversion
efficiency maximization over a wide range of wind velocities. This
paper presents the performance analysis of a wind driven double
output induction generator (DOIG) operating at varying shafts speed.
A periodic transient state analysis of DOIG equipped with two
converters is carried out using a hybrid induction machine model.
This paper simulates the harmonic content of waveforms in various
points of drive at different speeds, based on the hybrid model
(dqabc). Then the sinusoidal and trapezoidal pulse-width–modulation
control techniques are used in order to improve the power factor of
the machine and to weaken the injected low order harmonics to the
supply. Based on the frequency spectrum, total harmonics distortion,
distortion factor and power factor. Finally advantages of sinusoidal
and trapezoidal pulse width modulation techniques are compared.
Abstract: Environmental aspects plays a central role in environmental management system (EMS) because it is the basis for the identification of an organization-s environmental targets. The
existing methods for the assessment of environmental aspects are grouped into three categories: risk assessment-based (RA-based),
LCA-based and criterion-based methods. To combine the benefits of
these three categories of research, this study proposes an integrated framework, combining RA-, LCA- and criterion-based methods. The
integrated framework incorporates LCA techniques for the identification of the causal linkage for aspect, pathway, receptor and
impact, uses fuzzy logic to assess aspects, considers fuzzy conditions,
in likelihood assessment, and employs a new multi-criteria decision analysis method - multi-criteria and multi-connection comprehensive
assessment (MMCA) - to estimate significant aspects in EMS. The proposed model is verified, using a real case study and the results show
that this method successfully prioritizes the environmental aspects.
Abstract: Weather systems use enormously complex
combinations of numerical tools for study and forecasting.
Unfortunately, due to phenomena in the world climate, such
as the greenhouse effect, classical models may become
insufficient mostly because they lack adaptation. Therefore,
the weather forecast problem is matched for heuristic
approaches, such as Evolutionary Algorithms.
Experimentation with heuristic methods like Particle Swarm
Optimization (PSO) algorithm can lead to the development of
new insights or promising models that can be fine tuned with
more focused techniques. This paper describes a PSO
approach for analysis and prediction of data and provides
experimental results of the aforementioned method on realworld
meteorological time series.