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Nangal Township, India

The LNM Institute of Information Technology , is a deemed university located in Jaipur, India, on an 100-acre campus. The institute is a public-private partnership between the Lakshmi Niwas Mittal and Usha Mittal foundation and the Government of Rajasthan as an autonomous non-profit organization.The Institute began in 2003 with a branch in India, Communication and Computer Engineering, in a temporary campus in Jaipur. Today, the institute operates out of its campus, about 10 km from the Jaipur-Agra Highway and 20 km from the heart of Jaipur City. The institute offers the following four disciplines: Communication and Computer Engineering Electronics and Communication Engineering Computer Science Engineering Mechanical and Mechatronics Engineering The LNMIIT infrastructure includes on-campus housing, hostels for boys and girls, sports facilities, shopping complex, studio apartments & faculty housing, an open-air theater, lecture halls, labs, and LAN connectivity. Wikipedia.


Tanveer M.,The LNM Institute of Information Technology
Cognitive Computation | Year: 2014

In this paper, we propose a new linear programming formulation of exact 1-norm twin support vector machine (TWSVM) for classification whose solution is obtained by solving a pair of dual exterior penalty problems as unconstrained minimization problems using Newton–Armijo algorithm. The idea of our formulation is to reformulate TWSVM as a strongly convex problem by incorporated regularization techniques and then derive an exact 1-norm linear programming formulation for TWSVM to improve robustness and sparsity. The solution of two modified unconstrained minimization problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems in TWSVM and TBSVM, which leads to extremely simple and fast algorithm. One significant advantage of our proposed method is the implementation of structural risk minimization principle. However, only empirical risk is considered in the primal problems of TWSVM due to its complex structure and thus may incur overfitting and suboptimal in some cases. Our approach has the advantage that a pair of matrix equation of order equals to the number of input examples is solved at each iteration of the algorithm. The algorithm converges from any starting point that can be easily implemented in MATLAB without using any optimization packages. Computational comparisons of our proposed method against original TWSVM, GEPSVM and SVM have been made on both synthetic and benchmark datasets. Experimental results show that our method is better or comparable in both computation time and classification accuracy. © 2014, Springer Science+Business Media New York. Source


Rangarajan R.,Physical Research Laboratory | Sarkar A.,Physical Research Laboratory | Sarkar A.,The LNM Institute of Information Technology
Astroparticle Physics | Year: 2013

Flat directions in generic supersymmetric theories can change the thermal history of the Universe. A novel scenario was proposed earlier where the vacuum expectation value of the flat directions induces large masses for all the gauge bosons and gauginos. This delays the thermalization of the Universe after inflation and solves the gravitino problem. In this article we perform a detailed calculation of the above scenario. We include the appropriate initial state particle distribution functions, consider the conditions for the feasibility of the non-thermal scenario, and investigate phase space suppression of gravitino production in the context of heavy gauge bosons and gauginos in the final state. We find that the total gravitino abundance generated is consistent with cosmological constraints. © 2013 Elsevier B.V. All rights reserved. Source


Tanveer M.,The LNM Institute of Information Technology
Knowledge and Information Systems | Year: 2015

In this paper, a new unconstrained minimization problem formulation is proposed for linear programming twin support vector machine (TWSVM) classifiers. The proposed formulation leads to two smaller-sized unconstrained minimization problems having their objective functions piecewise differentiable. However, since their objective functions contain the non-smooth “plus” function, two new smoothing approaches are assumed to solve the proposed formulation, and then apply Newton-Armijo algorithm. The idea of our formulation is to reformulate TWSVM as a strongly convex problem by incorporated regularization techniques and then derive smooth 1-norm linear programming formulation for TWSVM to improve robustness. One significant advantage of our proposed algorithm over TWSVM is that the structural risk minimization principle is implemented in the primal problems which embodies the marrow of statistical learning theory. In addition, the solution of two modified unconstrained minimization problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems in TWSVM and TBSVM, which leads to extremely simple and fast algorithm. Our approach has the advantage that a pair of matrix equation of order equals to the number of input examples is solved at each iteration of the algorithm. The algorithm converges from any starting point that can be easily implemented in MATLAB without using any optimization packages. The performance of our proposed method is verified experimentally on several benchmark and synthetic datasets. Experimental results show the effectiveness of our methods in both training time and classification accuracy. © 2014, Springer-Verlag London. Source


Dixit D.,Jaypee University of Engineering & Technology | Dixit D.,The LNM Institute of Information Technology | Sahu P.R.,Indian Institute of Technology Guwahati
IEEE Transactions on Wireless Communications | Year: 2013

Performance of quadrature amplitude modulation (QAM) scheme in two-wave with diffuse power (TWDP) fading environment is analyzed. Closed-form expressions for the exact average symbol error rate (ASER) of general order rectangular QAM (RQAM) and cross QAM (XQAM) schemes are presented using moment generating function of TWDP fading distribution. Obtained ASER expressions are in the form of Appell's (Φ1(·)) and Lauricella's (Φ1 3(·)) hypergeometric functions which can be numerically evaluated using either integral or series representation. Further, closed-form expression for the nth order moment of the received signal-to-noise ratio is derived. Numerical results show excellent agreement with simulation results. © 2002-2012 IEEE. Source


Adhikari R.,The LNM Institute of Information Technology
Applied Intelligence | Year: 2015

Forecasting a time series with reasonable accuracy is an important but quite difficult task that has been attracting lots of research attention for many years. A widely approved fact is that combining forecasts from multiple models significantly improves the forecasting precision as well as often produces better forecasts than each constituent model. The existing literature is accumulated with linear methods of combining forecasts but nonlinear approaches have received very limited research attention, so far. This paper proposes a novel nonlinear forecasts combination mechanism in which the combined model is constructed from the individual forecasts and the mutual dependencies between pairs of forecasts. The individual forecasts are performed through three well recognized models, whereas five correlation measures are investigated for estimating the mutual association between two different forecasts.Empirical analysis with six real-world time series demonstrates that the proposed ensemble substantially reduces the forecasting errors and also outperforms each component model as well as other conventional linear combination methods, in terms of out-of-sample forecasting accuracy. © 2015, Springer Science+Business Media New York. Source

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