Munjal University

Gurgaon, India

Munjal University

Gurgaon, India

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Trivedi S.K.,Munjal University | Dey S.,Information Systems Management Institute
VINE Journal of Information and Knowledge Management Systems | Year: 2016

Purpose: The email is an important medium for sharing information rapidly. However, spam, being a nuisance in such communication, motivates the building of a robust filtering system with high classification accuracy and good sensitivity towards false positives. In that context, this paper aims to present a combined classifier technique using a committee selection mechanism where the main objective is to identify a set of classifiers so that their individual decisions can be combined by a committee selection procedure for accurate detection of spam. Design/methodology/approach: For training and testing of the relevant machine learning classifiers, text mining approaches are used in this research. Three data sets (Enron, SpamAssassin and LingSpam) have been used to test the classifiers. Initially, pre-processing is performed to extract the features associated with the email files. In the next step, the extracted features are taken through a dimensionality reduction method where non-informative features are removed. Subsequently, an informative feature subset is selected using genetic feature search. Thereafter, the proposed classifiers are tested on those informative features and the results compared with those of other classifiers. Findings: For building the proposed combined classifier, three different studies have been performed. The first study identifies the effect of boosting algorithms on two probabilistic classifiers: Bayesian and Naïve Bayes. In that study, AdaBoost has been found to be the best algorithm for performance boosting. The second study was on the effect of different Kernel functions on support vector machine (SVM) classifier, where SVM with normalized polynomial (NP) kernel was observed to be the best. The last study was on combining classifiers with committee selection where the committee members were the best classifiers identified by the first study i.e. Bayesian and Naïve bays with AdaBoost, and the committee president was selected from the second study i.e. SVM with NP kernel. Results show that combining of the identified classifiers to form a committee machine gives excellent performance accuracy with a low false positive rate. Research limitations/implications: This research is focused on the classification of email spams written in English language. Only body (text) parts of the emails have been used. Image spam has not been included in this work. We have restricted our work to only emails messages. None of the other types of messages like short message service or multi-media messaging service were a part of this study. Practical implications: This research proposes a method of dealing with the issues and challenges faced by internet service providers and organizations that use email. The proposed model provides not only better classification accuracy but also a low false positive rate. Originality/value: The proposed combined classifier is a novel classifier designed for accurate classification of email spam. © 2016, © Emerald Group Publishing Limited.

Sharma R.,Munjal University
CEUR Workshop Proceedings | Year: 2016

In this paper, we present our system addressing Task 1 of CL-SciSumm Shared Task at BIRNDL 2016. Our system makes use of lexical and syntactic dependency cues, and applies rule-based approach to extract text spans in the Reference Paper that accurately reflect the citances. Further, we make use of lexical cues to identify discourse facets of the paper to which cited text belongs. The lexical and syntactic cues are obtained on pre-processed text of the citances, and the reference paper. We report our results obtained for development set using our system for identifying reference scope of citances in this paper.

Trivedi S.K.,Munjal University | Dey S.,Information Systems Management Institute
ACM International Conference Proceeding Series | Year: 2016

Classification of the spam from bunch of the email files is a challenging research area in text mining domain. However, machine learning based approaches are widely experimented in the literature with enormous success. For excellent learning of the classifiers, few numbers of informative features are important. This researh presents a comparative study between various supervised feature selection methods such as Document Frequency (DF), Chi-Squared ( 2 x), Information Gain (IG), Gain Ratio (GR), Relief F (RF), and One R (OR). Two corpuses (Enron and SpamAssassin) are selected for this study where enron is main corpus and spamAssassin is used for validation of the results. Bayesian Classifier is taken to classify the given corpuses with the help of features selected by above feature selection techniques. Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate whereas DF and 2 xwere not so effective methods. Bayesian classifier has proven its worth in this study in terms of good performance accuracy and low false positives. © 2016 ACM.

Dawra N.,Munjal University | Dawra N.,Indian Institute of Technology Delhi | Ram R.N.,Indian Institute of Technology Delhi
Synthesis (Germany) | Year: 2016

The optimized preparation of a rare class of highly functionalized (chlorinated and heteroatom-rich) monocyclic β-lactams by the Staudinger reaction of reactive acyclic S-alkylisothioureas with dichloroketene is presented. The use of acyclic S-alkylisothioureas in the Staudinger β-lactam synthesis has been demonstrated for the first time. The present method is mild, economical and wide in scope. Copyright © 2016, Georg Thieme Verlag. All rights reserved.

Mussada E.K.,Munjal University | Patowari P.K.,National Institute of Technology Silchar
Surface Engineering | Year: 2015

The present work focuses on characterisation of the deposited layer on aluminium substrate processed with electric discharge coating process. Different characterisation techniques such as scanning electron microscopy, energy dispersive X-ray and X-ray diffraction (XRD) have been employed to study the surface morphological changes, presence of materials and their phase transformations due to the formation and deposition of composite material layer on work surface. The grain size of the deposited particles has a significant effect on mechanical properties. Thus, an attempt has been made to calculate the grain size, dislocation density and microstrain of deposited material and its particles. The formation of tungsten carbides, namely, WC and W2C, is detected through XRD analysis. Clusters of globular particles in nanoscale are observed throughout the deposited layer. © 2015 Institute of Materials, Minerals and Mining Published by Maney on behalf of the Institute.

