Electrical and Electronic Engineering - Research Publications
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ItemDetecting selective forwarding attacks in wireless sensor networks using support vector machines(Institute of Electrical and Electronic Engineers, 2007)Wireless Sensor Networks (WSNs) are a new technology foreseen to be used increasingly in the near future due to their data acquisition and data processing abilities. Security for WSNs is an area that needs to be considered in order to protect the functionality of these networks, the data they convey and the location of their members. The security models and protocols used in wired and other networks are not suited to WSNs because of their severe resource constraints, especially concerning energy . In this article, we propose a centralized intrusion detection scheme based on Support Vector Machines (SVMs) and sliding windows. We find that our system can detect black hole attacks and selective forwarding attacks with high accuracy without depleting the nodes of their energy.
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ItemIterative fuzzy support vector machine classification(IEEE, ELECTRON DEVICES SOC & RELIABILITY GROUP, 2007)
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ItemQuaternionic and complex-valued Support Vector Regression for Equalization and Function Approximation(IEEE - Institute of Electrical and Electronic Engineers, 2007)
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ItemProtein topology classification using two-stage support vector machines.(Universal Academy Press, 2006)The determination of the first 3-D model of a protein from its sequence alone is a non-trivial problem. The first 3-D model is the key to the molecular replacement method of solving phase problem in x-ray crystallography. If the sequence identity is more than 30%, homology modelling can be used to determine the correct topology (as defined by CATH) or fold (as defined by SCOP). If the sequence identity is less than 25%, however, the task is very challenging. In this paper we address the topology classification of proteins with sequence identity of less than 25%. The input information to the system is amino acid sequence, the predicted secondary structure and the predicted real value relative solvent accessibility. A two stage support vector machine (SVM) approach is proposed for classifying the sequences to three different structural classes (alpha, beta, alpha+beta) in the first stage and 39 topologies in the second stage. The method is evaluated using a newly curated dataset from CATH with maximum pairwise sequence identity less than 25%. An impressive overall accuracy of 87.44% and 83.15% is reported for class and topology prediction, respectively. In the class prediction stage, a sensitivity of 0.77 and a specificity of 0.91 is obtained. Data file, SVM implementation (SVMHEAVY) and result files can be downloaded from http://www.ee.unimelb.edu.au/ISSNIP/downloads/.
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ItemStability Analysis of the Decomposition Method for solving Support Vector Machines( 2005)In situations where processing memory is limited, the Support Vector Machine quadratic program can be decomposed into smaller sub-problems and solved sequentially. The convergence of this method has been proven previously through the use of a counting method. In this initial investigation, we approach the convergence analysis by treating the decomposed sub-problems as subsystems of a general system. The gradients of the subproblems and the inequality constraints are explicitly modelled as system variables. The change in these variables during optimization form a dynamic system modelled by vector differential equations. We show that the change in the objective function can be written as the energy in the system. This makes it a natural Lyapunov function which has an asymptotically stable point at the origin. The asymptotic stability of the whole system then follows under certain assumptions.
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ItemDisulphide Bridge Prediction using Fuzzy Support Vector Machines( 2005)One of the major contributors to the native form of protien is cystines forming covalent bonds in oxidized state. The Prediction of such bridges from the sequence is a very challenging task given that the number of bridges will rise exponentially as the number of cystines increases. We propose a novel technique for disulphide bridge prediction based on Fuzzy Support Vector Machines. We call the system DIzzy. In our investigation, we look at disulphide bond connectivity given two Cystines with and without a priori knowledge of the bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features such as the probability of occurrence of each amino acid in different secondary structure states along with psiblast profiles. The performance is compared with normal support vector machines. We evaluate our method and compare it with the existing method using SPX dataset.
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ItemDistributed data fusion using support vector machines( 2002)The basic quantity to be estimated in the Bayesian approach to data fusion is the conditional probability density function (CPDF). In recent times, computationally efficient particle filtering approaches are gaining growing importance in estimating these CPDF. In this approach, i.i.d samples are used to represent the conditional probability densities. However, their application in data fusion is severely limited due to the fact that the information is stored in the form of a large set of samples. In all practical data fusion systems that have limited communication bandwidth, broadcasting this probabilistic information, available as a set of samples, to the fusion center is impractical. Support vector machines, through statistical learning theory, provide a way of compressing information by generating optimal kernal based representations. In this paper we use SVM to compress the probabilistic information available in the form of i.i.d samples and apply it to solve the Bayesian data fusion problem. We demonstrate this technique on a multi-sensor tracking example.
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ItemMachine learning using support vector machines( 2000)Machine learning invokes the imagination of many scientific minds due to its potential to solve complex and difficult real world problems. This paper gives methods of constructing machine learning tools using Support Vector Machines (SVMs). We first give a simple example to illustrate the basic concept and then demonstrate further with a practical problem. The practical problem is concerned with electronic monitoring of fishways for automatic counting of different fish species for the purpose of environmental management in Australian rivers. The results illustrate the power of the SVM approaches on the sample problem and their computational attractiveness for practical implementations.
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ItemA convergence rate estimate for the SVM decomposition method( 2005)The training of Support Vector Machines using the decomposition method has one drawback; namely the selection of working sets such that convergence is as fast as possible. It has been shown by Lin that the rate is linear in the worse case under the assumption that all bounded Support Vectors have been determined. The analysis was done based on the change in the objective function and under a SVMlight selection rule. However, the rate estimate given is independent of time and hence gives little indication as to how the linear convergence speed varies during the iteration. In this initial analysis, we provide a treatment of the convergence from a gradient contraction perspective. We propose a necessary and sufficient condition which when satisfied provides strict linear convergence of the algorithm. The condition can also be interpreted as a basic requirement for a sequence of working sets in order to achieve such a convergence rate. Based on this condition, a time dependant rate estimate is then further derived. This estimate is shown to monotonically approach unity from below.