Indraprastha University

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Indraprastha University

India
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Jatana N.,MSIT | Suri B.,Indraprastha University | Kumar P.,MSIT | Wadhwa B.,National University of Singapore
ACM International Conference Proceeding Series | Year: 2016

This paper presents a novel approach for reduction of test cases in a test suite using mutation testing. The proposed approach maps the problem of reduction of test cases to the set cover problem which is one of the Karp's originally proposed 21 NP hard problems. The solution to the problem uses a Greedy Approach to find the reduced test suite. Our preliminary evaluation on three programs (Triangle problem, Quadratic problem and TCAS) shows encouraging results. © 2016 ACM.


Suri B.,Indraprastha University | Jatana N.,MSIT | Tomer M.,MSIT
Proceedings of the 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education, MITE 2015 | Year: 2015

Learning Software Engineering principles, processes, methods and tools is a prerequisite to becoming a Software Engineer. The technological advancements and revolution in this new century are probing the boundaries of education and training in software engineering. This work tries to locate the shortcomings in existing approaches to Education in Software Engineering (ESE) in respect to today's technological advancements. We hereby proposed a new model for Education in Software Engineering to bridge the industry-academia gap. © 2015 IEEE.


Sharma S.K.,Indraprastha University | Chandra P.,University of Delhi
Proceedings - 2010 International Conference on Computational Intelligence and Communication Networks, CICN 2010 | Year: 2010

This paper presents an adaptive slope basic dynamic node creation algorithm for single hidden layer neural networks (ASBDNCA). The proposed algorithm is a constructive approach of building a single hidden layer neural network. The ASBDNCA puts emphasis on architectural adaptation and functional adaptation during learning. It uses gradient descent optimization method in sequential mode as the weights update rule of individual hidden node. To achieve functional adaptation, the slope of the sigmoidal activation function (SAF) is adapted during learning. The algorithm determines not only optimal number of hidden nodes, as also optimum value of the slope parameter for the non-linear nodes. One simple variant derived from ASBDNCA in which the slope parameter of SAF is fixed. Both the variants are compared to each other on five function approximation tasks. Simulation results reveal that adaptive slope SAF present several advantages over traditional fixed shape sigmoidal activation function, resulting in increased flexibility, smoother learning, better convergence and better generalization performance. © 2010 IEEE.


Sharma S.K.,Indraprastha University | Chandra P.,University of Delhi
Proceedings - 3rd International Conference on Emerging Trends in Engineering and Technology, ICETET 2010 | Year: 2010

Cascade 2 algorithm is a variant of Cascade-Correlation algorithm that is a well-known and widely used constructive neural networks algorithm. We propose an adaptive slope sigmoidal function cascading neural networks algorithm (ASCNNA) in this paper. The proposed algorithm emphasizes on architectural adaptation and functional adaptation during training. This algorithm is a constructive approach of building cascading architecture and uses gradient descent method in sequential mode as the weight update rule of individual hidden node. To achieve functional adaptation, the slope of the sigmoidal function is adapted during learning. The algorithm determines not only the optimum number of hidden layers' node, as also the optimum value of the slope parameter of sigmoidal function for nonlinear nodes. One simple variant derived from ASCNNA is where the slope parameter of sigmoidal function is fixed. Both the variants are compared to each other on five function approximation tasks. Simulation results reveal that adaptive slope sigmoidal function presents several advantages over traditional fixed shape sigmoidal function, resulting in increased flexibility, smoother learning, and better convergence and generalization performance. © 2010 IEEE.


Pandey D.,University of Petroleum and Energy Studies | Singh A.K.,Indraprastha University | Sarkar A.,University of Petroleum and Energy Studies
76th European Association of Geoscientists and Engineers Conference and Exhibition 2014: Experience the Energy - Incorporating SPE EUROPEC 2014 | Year: 2014

The transportation of heavy oil is always the matter of concern for oil industries. The viscosity of heavy oil ranges from 200 CP (centipoise) to 5,000 CP and is strict function of composition of hydrocarbons. Due to high viscosity, the mobility of heavy crude is very low at reservoir conditions. Moreover, it is very difficult to flow heavy crude at cold temperatures from offshore regions to the refinery. Therefore, despite in the improvements in thermal recovery methods such as Toe to Heel Air Injection (THAI), Steam assisted Gravity Drainage (SAGD), Steam Injections, etc., some diluents like C4+ and hydrocarbon gases are introduced with it. Recently, the team of researchers under Rongjia Tao, a physicist at Temple University in Philadelphia, has found an alternative way to transport heavy crude by applying electric and magnetic impulses. Magnetic impulses are basically more effective for paraffin based crude oil and its effect lasts for 8 to 10 hours after the implementation of strong magnetic field. This technical paper aims at analyzing Mr. Tao's experiment, rectifying its limitations and proposing a better experimental setup for the pilot experiment so that it can be further extrapolated to the industrial point of application.


