Sang Yong Han

Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis

This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.

Self organizing sensor networks using intelligent clustering

Minimization of the number of cluster heads in a wireless sensor network is a very important problem to reduce channel contention and to improve the efficiency of the algorithm when executed at the level of cluster-heads. This paper proposes a Self Organizing Sensor (SOS) network based on an intelligent clustering algorithm which does not require many user defined parameters and random selection to form clusters like in Algorithm for Cluster Establishment (ACE) [2]. The proposed SOS algorithm is compared with ACE and the empirical results clearly illustrate that the SOS algorithm can reduce the number of cluster heads.

The N/R one time password system

A new one time password system is described which is secure against eavesdropping and server database compromise at the same time. Traditionally, these properties have proven to be difficult to satisfy at the same time and only one previous scheme i.e. Lamport hashes also called S/KEY one time password system has claimed to achieve that. Lamport hashes however have a limitation that they are computationally intensive for the client and the number of times a client may login before the system should be re-initialized is small. We address these limitations to come up with a new scheme called the N/R one time password system. The basic idea is have the server aid the client computation by inserting breakpoints in the hash chains. Client computational requirements are dramatically reduced without any increase in the server computational requirements and the number of times a client may login before the system has to be reinitialized is also increased significantly. The system is particularly suited for mobile and constrained devices having limited computational power.

Stock Market Prediction Using Multi Expression Programming

The use of intelligent systems for stock market predictions has been widely established. In this paper, we introduce a genetic programming technique (called multi-expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno Neuro-Fuzzy model and difference boosting neural network. We considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index as test data

SCIDS: a soft computing intrusion detection system

An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This paper evaluates three fuzzy rule based classifiers for IDS and the performance is compared with decision trees, support vector machines and linear genetic programming. Further, Soft Computing (SC) based IDS (SCIDS) is modeled as an ensemble of different classifiers to build light weight and more accurate (heavy weight) IDS. Empirical results clearly show that SC approach could play a major role for intrusion detection.

Improving kNN text categorization by removing outliers from training set

We show that excluding outliers from the training data significantly improves kNN classifier, which in this case performs about 10% better than the best know method—Centroid-based classifier. Outliers are the elements whose similarity to the centroid of the corresponding category is below a threshold.

A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization

The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunovs stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy.

Chairs' welcome message

In this paper we present techniques for comparison between behavioral level and register transfer level (RTL) design descriptions by mapping the designs into virtual controllers and virtual datapaths. We also discuss about how the equivalence between behavioral level and RTL designs can be defined precisely using the proposed attribute statements in an interactive fashion. Implementation issues as well as considerations on real life industrial design examples are presented as well.