Abstract: The security of computer networks plays a strategic
role in modern computer systems. Intrusion Detection Systems (IDS)
act as the 'second line of defense' placed inside a protected
network, looking for known or potential threats in network traffic
and/or audit data recorded by hosts. We developed an Intrusion
Detection System using LAMSTAR neural network to learn patterns
of normal and intrusive activities, to classify observed system
activities and compared the performance of LAMSTAR IDS with
other classification techniques using 5 classes of KDDCup99 data.
LAMSAR IDS gives better performance at the cost of high
Computational complexity, Training time and Testing time, when
compared to other classification techniques (Binary Tree classifier,
RBF classifier, Gaussian Mixture classifier). we further reduced the
Computational Complexity of LAMSTAR IDS by reducing the
dimension of the data using principal component analysis which in
turn reduces the training and testing time with almost the same
performance.
Abstract: This paper presents a recognition system for isolated
words like robot commands. It’s carried out by Time Delay Neural
Networks; TDNN. To teleoperate a robot for specific tasks as turn,
close, etc… In industrial environment and taking into account the
noise coming from the machine. The choice of TDNN is based on its
generalization in terms of accuracy, in more it acts as a filter that
allows the passage of certain desirable frequency characteristics of
speech; the goal is to determine the parameters of this filter for
making an adaptable system to the variability of speech signal and to
noise especially, for this the back propagation technique was used in
learning phase. The approach was applied on commands pronounced
in two languages separately: The French and Arabic. The results for
two test bases of 300 spoken words for each one are 87%, 97.6% in
neutral environment and 77.67%, 92.67% when the white Gaussian
noisy was added with a SNR of 35 dB.
Abstract: The aim of this paper is to identify the most suitable
model for churn prediction based on three different techniques. The
paper identifies the variables that affect churn in reverence of
customer complaints data and provides a comparative analysis of
neural networks, regression trees and regression in their capabilities
of predicting customer churn.
Abstract: This paper discusses the effectiveness of the EEG signal
for human identification using four or less of channels of two different
types of EEG recordings. Studies have shown that the EEG signal
has biometric potential because signal varies from person to person
and impossible to replicate and steal. Data were collected from 10
male subjects while resting with eyes open and eyes closed in 5
separate sessions conducted over a course of two weeks. Features
were extracted using the wavelet packet decomposition and analyzed
to obtain the feature vectors. Subsequently, the neural networks
algorithm was used to classify the feature vectors. Results show that,
whether or not the subjects- eyes were open are insignificant for a 4–
channel biometrics system with a classification rate of 81%. However,
for a 2–channel system, the P4 channel should not be included if data
is acquired with the subjects- eyes open. It was observed that for 2–
channel system using only the C3 and C4 channels, a classification
rate of 71% was achieved.
Abstract: The main idea behind in network aggregation is that,
rather than sending individual data items from sensors to sinks,
multiple data items are aggregated as they are forwarded by the
sensor network. Existing sensor network data aggregation techniques
assume that the nodes are preprogrammed and send data to a central
sink for offline querying and analysis. This approach faces two major
drawbacks. First, the system behavior is preprogrammed and cannot
be modified on the fly. Second, the increased energy wastage due to
the communication overhead will result in decreasing the overall
system lifetime. Thus, energy conservation is of prime consideration
in sensor network protocols in order to maximize the network-s
operational lifetime. In this paper, we give an energy efficient
approach to query processing by implementing new optimization
techniques applied to in-network aggregation. We first discuss earlier
approaches in sensors data management and highlight their
disadvantages. We then present our approach “Energy Efficient
Indexed Aggregation" (EEIA) and evaluate it through several
simulations to prove its efficiency, competence and effectiveness.
Abstract: Information of nodes’ locations is an important
criterion for lots of applications in Wireless Sensor Networks. In the
hop-based range-free localization methods, anchors transmit the
localization messages counting a hop count value to the whole
network. Each node receives this message and calculates its own
distance with anchor in hops and then approximates its own position.
However the estimative distances can provoke large error, and affect
the localization precision. To solve the problem, this paper proposes
an algorithm, which makes the unknown nodes fix the nearest anchor
as a reference and select two other anchors which are the most
accurate to achieve the estimated location. Compared to the DV-Hop
algorithm, experiment results illustrate that proposed algorithm has
less average localization error and is more effective.
