Abstract: This paper presents the development of a Bayesian
belief network classifier for prediction of graft status and survival
period in renal transplantation using the patient profile information
prior to the transplantation. The objective was to explore feasibility
of developing a decision making tool for identifying the most suitable
recipient among the candidate pool members. The dataset was
compiled from the University of Toledo Medical Center Hospital
patients as reported to the United Network Organ Sharing, and had
1228 patient records for the period covering 1987 through 2009. The
Bayes net classifiers were developed using the Weka machine
learning software workbench. Two separate classifiers were induced
from the data set, one to predict the status of the graft as either failed
or living, and a second classifier to predict the graft survival period.
The classifier for graft status prediction performed very well with a
prediction accuracy of 97.8% and true positive values of 0.967 and
0.988 for the living and failed classes, respectively. The second
classifier to predict the graft survival period yielded a prediction
accuracy of 68.2% and a true positive rate of 0.85 for the class
representing those instances with kidneys failing during the first year
following transplantation. Simulation results indicated that it is
feasible to develop a successful Bayesian belief network classifier for
prediction of graft status, but not the graft survival period, using the
information in UNOS database.
Abstract: Tracing and locating the geographical location of users (Geolocation) is used extensively in todays Internet. Whenever we, e.g., request a page from google we are - unless there was a specific configuration made - automatically forwarded to the page with the relevant language and amongst others, dependent on our location identified, specific commercials are presented. Especially within the area of Network Security, Geolocation has a significant impact. Because of the way the Internet works, attacks can be executed from almost everywhere. Therefore, for an attribution, knowledge of the origination of an attack - and thus Geolocation - is mandatory in order to be able to trace back an attacker. In addition, Geolocation can also be used very successfully to increase the security of a network during operation (i.e. before an intrusion actually has taken place). Similar to greylisting in emails, Geolocation allows to (i) correlate attacks detected with new connections and (ii) as a consequence to classify traffic a priori as more suspicious (thus particularly allowing to inspect this traffic in more detail). Although numerous techniques for Geolocation are existing, each strategy is subject to certain restrictions. Following the ideas of Endo et al., this publication tries to overcome these shortcomings with a combined solution of different methods to allow improved and optimized Geolocation. Thus, we present our architecture for improved Geolocation, by designing a new algorithm, which combines several Geolocation techniques to increase the accuracy.
Abstract: The necessity of ever-increasing use of distributed
data in computer networks is obvious for all. One technique that is
performed on the distributed data for increasing of efficiency and
reliablity is data rplication. In this paper, after introducing this
technique and its advantages, we will examine some dynamic data
replication. We will examine their characteristies for some overus
scenario and the we will propose some suggestion for their
improvement.
Abstract: Automatic currency note recognition invariably
depends on the currency note characteristics of a particular country
and the extraction of features directly affects the recognition ability.
Sri Lanka has not been involved in any kind of research or
implementation of this kind. The proposed system “SLCRec" comes
up with a solution focusing on minimizing false rejection of notes.
Sri Lankan currency notes undergo severe changes in image quality
in usage. Hence a special linear transformation function is adapted to
wipe out noise patterns from backgrounds without affecting the
notes- characteristic images and re-appear images of interest. The
transformation maps the original gray scale range into a smaller
range of 0 to 125. Applying Edge detection after the transformation
provided better robustness for noise and fair representation of edges
for new and old damaged notes. A three layer back propagation
neural network is presented with the number of edges detected in row
order of the notes and classification is accepted in four classes of
interest which are 100, 500, 1000 and 2000 rupee notes. The
experiments showed good classification results and proved that the
proposed methodology has the capability of separating classes
properly in varying image conditions.
Abstract: This paper presents an approach for early breast
cancer diagnostic by employing combination of artificial neural
networks (ANN) and multiwaveletpacket based subband image
decomposition. The microcalcifications correspond to high-frequency
components of the image spectrum, detection of microcalcifications
is achieved by decomposing the mammograms into different
frequency subbands,, reconstructing the mammograms from the
subbands containing only high frequencies. For this approach we
employed different types of multiwaveletpacket. We used the result
as an input of neural network for classification. The proposed
methodology is tested using the Nijmegen and the Mammographic
Image Analysis Society (MIAS) mammographic databases and
images collected from local hospitals. Results are presented as the
receiver operating characteristic (ROC) performance and are
quantified by the area under the ROC curve.
Abstract: Reliable secure multicast communication in mobile
adhoc networks is challenging due to its inherent characteristics of
infrastructure-less architecture with lack of central authority, high
packet loss rates and limited resources such as bandwidth, time and
power. Many emerging commercial and military applications require
secure multicast communication in adhoc environments. Hence key
management is the fundamental challenge in achieving reliable
secure communication using multicast key distribution for mobile
adhoc networks. Thus in designing a reliable multicast key
distribution scheme, reliability and congestion control over
throughput are essential components. This paper proposes and
evaluates the performance of an enhanced optimized multicast cluster
tree algorithm with destination sequenced distance vector routing
protocol to provide reliable multicast key distribution. Simulation
results in NS2 accurately predict the performance of proposed
scheme in terms of key delivery ratio and packet loss rate under
varying network conditions. This proposed scheme achieves
reliability, while exhibiting low packet loss rate with high key
delivery ratio compared with the existing scheme.
