Abstract: When faced with stochastic networks with an uncertain
duration for their activities, the securing of network completion time
becomes problematical, not only because of the non-identical pdf of
duration for each node, but also because of the interdependence of
network paths. As evidenced by Adlakha & Kulkarni [1], many
methods and algorithms have been put forward in attempt to resolve
this issue, but most have encountered this same large-size network
problem. Therefore, in this research, we focus on network reduction
through a Series/Parallel combined mechanism. Our suggested
algorithm, named the Activity Network Reduction Algorithm
(ANRA), can efficiently transfer a large-size network into an S/P
Irreducible Network (SPIN). SPIN can enhance stochastic network
analysis, as well as serve as the judgment of symmetry for the Graph
Theory.
Abstract: Attack graph is an integral part of modeling the
overview of network security. System administrators use attack graphs to determine how vulnerable their systems are and to determine
what security measures to deploy to defend their systems. Previous methods on AGG(attack graphs generation) are aiming at
the whole network, which makes the process of AGG complex and
non-scalable. In this paper, we propose a new approach which is
simple and scalable to AGG by decomposing the whole network into atomic domains. Each atomic domain represents a host with a specific privilege. Then the process for AGG is achieved by communications
among all the atomic domains. Our approach simplifies the process
of design for the whole network, and can gives the attack graphs including each attack path for each host, and when the network changes we just carry on the operations of corresponding atomic
domains which makes the process of AGG scalable.
Abstract: Bioprocesses are appreciated as difficult to control because their dynamic behavior is highly nonlinear and time varying, in particular, when they are operating in fed batch mode. The research objective of this study was to develop an appropriate control method for a complex bioprocess and to implement it on a laboratory plant. Hence, an intelligent control structure has been designed in order to produce biomass and to maximize the specific growth rate.
Abstract: Automatic reusability appraisal is helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the
software from scratch. But the issue of how to identify reusable
components from existing systems has remained relatively
unexplored. In this research work, structural attributes of software
components are explored using software metrics and quality of the
software is inferred by different Neural Network based approaches,
taking the metric values as input. The calculated reusability value
enables to identify a good quality code automatically. It is found that
the reusability value determined is close to the manual analysis used
to be performed by the programmers or repository managers. So, the
developed system can be used to enhance the productivity and
quality of software development.
Abstract: In this paper a new Joint Adaptive Block Matching
Search (JABMS) algorithm is proposed to generate motion vector
and search a best match macro block by classifying the motion vector
movement based on prediction error. Diamond Search (DS)
algorithm generates high estimation accuracy when motion vector is
small and Adaptive Rood Pattern Search (ARPS) algorithm can
handle large motion vector but is not very accurate. The proposed
JABMS algorithm which is capable of considering both small and
large motions gives improved estimation accuracy and the
computational cost is reduced by 15.2 times compared with
Exhaustive Search (ES) algorithm and is 1.3 times less compared
with Diamond search algorithm.
Abstract: In this paper, we proposed the robust mobile object
detection method for light effect in the night street image block based
updating reference background model using block state analysis.
Experiment image is acquired sequence color video from steady
camera. When suddenly appeared artificial illumination, reference
background model update this information such as street light, sign
light. Generally natural illumination is change by temporal, but
artificial illumination is suddenly appearance. So in this paper for
exactly detect artificial illumination have 2 state process. First process
is compare difference between current image and reference
background by block based, it can know changed blocks. Second
process is difference between current image-s edge map and reference
background image-s edge map, it possible to estimate illumination at
any block. This information is possible to exactly detect object,
artificial illumination and it was generating reference background
more clearly. Block is classified by block-state analysis. Block-state
has a 4 state (i.e. transient, stationary, background, artificial
illumination). Fig. 1 is show characteristic of block-state respectively
[1]. Experimental results show that the presented approach works well
in the presence of illumination variance.
Abstract: The emergence of the Internet has brewed the
revolution of information storage and retrieval. As most of the
data in the web is unstructured, and contains a mix of text,
video, audio etc, there is a need to mine information to cater to
the specific needs of the users without loss of important
hidden information. Thus developing user friendly and
automated tools for providing relevant information quickly
becomes a major challenge in web mining research. Most of
the existing web mining algorithms have concentrated on
finding frequent patterns while neglecting the less frequent
ones that are likely to contain outlying data such as noise,
irrelevant and redundant data. This paper mainly focuses on
Signed approach and full word matching on the organized
domain dictionary for mining web content outliers. This
Signed approach gives the relevant web documents as well as
outlying web documents. As the dictionary is organized based
on the number of characters in a word, searching and retrieval
of documents takes less time and less space.
Abstract: This paper addresses the problem of source separation
in images. We propose a FastICA algorithm employing a modified
Gaussian contrast function for the Blind Source Separation.
