Jadavpur University

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

Swarm Intelligence: A Review of Algorithms

Abstract: Swarm intelligence (SI), an integral part in the field of artificial intelligence, is gradually gaining prominence, as more and more high complexity problems require solutions which may be sub-optimal but yet achievable within a reasonable period of time. Mostly inspired by biological systems, swarm intelligence adopts the collective behaviour of an organized group of animals, as they strive to survive. This study aims to discuss the governing idea, identify the potential application areas and present a detailed survey of eight SI algorithms. The newly developed algorithms discussed in the study are the insect-based algorithms and animal-based algorithms in minute detail. More specifically, we focus on the algorithms inspired by ants, bees, fireflies, glow-worms, bats, monkeys, lions and wolves. The inspiration analyses on these algorithms highlight the way these algorithms operate. Variants of these algorithms have been introduced after the inspiration analysis. Specific areas for the application of such algorithms have also been highlighted for researchers interested in the domain. The study attempts to provide an initial understanding for the exploration of the technical aspects of the algorithms and their future scope by the academia and practice.

Charged anisotropic matter with linear or nonlinear equation of state

Abstract: Ivanov pointed out substantial analytical difficulties associated with self-gravitating, static, isotropic fluid spheres when pressure explicitly depends on matter density. Simplifications achieved with the introduction of electric charge were noticed as well. We deal with self-gravitating, charged, anisotropic fluids and get even more flexibility in solving the Einstein-Maxwell equations. In order to discuss analytical solutions we extend Krori and Barua's method to include pressure anisotropy and linear or nonlinear equations of state. The field equations are reduced to a system of three algebraic equations for the anisotropic pressures as well as matter and electrostatic energy densities. Attention is paid to compact sources characterized by positive matter density and positive radial pressure. Arising solutions satisfy the energy conditions of general relativity. Spheres with vanishing net charge contain fluid elements with unbounded proper charge density located at the fluid-vacuum interface. Notably the electric force acting on these fluid elements is finite, although the acting electric field is zero. Net charges can be huge (${10}^{19}C$) and maximum electric field intensities are very large (${10}^{23}--{10}^{24}\text{ }\text{ }\mathrm{statvolt}/\mathrm{cm}$) even in the case of zero net charge. Inward-directed fluid forces caused by pressure anisotropy may allow equilibrium configurations with larger net charges and electric field intensities than those found in studies of charged isotropic fluids. Links of these results with charged strange quark stars as well as models of dark matter including massive charged particles are highlighted. The van der Waals equation of state leading to matter densities constrained by cubic polynomial equations is briefly considered. The fundamental question of stability is left open.

On some aspects of validation of predictive quantitative structure–activity relationship models

Abstract: The success of any quantitative structure-activity relationship model depends on the accuracy of the input data, selection of appropriate descriptors and statistical tools and, most importantly, the validation of the developed model. Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose. This review focuses on the importance of validation of quantitative structure-activity relationship models and different methods of validation. Some important issues, such as internal versus external validation, method of selection of training set compounds and training set size, applicability domain, variable selection and suitable parameters to indicate external predictivity, are also discussed.

Cross-correlation aided support vector machine classifier for classification of EEG signals

Abstract: Over the last few decades pattern classification has been one of the most challenging area of research. In the present-age pattern classification problems, the support vector machines (SVMs) have been extensively adopted as machine learning tools. SVM achieves higher generalization performance, as it utilizes an induction principle called structural risk minimization (SRM) principle. The SRM principle seeks to minimize the upper bound of the generalization error consisting of the sum of the training error and a confidence interval. SVMs are basically designed for binary classification problems and employs supervised learning to find the optimal separating hyperplane between the two classes of data. The main objective of this paper is to introduce a most promising pattern recognition technique called cross-correlation aided SVM based classifier. The idea of using cross-correlation for feature extraction is relatively new in the domain of pattern recognition. In this paper, the proposed technique has been utilized for binary classification of EEG signals. The binary classifiers employ suitable features extracted from crosscorrelograms of EEG signals. These cross-correlation aided SVM classifiers have been employed for some benchmark EEG signals and the proposed method could achieve classification accuracy as high as 95.96% compared to a recently proposed method where the reported accuracy was 94.5%.