Jadavpur University

About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Schiff base. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.

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

Abstract: 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).

Selection of industrial robots using compromise ranking and outranking methods

Abstract: Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is 'VIsekriterijumsko KOmpromisno Rangiranje' (VIKOR), a compromise ranking method and the other one is 'ELimination and Et Choice Translating REality' (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.