Kaushik Das Sharma

Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique

A PSO-adaptive KNN based gene selection method is proposed to select useful genes.A heuristic for selecting the optimal values of K efficiently is also proposed.The proposed technique is applied on SRBCT, ALL_AML and MLL microarray datasets.The usefulness of the identified genes is reconfirmed using SVM classifier.The method finds 6, 3 and 4 genes for SRBCT, ALL_AML, and MLL with high accuracy. These days, microarray gene expression data are playing an essential role in cancer classifications. However, due to the availability of small number of effective samples compared to the large number of genes in microarray data, many computational methods have failed to identify a small subset of important genes. Therefore, it is a challenging task to identify small number of disease-specific significant genes related for precise diagnosis of cancer sub classes. In this paper, particle swarm optimization (PSO) method along with adaptive K-nearest neighborhood (KNN) based gene selection technique are proposed to distinguish a small subset of useful genes that are sufficient for the desired classification purpose. A proper value of K would help to form the appropriate numbers of neighborhood to be explored and hence to classify the dataset accurately. Thus, a heuristic for selecting the optimal values of K efficiently, guided by the classification accuracy is also proposed. This proposed technique of finding minimum possible meaningful set of genes is applied on three benchmark microarray datasets, namely the small round blue cell tumor (SRBCT) data, the acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) data and the mixed-lineage leukemia (MLL) data. Results demonstrate the usefulness of the proposed method in terms of classification accuracy on blind test samples, number of informative genes and computing time. Further, the usefulness and universal characteristics of the identified genes are reconfirmed by using different classifiers, such as support vector machine (SVM).

A PSO–Lyapunov Hybrid Stable Adaptive Fuzzy Tracking Control Approach for Vision-Based Robot Navigation

This paper proposes a novel methodology for autonomous mobile robot navigation utilizing the concept of tracking control. Vision-based path planning and subsequent tracking are performed by utilizing proposed stable adaptive state feedback fuzzy tracking controllers designed using the Lyapunov theory and particle-swarm-optimization (PSO)-based hybrid approaches. The objective is to design two self-adaptive fuzzy controllers, for -direction and -direction movements, optimizing both its structures and free parameters, such that the designed controllers can guarantee desired stability and, simultaneously, can provide satisfactory tracking performance for the vision-based navigation of mobile robot. The design methodology for the controllers simultaneously utilizes the global search capability of PSO and Lyapunov-theory-based local search method, thus providing a high degree of automation. Two different variants of hybrid approaches have been employed in this work. The proposed schemes have been implemented in both simulation and experimentations with a real robot, and the results demonstrate the usefulness of the proposed concept.

A novel local extrema based gravitational search algorithm and its application in face recognition using one training image per class

In this present paper a new methodology has been presented involving a stochastic optimization based approach to solve the face recognition problem with only one training image per class. Singular value decomposition (SVD) is used to decompose the single training image into two component images in order to compute the within class scatter matrix. The stochastic optimization approach is implemented employing gravitational search algorithm (GSA) which searches for an optimal transform matrix instead of using the traditional solution of general eigenvalue problem as is carried out in Fisher linear discriminant analysis (FLDA). The present paper also proposes two novel variants of GSA, namely the 2-D version of GSA, in order to cater for the 2-D image data, and the other one is a 2-D randomized local extrema based GSA (RLEGSA), which employs a stochastic local neighborhood based search instead of global search, as in basic GSA. Finally, a novel concept of performing an automated selection of projection vectors is incorporated in the 2-D RLEGSA to propose an improved variant, called the Modified RLEGSA (MRLEGSA). Experimental results, based on benchmark Yale A and ORL databases, show that the proposed methods outperform several existing schemes.

Harmony search-based hybrid stable adaptive fuzzy tracking controllers for vision-based mobile robot navigation

In this paper the harmony search (HS) algorithm and Lyapunov theory are hybridized together to design a stable adaptive fuzzy tracking control strategy for vision-based navigation of autonomous mobile robots. The proposed variant of HS algorithm, with complete dynamic harmony memory (named here as DyHS algorithm), is utilized to design two self-adaptive fuzzy controllers, for

A simple visual secret sharing scheme employing particle swarm optimization

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Quantum signal processing-based visual cryptography with unexpanded shares

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Speed tracking scheme for FOC based induction motor by fuzzy controller

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