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.

Mixed micelle formation among anionic gemini surfactant (212) and its monomer (SDMA) with conventional surfactants (C12E5 and C12E8) in brine solution at pH 11.

Abstract: The micellization of anionic gemini surfactant, N,N‘-ethylene(bis(sodium N-dodecanoyl-β-alaninate)) (212), and its monomer, N-dodecanoyl-N-methyl alaninate (SDMA), and polyethoxylated nonionic surf...

An analytic model for interacting dark energy and its observational constraints

Abstract: The paper deals with a theoretical model for interacting dark energy. The interaction between the cold dark matter (dust) and the dark energy has been assumed to be non-gravitational in nature. Exact analytic cosmological solutions are obtained both for constant and variable equation of state for dark energy. It is found that, for very small value of the coupling parameter (in the interaction term), the model asymptotically extends up to $\Lambda$CDM, while the model can enter into the phantom domain asymptotically, if the coupling parameter is not so small. Both the solutions are then analyzed with 194 Supernovae Type Ia data. The best fit parameters are shown with 1$\sigma$ and 2$\sigma$ confidence intervals. Finally, we have discussed the cosmographic parameters for both the cases.

A Machine Vision Technique for Grading of Harvested Mangoes Based on Maturity and Quality

Abstract: In agricultural and food industry, the proper grading of fruits is very important to increase the profitability. In this paper, a scheme for automated grading of mango (Mangifera Indica L.) according to maturity level in terms of actual-days-to-rot and quality attributes, such as size, shape, and surface defect has been proposed. The proposed scheme works on intelligent machine vision-based techniques for grading of mangoes in four different categories, which are determined on the basis of market distance and market value. In this system, video image is captured by a charge couple device camera placed on the top of a conveyer belt carrying mangoes, thereafter several image processing techniques are applied to collect features, which are sensitive to the maturity and quality. For maturity prediction in terms of actual-days-to-rot, support vector regression has been employed and for the estimation of quality from the quality attributes, multiattribute decision making system has been adopted. Finally, fuzzy incremental learning algorithm has been used for grading based on maturity and quality. The performance accuracy achieved using this proposed system for grading of mango fruit is nearly 87%. Moreover, the repeatability of the proposed system is found to be 100%.