Abstract: This paper is concerned with the existence and unique¬ness of pseudo-almost periodic solutions to the chaotic delayed neural networks (t)= —Dx(t) ± A f (x (t)) B f (x (t — r)) C f (x(p))dp J (t) . t-o Under some suitable assumptions on A, B, C, D, J and f, the existence and uniqueness of a pseudo-almost periodic solution to equation above is obtained. The results of this paper are new and they complement previously known results.
Abstract: This paper discusses the Urdu script characteristics,
Urdu Nastaleeq and a simple but a novel and robust technique to
recognize the printed Urdu script without a lexicon. Urdu being a
family of Arabic script is cursive and complex script in its nature, the
main complexity of Urdu compound/connected text is not its
connections but the forms/shapes the characters change when it is
placed at initial, middle or at the end of a word. The characters
recognition technique presented here is using the inherited
complexity of Urdu script to solve the problem. A word is scanned
and analyzed for the level of its complexity, the point where the level
of complexity changes is marked for a character, segmented and
feeded to Neural Networks. A prototype of the system has been
tested on Urdu text and currently achieves 93.4% accuracy on the
average.
Abstract: The importance of our country-s communication
system is noticeable when a disaster occurs. The communication
system in our country includes wired and wireless telephone
networks, radio, satellite system and more increasingly internet. Even
though our communication system is most extensive and dependable,
extreme conditions can put a strain on them. Interoperability between
heterogeneous wireless networks can be used to provide efficient
communication for emergency first response. IEEE 802.21 specifies
Media Independent Handover (MIH) services to enhance the mobile
user experience by optimizing handovers between heterogeneous
access networks. This paper presents an algorithm to improve
congestion control in MIH framework. It is analytically shown that
by including time factor in network selection we can optimize
congestion in the network.
Abstract: Artificial Neural Network (ANN)s can be modeled for
High Energy Particle analysis with special emphasis on shower core
location. The work describes the use of an ANN based system which
has been configured to predict locations of cores of showers in the
range 1010.5 to 1020.5 eV. The system receives density values as
inputs and generates coordinates of shower events recorded for values
captured by 20 core positions and 80 detectors in an area of 100
meters. Twenty ANNs are trained for the purpose and the positions
of shower events optimized by using cooperative ANN learning. The
results derived with variations of input upto 50% show success rates
in the range of 90s.
Abstract: Distributed Power generation has gained a lot of
attention in recent times due to constraints associated with
conventional power generation and new advancements in DG
technologies .The need to operate the power system economically
and with optimum levels of reliability has further led to an increase
in interest in Distributed Generation. However it is important to place
Distributed Generator on an optimum location so that the purpose of
loss minimization and voltage regulation is dully served on the
feeder. This paper investigates the impact of DG units installation on
electric losses, reliability and voltage profile of distribution networks.
In this paper, our aim would be to find optimal distributed
generation allocation for loss reduction subjected to constraint of
voltage regulation in distribution network. The system is further
analyzed for increased levels of Reliability. Distributed Generator
offers the additional advantage of increase in reliability levels as
suggested by the improvements in various reliability indices such as
SAIDI, CAIDI and AENS. Comparative studies are performed and
related results are addressed. An analytical technique is used in order
to find the optimal location of Distributed Generator. The suggested
technique is programmed under MATLAB software. The results
clearly indicate that DG can reduce the electrical line loss while
simultaneously improving the reliability of the system.
Abstract: A power cable is widely used for power supply in
power distributing networks and power transmission lines. Due to
limitations in the production, delivery and setting up power cables,
they are produced and delivered in several separate lengths. Cable
itself, consists of two cable terminations and arbitrary number of
cable joints, depending on the cable route length. Electrical stress
control is needed to prevent a dielectric breakdown at the end of the
insulation shield in both the air and cable insulation. Reliability of
cable joint depends on its materials, design, installation and operating
environment. The paper describes design and performance results for
new modeled cable joints. Design concepts, based on numerical
calculations, must be correct. An Equivalent Electrodes
Method/Boundary Elements Method-hybrid approach that allows
electromagnetic field calculations in multilayer dielectric media,
including inhomogeneous regions, is presented.
Abstract: This paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial
Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water
flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by
sensors to construct an empirical model for time series prediction and
classification of events. These two components have been installed,
tested and verified in an experimental site in a UK water distribution
system. Verification of the system has been achieved from a series of
simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network
management.