Experimental result shows that the proposed Modified Gaussian
FastICA is effectively used for Blind Source Separation to obtain
better quality images. In this paper, a comparative study has been
made with other popular existing algorithms. The peak signal to
noise ratio (PSNR) and improved signal to noise ratio (ISNR) are
used as metrics for evaluating the quality of images. The ICA metric
Amari error is also used to measure the quality of separation.
Abstract: Representation and description of object shapes by the
slopes of their contours or borders are proposed. The idea is to capture
the essence of the features that make it easier for a shape to be
stored, transmitted, compared and recognized. These features must
be independent of translation, rotation and scaling of the shape. A
approach is proposed to obtain high performance, efficiency and to
merge the boundaries into sequence of straight line segments with
the fewest possible segments. Evaluation on the performance of the
proposed method is based on its comparison with established method
of object shape description.
Abstract: The physical methods for RNA secondary structure prediction are time consuming and expensive, thus methods for computational prediction will be a proper alternative. Various algorithms have been used for RNA structure prediction including dynamic programming and metaheuristic algorithms. Musician's behaviorinspired harmony search is a recently developed metaheuristic algorithm which has been successful in a wide variety of complex optimization problems. This paper proposes a harmony search algorithm (HSRNAFold) to find RNA secondary structure with minimum free energy and similar to the native structure. HSRNAFold is compared with dynamic programming benchmark mfold and metaheuristic algorithms (RnaPredict, SetPSO and HelixPSO). The results showed that HSRNAFold is comparable to mfold and better than metaheuristics in finding the minimum free energies and the number of correct base pairs.
Abstract: MANEMO is the integration of Network Mobility
(NEMO) and Mobile Ad Hoc Network (MANET). A MANEMO
node has an interface to both a MANET and NEMO network, and
therefore should choose the optimal interface for packet delivery,
however such a handover between interfaces will introduce packet
loss. We define the steps necessary for a MANEMO handover,
using Mobile IP and NEMO to signal the new binding to the
relevant Home Agent(s). The handover steps aim to minimize the
packet loss by avoiding waiting for Duplicate Address Detection
and Neighbour Unreachability Detection. We present expressions for
handover delay and packet loss, and then use numerical examples to
evaluate a MANEMO handover. The analysis shows how the packet
loss depends on level of nesting within NEMO, the delay between
Home Agents and the load on the MANET, and hence can be used
to developing optimal MANEMO handover algorithms.
Abstract: The vast amount of information on the World Wide
Web is created and published by many different types of providers.
Unlike books and journals, most of this information is not subject to
editing or peer review by experts. This lack of quality control and the
explosion of web sites make the task of finding quality information
on the web especially critical. Meanwhile new facilities for
producing web pages such as Blogs make this issue more significant
because Blogs have simple content management tools enabling nonexperts
to build easily updatable web diaries or online journals. On
the other hand despite a decade of active research in information
quality (IQ) there is no framework for measuring information quality
on the Blogs yet. This paper presents a novel experimental
framework for ranking quality of information on the Weblog. The
results of data analysis revealed seven IQ dimensions for the Weblog.
For each dimension, variables and related coefficients were
calculated so that presented framework is able to assess IQ of
Weblogs automatically.
Abstract: The lack of any centralized infrastructure in mobile ad
hoc networks (MANET) is one of the greatest security concerns in
the deployment of wireless networks. Thus communication in
MANET functions properly only if the participating nodes cooperate
in routing without any malicious intention. However, some of the
nodes may be malicious in their behavior, by indulging in flooding
attacks on their neighbors. Some others may act malicious by
launching active security attacks like denial of service. This paper
addresses few related works done on trust evaluation and
establishment in ad hoc networks. Related works on flooding attack
prevention are reviewed. A new trust approach based on the extent of
friendship between the nodes is proposed which makes the nodes to
co-operate and prevent flooding attacks in an ad hoc environment.
The performance of the trust algorithm is tested in an ad hoc network
implementing the Ad hoc On-demand Distance Vector (AODV)
protocol.
Abstract: Segmenting the lungs in medical images is a
challenging and important task for many applications. In particular,
automatic segmentation of lung cavities from multiple magnetic
resonance (MR) images is very useful for oncological applications
such as radiotherapy treatment planning. However, distinguishing of
the lung areas is not trivial due to largely changing lung shapes, low
contrast and poorly defined boundaries. In this paper, we address
lung segmentation problem from pulmonary magnetic resonance
images and propose an automated method based on a robust regionaided
geometric snake with a modified diffused region force into the
standard geometric model definition. The extra region force gives the
snake a global complementary view of the lung boundary
information within the image which along with the local gradient
flow, helps detect fuzzy boundaries. The proposed method has been
successful in segmenting the lungs in every slice of 30 magnetic
resonance images with 80 consecutive slices in each image. We
present results by comparing our automatic method to manually
segmented lung cavities provided by an expert radiologist and with
those of previous works, showing encouraging results and high
robustness of our approach.
