Ujjwal Maulik - Academia.edu (original) (raw)

Papers by Ujjwal Maulik

In this paper, a new type of point symmetry based distance is proposed. Thereafter a genetic algo... more In this paper, a new type of point symmetry based distance is proposed. Thereafter a genetic algorithm based clustering technique which uses this point symmetry based distance (GASDCA) is developed. GASDCA is therefore able to detect both convex and non-convex clusters. Kd-tree based nearest neighbor search is used to reduce the complexity of nding the closest symmetric point. The proposed

2014 International Conference on Computational Intelligence and Communication Networks, 2014

Most of the image preprocessing techniques by existing neighborhood neural networks, suffer from ... more Most of the image preprocessing techniques by existing neighborhood neural networks, suffer from the problem of false classification of the image features. This is mainly due to the redundancy in the interconnectivity patterns of the networks. The larger number of interconnections in these networks implies a larger network complexity as well. A fuzzy set-theoretic pruning algorithm to refine the interconnection

Soft Computing for Image and Multimedia Data Processing, 2013

16th International Conference on VLSI Design, 2003. Proceedings., 2003

This paper introduces the novel concept of ghost-FSM as a BIST structure for sequential machines.... more This paper introduces the novel concept of ghost-FSM as a BIST structure for sequential machines. A ghost-FSM is a contrived implicit machine that is deliberately embedded within the framework of a given FSM. A ghost-FSM remains dormant except at the testing phase, when it helps to generate those test patterns that, otherwise, could not be generated due to certain idiosyncrasies

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013

ABSTRACT In this article a fuzzy rule-based classifier has been designed on the framework of mult... more ABSTRACT In this article a fuzzy rule-based classifier has been designed on the framework of multiobjective Particle Swarm Optimization. The proposed approach is applied on microarray gene expression data to obtain genes with significant expression with respect to two different classes. Two fuzzy sets are represented with the linguistic values “high” and “low”. On the training dataset, the proposed approach is applied for the purpose of deriving good classification rules. To be precise, the good rules are those that have less attributes in the antecedent part and provide maximum accuracy. Moreover we also consider the existing redundancy among the selected rules which should be minimized. Here the underlying structure is modeled using multiobjective PSO with the support of non-dominated sorting and crowding distance sorting. The first objective is to maximize the classification accuracy and second objective is to minimize the rule-base complexity (number of rules and average rule length) and the redundancy of the rules. The performance of the proposed algorithm is compared with that of single objective versions, Support Vector machine classifier and Bayes classifier on several real-life datasets.

IEEE Journal of Translational Engineering in Health and Medicine, 2014

2011 International Conference on Recent Trends in Information Systems, 2011

This article introduces a semisupervised support vector machine classification technique that exp... more This article introduces a semisupervised support vector machine classification technique that exploits both labeled and unlabeled points for addressing the problem of pixel classification of remote sensing images. The proposed method is based on the transductive inference and in particular transductive SVM (TSVM). Transductive SVM progressively searches a reliable separating hyperplane in the high dimensional space through iterative method exploiting both labeled and unlabeled samples. In particular, a thresholding strategy and similarity in classification between successive transductive sets are exploited to select the reliable samples from the unlabeled set. The proposed technique is applied on two labeled datasets and one large unlabeled image dataset: IRS image of Mumbai and compared with the standard SVM and progressive TSVM (PTSVM). Experimental results confirm that employing this learning scheme removes unnecessary points to a great extent from the unlabeled set and increases the accuracy level on the other hand. Comparison is made in terms of accuracy for the numeric datasets and quantitative cluster validity indices as well as classified image quality for the image dataset.

2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2013

Concepts, Methodologies, Tools, and Applications, 2013

ABSTRACT Identification of cancer subtypes is the central goal in the cancer gene expression data... more ABSTRACT Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based clustering algorithm. A natural basis for analyzing gene expression data using the symmetry-based algorithm is to group together genes with similar symmetrical patterns of microarray expressions. Rough-set theory helps in faster convergence and initial automatic optimal classification, thereby solving the problem of unknown knowledge of number of clusters in gene expression measurement data. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts to overcome overlapping cluster problem. The rough modified symmetry-based clustering algorithm is compared with another newly implemented rough-improved symmetry-based clustering algorithm and existing K-Means algorithm over five benchmark cancer gene expression data sets, to demonstrate its superiority in terms of validity. The statistical analyses are also performed to establish the significance of this rough modified symmetry-based clustering approach.

... Joaquín Derraca,∗, Salvador Garcíab, Daniel Molinac, Francisco Herreraa ... Tel.: +34 958 240... more ... Joaquín Derraca,∗, Salvador Garcíab, Daniel Molinac, Francisco Herreraa ... Tel.: +34 958 240598; fax: +34 958 243317. E-mail addresses: jderrac@decsai.ugr.es (J. Derrac), sglopez@ujaen.es (S. García), daniel.molina@uca.es (D. Molina), herrera@decsai.ugr.es (F. Herrera). ...