Abstract: Design for cost (DFC) is a method that reduces life
cycle cost (LCC) from the angle of designers. Multiple domain
features mapping (MDFM) methodology was given in DFC. Using
MDFM, we can use design features to estimate the LCC. From the
angle of DFC, the design features of family cars were obtained, such
as all dimensions, engine power and emission volume. At the
conceptual design stage, cars- LCC were estimated using back
propagation (BP) artificial neural networks (ANN) method and
case-based reasoning (CBR). Hamming space was used to measure the
similarity among cases in CBR method. Levenberg-Marquardt (LM)
algorithm and genetic algorithm (GA) were used in ANN. The
differences of LCC estimation model between CBR and artificial
neural networks (ANN) were provided. ANN and CBR separately
each method has its shortcomings. By combining ANN and CBR
improved results accuracy was obtained. Firstly, using ANN selected
some design features that affect LCC. Then using LCC estimation
results of ANN could raise the accuracy of LCC estimation in CBR
method. Thirdly, using ANN estimate LCC errors and correct errors in
CBR-s estimation results if the accuracy is not enough accurate.
Finally, economically family cars and sport utility vehicle (SUV) was
given as LCC estimation cases using this hybrid approach combining
ANN and CBR.
Abstract: In this paper, a Bayesian Network (BN) based system
is presented for providing clinical decision support to healthcare
practitioners in rural or remote areas of India for young infants or
children up to the age of 5 years. The government is unable to
appoint child specialists in rural areas because of inadequate number
of available pediatricians. It leads to a high Infant Mortality Rate
(IMR). In such a scenario, Intelligent Pediatric System provides a
realistic solution. The prototype of an intelligent system has been
developed that involves a knowledge component called an Intelligent
Pediatric Assistant (IPA); and User Agents (UA) along with their
Graphical User Interfaces (GUI). The GUI of UA provides the
interface to the healthcare practitioner for submitting sign-symptoms
and displaying the expert opinion as suggested by IPA. Depending
upon the observations, the IPA decides the diagnosis and the
treatment plan. The UA and IPA form client-server architecture for
knowledge sharing.
Abstract: Variable channel conditions in underwater networks,
and variable distances between sensors due to water current, leads to
variable bit error rate (BER). This variability in BER has great
effects on energy efficiency of error correction techniques used. In
this paper an efficient energy adaptive hybrid error correction
technique (AHECT) is proposed. AHECT adaptively changes error
technique from pure retransmission (ARQ) in a low BER case to a
hybrid technique with variable encoding rates (ARQ & FEC) in a
high BER cases. An adaptation algorithm depends on a precalculated
packet acceptance rate (PAR) look-up table, current BER,
packet size and error correction technique used is proposed. Based
on this adaptation algorithm a periodically 3-bit feedback is added to
the acknowledgment packet to state which error correction technique
is suitable for the current channel conditions and distance.
Comparative studies were done between this technique and other
techniques, and the results show that AHECT is more energy
efficient and has high probability of success than all those
techniques.
Abstract: Opportunistic Data Forwarding (ODF) has drawn much attention in mobile adhoc networking research in recent years. The effectiveness of ODF in MANET depends on a suitable routing protocol which provides a powerful source routing services. PLSR is featured by source routing, loop free and small routing overhead. The update messages in PLSR are integrated into a tree structure and no need to time stamp routing updates which reduces the routing overhead.
Abstract: Data security in u-Health system can be an important
issue because wireless network is vulnerable to hacking. However, it is
not easy to implement a proper security algorithm in an embedded
u-health monitoring because of hardware constraints such as low
performance, power consumption and limited memory size and etc. To
secure data that contain personal and biosignal information, we
implemented several security algorithms such as Blowfish, data
encryption standard (DES), advanced encryption standard (AES) and
Rivest Cipher 4 (RC4) for our u-Health monitoring system and the
results were successful. Under the same experimental conditions, we
compared these algorithms. RC4 had the fastest execution time.
Memory usage was the most efficient for DES. However, considering
performance and safety capability, however, we concluded that AES
was the most appropriate algorithm for a personal u-Health monitoring
system.
Abstract: This paper introduces two decoders for binary linear
codes based on Metaheuristics. The first one uses a genetic algorithm
and the second is based on a combination genetic algorithm with
a feed forward neural network. The decoder based on the genetic
algorithms (DAG) applied to BCH and convolutional codes give good
performances compared to Chase-2 and Viterbi algorithm respectively
and reach the performances of the OSD-3 for some Residue
Quadratic (RQ) codes. This algorithm is less complex for linear
block codes of large block length; furthermore their performances
can be improved by tuning the decoder-s parameters, in particular the
number of individuals by population and the number of generations.
In the second algorithm, the search space, in contrast to DAG which
was limited to the code word space, now covers the whole binary
vector space. It tries to elude a great number of coding operations
by using a neural network. This reduces greatly the complexity of
the decoder while maintaining comparable performances.