Abstract: Streaming Applications usually run in parallel or in
series that incrementally transform a stream of input data. It poses a
design challenge to break such an application into distinguishable
blocks and then to map them into independent hardware processing
elements. For this, there is required a generic controller that
automatically maps such a stream of data into independent processing
elements without any dependencies and manual considerations. In
this paper, Kahn Process Networks (KPN) for such streaming
applications is designed and developed that will be mapped on
MPSoC. This is designed in such a way that there is a generic Cbased
compiler that will take the mapping specifications as an input
from the user and then it will automate these design constraints and
automatically generate the synthesized RTL optimized code for
specified application.
Abstract: In this paper, genetic algorithm (GA) is proposed for
the design of an optimization algorithm to achieve the bandwidth
allocation of ATM network. In Broadband ISDN, the ATM is a highbandwidth;
fast packet switching and multiplexing technique. Using
ATM it can be flexibly reconfigure the network and reassign the
bandwidth to meet the requirements of all types of services. By
dynamically routing the traffic and adjusting the bandwidth
assignment, the average packet delay of the whole network can be
reduced to a minimum. M/M/1 model can be used to analyze the
performance.
Abstract: An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.
Abstract: Purpose of this work is to develop an automatic classification system that could be useful for radiologists in the breast cancer investigation. The software has been designed in the framework of the MAGIC-5 collaboration. In an automatic classification system the suspicious regions with high probability to include a lesion are extracted from the image as regions of interest (ROIs). Each ROI is characterized by some features based generally on morphological lesion differences. A study in the space features representation is made and some classifiers are tested to distinguish the pathological regions from the healthy ones. The results provided in terms of sensitivity and specificity will be presented through the ROC (Receiver Operating Characteristic) curves. In particular the best performances are obtained with the Neural Networks in comparison with the K-Nearest Neighbours and the Support Vector Machine: The Radial Basis Function supply the best results with 0.89 ± 0.01 of area under ROC curve but similar results are obtained with the Probabilistic Neural Network and a Multi Layer Perceptron.
Abstract: This paper describes a UDP over IP based, server-oriented redundant host configuration protocol (RHCP) that can be used by collaborating embedded systems in an ad-hoc network to acquire a dynamic IP address. The service is provided by a single network device at a time and will be dynamically reassigned to one of the other network clients if the primary provider fails. The protocol also allows all participating clients to monitor the dynamic makeup of the network over time. So far the algorithm has been implemented and tested on an 8-bit embedded system architecture with a 10Mbit Ethernet interface.
Abstract: Mobile Ad hoc Networks is an autonomous system of
mobile nodes connected by multi-hop wireless links without
centralized infrastructure support. As mobile communication gains
popularity, the need for suitable ad hoc routing protocols will
continue to grow. Efficient dynamic routing is an important research
challenge in such a network. Bandwidth constrained mobile devices
use on-demand approach in their routing protocols because of its
effectiveness and efficiency. Many researchers have conducted
numerous simulations for comparing the performance of these
protocols under varying conditions and constraints. Most of them are
not aware of MAC Protocols, which will impact the relative
performance of routing protocols considered in different network
scenarios. In this paper we investigate the choice of MAC protocols
affects the relative performance of ad hoc routing protocols under
different scenarios. We have evaluated the performance of these
protocols using NS2 simulations. Our results show that the
performance of routing protocols of ad hoc networks will suffer when
run over different MAC Layer protocols.
Abstract: Video sensor networks operate on stringent requirements
of latency. Packets have a deadline within which they have
to be delivered. Violation of the deadline causes a packet to be
treated as lost and the loss of packets ultimately affects the quality
of the application. Network latency is typically a function of many
interacting components. In this paper, we propose ways of reducing
the forwarding latency of a packet at intermediate nodes. The
forwarding latency is caused by a combination of processing delay
and queueing delay. The former is incurred in order to determine the
next hop in dynamic routing. We show that unless link failures in a
very specific and unlikely pattern, a vast majority of these lookups
are redundant. To counter this we propose source routing as the
routing strategy. However, source routing suffers from issues related
to scalability and being impervious to network dynamics. We propose
solutions to counter these and show that source routing is definitely
a viable option in practical sized video networks. We also propose a
fast and fair packet scheduling algorithm that reduces queueing delay
at the nodes. We support our claims through extensive simulation on
realistic topologies with practical traffic loads and failure patterns.