Abstract: In this study, a high accuracy protein-protein interaction
prediction method is developed. The importance of the proposed
method is that it only uses sequence information of proteins while
predicting interaction. The method extracts phylogenetic profiles of
proteins by using their sequence information. Combining the phylogenetic
profiles of two proteins by checking existence of homologs
in different species and fitting this combined profile into a statistical
model, it is possible to make predictions about the interaction status
of two proteins.
For this purpose, we apply a collection of pattern recognition
techniques on the dataset of combined phylogenetic profiles of protein
pairs. Support Vector Machines, Feature Extraction using ReliefF,
Naive Bayes Classification, K-Nearest Neighborhood Classification,
Decision Trees, and Random Forest Classification are the methods
we applied for finding the classification method that best predicts
the interaction status of protein pairs. Random Forest Classification
outperformed all other methods with a prediction accuracy of 76.93%
Abstract: Simultaneous determination of multicomponents of phenol, resorcinol and catechol with a chemometric technique a PCranking artificial neural network (PCranking-ANN) algorithm is reported in this study. Based on the data correlation coefficient method, 3 representative PCs are selected from the scores of original UV spectral data (35 PCs) as the original input patterns for ANN to build a neural network model. The results obtained by iterating 8000 .The RMSEP for phenol, resorcinol and catechol with PCranking- ANN were 0.6680, 0.0766 and 0.1033, respectively. Calibration matrices were 0.50-21.0, 0.50-15.1 and 0.50-20.0 μg ml-1 for phenol, resorcinol and catechol, respectively. The proposed method was successfully applied for the determination of phenol, resorcinol and catechol in synthetic and water samples.
Abstract: A prototype of an anomaly detection system was
developed to automate process of recognizing an anomaly of
roentgen image by utilizing fuzzy histogram hyperbolization image
enhancement and back propagation artificial neural network.
The system consists of image acquisition, pre-processor, feature
extractor, response selector and output. Fuzzy Histogram
Hyperbolization is chosen to improve the quality of the roentgen
image. The fuzzy histogram hyperbolization steps consist of
fuzzyfication, modification of values of membership functions and
defuzzyfication. Image features are extracted after the the quality of
the image is improved. The extracted image features are input to the
artificial neural network for detecting anomaly. The number of nodes
in the proposed ANN layers was made small.
Experimental results indicate that the fuzzy histogram
hyperbolization method can be used to improve the quality of the
image. The system is capable to detect the anomaly in the roentgen
image.
Abstract: In this paper, we propose a selective mutation method
for improving the performances of genetic algorithms. In selective
mutation, individuals are first ranked and then additionally mutated
one bit in a part of their strings which is selected corresponding to
their ranks. This selective mutation helps genetic algorithms to fast
approach the global optimum and to quickly escape local optima.
This results in increasing the performances of genetic algorithms.
We measured the effects of selective mutation with four function
optimization problems. It was found from extensive experiments that
the selective mutation can significantly enhance the performances of
genetic algorithms.
Abstract: In the artificial intelligence field, knowledge
representation and reasoning are important areas for intelligent
systems, especially knowledge base systems and expert systems.
Knowledge representation Methods has an important role in
designing the systems. There have been many models for knowledge
such as semantic networks, conceptual graphs, and neural networks.
These models are useful tools to design intelligent systems. However,
they are not suitable to represent knowledge in the domains of reality
applications. In this paper, new models for knowledge representation
called computational networks will be presented. They have been
used in designing some knowledge base systems in education for
solving problems such as the system that supports studying
knowledge and solving analytic geometry problems, the program for
studying and solving problems in Plane Geometry, the program for
solving problems about alternating current in physics.
Abstract: In this paper, we propose the robust water level detection method based on the accumulated histogram under small changed image which is acquired from water level surveillance camera. In general surveillance system, this is detecting and recognizing invasion from searching area which is in big change on the sequential images. However, in case of a water level detection system, these general surveillance techniques are not suitable due to small change on the water surface. Therefore the algorithm introduces the accumulated histogram which is emphasizing change of water surface in sequential images. Accumulated histogram is based on the current image frame. The histogram is cumulating differences between previous images and current image. But, these differences are also appeared in the land region. The band pass filter is able to remove noises in the accumulated histogram Finally, this algorithm clearly separates water and land regions. After these works, the algorithm converts from the water level value on the image space to the real water level on the real space using calibration table. The detected water level is sent to the host computer with current image. To evaluate the proposed algorithm, we use test images from various situations.