Abstract: Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.
Abstract: Wireless sensor networks are consisted of hundreds or
thousands of small sensors that have limited resources.
Energy-efficient techniques are the main issue of wireless sensor
networks. This paper proposes an energy efficient agent-based
framework in wireless sensor networks. We adopt biologically
inspired approaches for wireless sensor networks. Agent operates
automatically with their behavior policies as a gene. Agent aggregates
other agents to reduce communication and gives high priority to nodes
that have enough energy to communicate. Agent behavior policies are
optimized by genetic operation at the base station. Simulation results
show that our proposed framework increases the lifetime of each node.
Each agent selects a next-hop node with neighbor information and
behavior policies. Our proposed framework provides self-healing,
self-configuration, self-optimization properties to sensor nodes.
Abstract: This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.
Abstract: Wireless sensor networks have been used in wide
areas of application and become an attractive area for researchers in
recent years. Because of the limited energy storage capability of
sensor nodes, Energy consumption is one of the most challenging
aspects of these networks and different strategies and protocols deals
with this area. This paper presents general methods for designing low
power wireless sensor network. Different sources of energy
consumptions in these networks are discussed here and techniques for
alleviating the consumption of energy are presented.
Abstract: In the age of global communications, heterogeneous
networks are seen to be the best choice of strategy to ensure continuous and uninterruptible services. This will allow mobile
terminal to stay in connection even they are migrating into different segment coverage through the handoff process. With the increase of
teletraffic demands in mobile cellular system, hierarchical cellular systems have been adopted extensively for more efficient channel
utilization and better QoS (Quality of Service). This paper presents a
bidirectional call overflow scheme between two layers of microcells and macrocells, where handoffs are decided by the velocity of mobile
making the call. To ensure that handoff calls are given higher priorities, it is assumed that guard channels are assigned in both
macrocells and microcells. A hysteresis value introduced in mobile velocity is used to allow mobile roam in the same cell if its velocity
changes back within the set threshold values. By doing this the number of handoffs is reduced thereby reducing the processing overhead and enhancing the quality of service to the end user.
Abstract: Network security attacks are the violation of
information security policy that received much attention to the
computational intelligence society in the last decades. Data mining
has become a very useful technique for detecting network intrusions
by extracting useful knowledge from large number of network data
or logs. Naïve Bayesian classifier is one of the most popular data
mining algorithm for classification, which provides an optimal way
to predict the class of an unknown example. It has been tested that
one set of probability derived from data is not good enough to have
good classification rate. In this paper, we proposed a new learning
algorithm for mining network logs to detect network intrusions
through naïve Bayesian classifier, which first clusters the network
logs into several groups based on similarity of logs, and then
calculates the prior and conditional probabilities for each group of
logs. For classifying a new log, the algorithm checks in which cluster
the log belongs and then use that cluster-s probability set to classify
the new log. We tested the performance of our proposed algorithm by
employing KDD99 benchmark network intrusion detection dataset,
and the experimental results proved that it improves detection rates
as well as reduces false positives for different types of network
intrusions.
Abstract: Modeling and simulation of biochemical reactions is of great interest in the context of system biology. The central dogma of this re-emerging area states that it is system dynamics and organizing principles of complex biological phenomena that give rise to functioning and function of cells. Cell functions, such as growth, division, differentiation and apoptosis are temporal processes, that can be understood if they are treated as dynamic systems. System biology focuses on an understanding of functional activity from a system-wide perspective and, consequently, it is defined by two hey questions: (i) how do the components within a cell interact, so as to bring about its structure and functioning? (ii) How do cells interact, so as to develop and maintain higher levels of organization and functions? In recent years, wet-lab biologists embraced mathematical modeling and simulation as two essential means toward answering the above questions. The credo of dynamics system theory is that the behavior of a biological system is given by the temporal evolution of its state. Our understanding of the time behavior of a biological system can be measured by the extent to which a simulation mimics the real behavior of that system. Deviations of a simulation indicate either limitations or errors in our knowledge. The aim of this paper is to summarize and review the main conceptual frameworks in which models of biochemical networks can be developed. In particular, we review the stochastic molecular modelling approaches, by reporting the principal conceptualizations suggested by A. A. Markov, P. Langevin, A. Fokker, M. Planck, D. T. Gillespie, N. G. van Kampfen, and recently by D. Wilkinson, O. Wolkenhauer, P. S. Jöberg and by the author.
Abstract: This paper describes about the process of recognition and classification of brain images such as normal and abnormal based on PSO-SVM. Image Classification is becoming more important for medical diagnosis process. In medical area especially for diagnosis the abnormality of the patient is classified, which plays a great role for the doctors to diagnosis the patient according to the severeness of the diseases. In case of DICOM images it is very tough for optimal recognition and early detection of diseases. Our work focuses on recognition and classification of DICOM image based on collective approach of digital image processing. For optimal recognition and classification Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Support Vector Machine (SVM) are used. The collective approach by using PSO-SVM gives high approximation capability and much faster convergence.