Kumar S.,University of Petroleum and Energy Studies | Singh V.,Indraprastha University | Mandal U.K.,Indraprastha University | Kotnala R.K.,National Physical Laboratory India
Inorganica Chimica Acta | Year: 2015

Co0.5Zn0.5Fe2O4 ferrite nanocrystals with average diameter in the range of 5-6 nm have been synthesized by reverse microemulsion technique. X-ray diffraction (XRD), transmission electron microscopy (TEM) and vibrating sample magnetometer (VSM) are used to characterize the structural, morphological, and magnetic properties. X-ray analysis showed that the nanocrystals possess cubic spinel structure. The absence of hysteresis, negligible remanence, and coercivity at 300 K indicate the superparamagnetic character and single domain in the nanocrystalline Co0.5Zn0.5Fe2O4 ferrite materials. The nanocrystalline Co0.5Zn0.5Fe2O4 ferrite was annealed at 600 °C. As a result of heat treatment, the average particle size increases from 5 nm to 7 nm and the corresponding magnetization value increases to 15.94 emu/g at 300 K. However, at a low temperature of 100 K, the annealed samples show hysteresis loop which is the characteristic of a superparamagnetic to ferromagnetic transition. A comparative study of the magnetic properties of Co0.5Zn0.5Fe2O4 ferrite nanocrystals obtained from (1) reverse microemulsion and (2) chemical co-precipitation route has also been carried out. © 2015 Elsevier B.V. All rights reserved.


Sharma S.K.,Indraprastha University | Chandra P.,University of Delhi
Advanced Materials Research | Year: 2012

In this paper we propose a constructive algorithm with adaptive sigmoidal function for designing single hidden layer feedforward neural network (CAASF). The proposed algorithm emphasizes on architectural adaptation and functional adaptation during training. This algorithm is a constructive approach to building single hidden layer neural networks dynamically. The activation functions used at non-linear hidden nodes are belonging to the well-defined sigmoidal class and adapted during training. The algorithm determines not only optimum number of hidden nodes, as also optimum sigmoidal function for the non-linear nodes. One simple variant derived from CAASF is where the sigmoidal function used at the hidden nodes is fixed. Both the variants are compared to each other on five regression functions. Simulation results reveal that adaptive sigmoidal function presents several advantages over traditional fixed sigmoid function, resulting in increased flexibility, smoother learning, better convergence and better generalization performance. © (2012) Trans Tech Publications, Switzerland.


Sharma S.K.,Indraprastha University | Chandra P.,University of Delhi
Advanced Materials Research | Year: 2012

This paper presents cascading neural networks using adaptive sigmoidal function (CNNASF). The proposed algorithm emphasizes on architectural adaptation and functional adaptation during training. This algorithm is a constructive approach to building cascading architecture dynamically. The activation functions used at the hidden layers' node are belonging to the well-defined sigmoidal class and adapted during training. The algorithm determines not only optimum number of hidden layers' node, as also optimum sigmoidal function for them. One simple variant derived from CNNASF is where the sigmoid function used at the hidden layers' node is fixed. Both the variants are compared to each other on five regression functions. Simulation results reveal that adaptive sigmoidal function presents several advantages over traditional fixed sigmoid function, resulting in increased flexibility, smoother learning, better convergence and better generalization performance. © (2012) Trans Tech Publications, Switzerland.


Sharma S.K.,Indraprastha University | Chandra P.,University of Delhi
Advances in Intelligent and Soft Computing | Year: 2011

In this paper, we propose an adaptive sigmoidal activation function cascading neural networks. The proposed algorithm emphasizes architectural adaptation and functional adaptation during training. This algorithm is a constructive approach to building cascading architecture dynamically. To achieve functional adaptation, an adaptive sigmoidal activation function is proposed for the hidden layers' node. The algorithm determines not only optimum number of hidden layers' nodes, as also optimum sigmoidal function for them. Four variants of the proposed algorithm are developed and discussed on the basis of activation function used. All the variants are empirically evaluated on five regression functions in terms of learning accuracy and generalization capability. Simulation results reveal that adaptive sigmoidal activation function presents several advantages over traditional fixed sigmoid function, resulting in increased flexibility, smoother learning, better learning accuracy and better generalization performance. © 2011 Springer-Verlag Berlin Heidelberg.


Mohapatra S.,Indraprastha University
Physical Chemistry Chemical Physics | Year: 2016

Nanocomposite thin films containing Ag nanoparticles embedded in the GeO2-SiO2 matrix were synthesized by the atom beam co-sputtering technique. The structural, optical and plasmonic properties and the chemical composition of the nanocomposite thin films were studied by transmission electron microscopy (TEM) with energy dispersive X-ray spectroscopy (EDX), UV-visible absorption spectroscopy and X-ray photoelectron spectroscopy (XPS). UV-visible absorption studies on Ag-SiO2 nanocomposites revealed the presence of a strong localized surface plasmon resonance (LSPR) peak characteristic of Ag nanoparticles at 413 nm, which showed a blue shift of 26 nm (413 to 387 nm) along with a significant broadening and drastic decrease in intensity with the incorporation of 16 at% of Ge into the SiO2 matrix. TEM studies on Ag-GeO2-SiO2 nanocomposite thin films confirmed the presence of Ag nanoparticles with an average size of 3.8 nm in addition to their aggregates with an average size of 16.2 nm. Thermal annealing in air resulted in strong enhancement in the intensity of the LSPR peak, which showed a regular red shift of 51 nm (from 387 to 438 nm) with the increase in annealing temperature up to 500°C. XPS studies showed that annealing in air resulted in oxidation of excess Ge atoms in the nanocomposite into GeO2. Our work demonstrates the possibility of controllably tuning the LSPR of Ag nanoparticles embedded in the GeO2-SiO2 matrix by single-step thermal annealing, which is interesting for optical applications. © 2016 the Owner Societies.

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