Awasthi M.K.,University of Petroleum and Energy Studies | Asthana R.,Munjal University | Agrawal G.S.,Manglayatan University
International Journal of Heat and Mass Transfer | Year: 2014

We study the linear Kelvin-Helmholtz instability of the interface between two viscous and incompressible fluids, when the phases are enclosed between two horizontal cylindrical surfaces coaxial with the interface in presence of mass and heat transfer across the interface. Here we use an irrotational theory known as viscous correction for the viscous potential flow theory; in which the discontinuities in the irrotational tangential velocity and shear stress are eliminated in the global energy balance by taking viscous contributions to the irrotational pressure. Both asymmetric and axisymmetric disturbances have been studied and stability criterion is given in terms of a critical value of relative velocity. It has been shown that the irrotational viscous flow with viscous correction gives rise to exactly the same dispersion relation as obtained by the dissipation method in which the viscous effect is accounted for by evaluating viscous dissipation using the irrotational flow. It has been observed that heat and mass transfer has destabilizing effect while irrotational shearing stresses stabilize the system. © 2014 Elsevier Ltd. All rights reserved.

Ahmed S.,Munjal University | Mammosser J.D.,Oak Ridge National Laboratory
Review of Scientific Instruments | Year: 2015

A R&D effort for in situ cleaning of 1.5 GHz Superconducting Radio Frequency (SRF) cavities at room erature using the plasma processing technique has been initiated at Jefferson Lab. This is a step toward the cleaning of cryomodules installed in the Continuous Electron Beam Accelerator Facility (CEBAF). For this purpose, we have developed an understanding of plasma discharge in a 5cell CEBAFtype SRF cavity having configurations similar to those in the main accelerator. The focus of this study involves the detailed investigations of developing a plasma discharge inside the cavity volume and avoids the breakdown condition in the vicinity of the ceramic RF window. A plasma discharge of the gas mixture ArO2 (90%:10%) can be established inside the cavity volume by the excitation of a resonant 4π/5 TM010mode driven by a klystron. The absence of any external magnetic field for generating the plasma is suitable for cleaning cavities installed in a complex cryomodule assembly. The procedures developed in these experimental investigations can be applied to any complex cavity structure. Details of these experimental measurements and the observations are discussed in the paper. © 2015 AIP Publishing LLC.

Awasthi M.K.,University of Petroleum and Energy Studies | Asthana R.,Munjal University | Uddin Z.,Munjal University
International Communications in Heat and Mass Transfer | Year: 2016

In this work, a viscous potential flow theory is used to study the nonlinear Kelvin-Helmholtz instability of the interface between two viscous, incompressible and thermally conducting fluids, when the phases are enclosed between two horizontal cylindrical surfaces coaxial with the interface and when there is mass and heat transfer across the interface. The method of multiple time expansions is applied and it is shown that the evolution of amplitudes is governed by a nonlinear first order partial differential equation. The various stability criteria are discussed both analytically and numerically, stability diagrams are studied graphically. It is observed that the heat and mass transfer has destabilizing effect on the stability of the system in the nonlinear analysis. © 2015 Elsevier Ltd.

Gupta S.,Munjal University | Gabrani G.,Munjal University
Proceedings of 2016 SAI Computing Conference, SAI 2016 | Year: 2016

The Grid environment is highly dynamic in nature where a variety of users from all over the world try to access the resources that are again distributed all over. Both the users and resources are highly varied. The users can be both valid and invalid, can have single job or multiple jobs, and can have jobs that can have different processing requirements, may be an old loyal user or may be a new one. Similarly the resources can have different characteristics like the processing speed, number of processing elements each resource has, whether the resources have joined or left the grid and so on. In such an environment if an un-authorized user is allowed to access the resource, there is a security threat to the grid. Even if one unauthorized user is allowed to access the grid; the whole grid becomes susceptible to many security threats. So resources should only be available to authorized users. Moreover, as number of users and their jobs increases, they face a lot of competition amongst themselves to get access to the resources. In order to solve the conflicts the resources must be allocated to the users by some arbitration mechanism. In this paper we propose a grid framework that focusses on solving these two major problems namely authentication and arbitration by means of using a robust authentication mechanism and by assigning priority to users and their jobs. In order to achieve secure resource allocation to valid user jobs for maximum resource utilization along with minimizing waiting time and elapsed time of jobs, a secure resource allocation scheme using ECC algorithm with two-level priority has been proposed. The results have been compared with a non-priority based system and our proposed system shows substantial improvement in both waiting time and elapsed time of jobs with better resource utilization. © 2016 IEEE.

Gabrani G.,Munjal University | Saini N.,Munjal University
2016 Symposium on Colossal Data Analysis and Networking, CDAN 2016 | Year: 2016

Software effort estimation is a complicated task being carried out by software developers as very little information is available to them in the early phases of software development. The information collected about various attributes of software needs to be subjective, which otherwise can lead to uncertainty. Inaccurate software effort estimation can be disastrous as both underestimation and overestimation may result in schedule overruns and incorrect estimation of budget. This paper focuses on the comparative study of various non-algorithmic techniques used for estimating the software effort by empirical evaluation of five different evolutionary learning algorithms. The accuracy of these algorithms is found out and the behavior of these algorithms is analyzed with respect to the size and the type of data. All the five techniques are applied on three different datasets and various paramenters such as MMRE, PRED(25), PRED(50), PRED(75) are calculated. The proposed results are compared to other machine learning methods like SVR, ANFIS etc. The results show that evolutionary learning algorithms give more accurate results than machine learning algorithms. © 2016 IEEE.

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