JIIT Noida

Greater Noida, India

JIIT Noida

Greater Noida, India
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Chattopadhyay A.,Indian School of Mines | Gupta S.,Indian School of Mines | Sharma V.K.,Defence Science and Technology Organisation, Australia | Kumari P.,JIIT Noida
Acta Mechanica | Year: 2011

The stresses developed in a body due to a moving load causing fracture are an interesting problem of mechanics having its application toward the stability of a medium. This paper is concerned with the stresses developed in an irregular isotropic half-space due to a normal moving load at a rough free surface. The irregularity has been taken in the form of a parabola and a rectangular irregularity has also been discussed as a special case of parabolic irregularity. Closed-form expressions for the normal and shear stresses have been obtained. The effect of friction, irregularity factor, and maximum amplitude of irregularity has been discussed for both stresses. © 2011 Springer-Verlag.

Kumari V.,BIT Mesra | Srivastava S.,JIIT Noida
2015 International Conference on Signal Processing and Communication, ICSC 2015 | Year: 2015

An H-plane Substrate Integrated Waveguide (SIW) horn antenna is designed and simulated using the Ansoft HFSS software. The shape of the flare is varied from a simple linear taper to corrugated (square corrugation and triangular corrugation) and stepped impedance transformation. The performance of the antenna with different flares is analyzed and compared for optimum results. © 2015 IEEE.

Joshi M.,JIIT Noida | Sardana N.,JIIT Noida
2014 7th International Conference on Contemporary Computing, IC3 2014 | Year: 2014

To enhance the testing, testability has been introduced. Testability refers the ease of testing with which faults in software are revealed. PIE (Propagation, Infection, Execution) is the strategy to estimate the software testability. To reduce the computational overhead of PIE, E-PIE (Extended Propagation, Infection, Execution) has been introduced. E-PIE technique consists of three stages. During the initial stages program is broken into blocks and then groups are generated. During the final stage target statements are being marked that is an essential part of E-PIE as testability computed for the marked target statement becomes the testability of whole group. In conventional E-PIE first statement of a group is being chosen as the target statement. This paper proposes another marking target statement strategy in which last statement is being chosen from every group to compute testability. Experimental results show that the proposed strategy provides better group testability as compared to the existing marking statement strategy. © 2014 IEEE.

Purwar A.,JIIT Noida | Singh S.K.,JIIT Noida
Journal of Intelligent Systems | Year: 2016

The quality of data is an important task in the data mining. The validity of mining algorithms is reduced if data is not of good quality. The quality of data can be assessed in terms of missing values (MV) as well as noise present in the data set. Various imputation techniques have been studied in MV study, but little attention has been given on noise in earlier work. Moreover, to the best of knowledge, no one has used density-based spatial clustering of applications with noise (DBSCAN) clustering for MV imputation. This paper proposes a novel technique density-based imputation (DBSCANI) built on density-based clustering to deal with incomplete values in the presence of noise. Density-based clustering algorithm proposed by Kriegal groups the objects according to their density in spatial data bases. The high-density regions are known as clusters, and the low-density regions refer to the noise objects in the data set. A lot of experiments have been performed on the Iris data set from life science domain and Jain's (2D) data set from shape data sets. The performance of the proposed method is evaluated using root mean square error (RMSE) as well as it is compared with existing K-means imputation (KMI). Results show that our method is more noise resistant than KMI on data sets used under study. © 2016 by De Gruyter.

Asawa K.,JIIT Noida | Vardhan R.,JIIT Noida
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering | Year: 2013

This paper aims at defining a real-time probabilistic model for user's mood in its dialect with a software agent, which has a long-term goal of counseling the user in the domain of "coping with exam pressure". We propose a new approach based on Hidden Markov Models (HMMs) to describe the differences in the sequence of emotions expressed due to different moods experienced by users. During real time operation, each user move is passed on to a vocal affect recognizer. The decisions from the recognizer about the kind of emotion expressed are then mapped into code-words to generate a sequence of discrete symbols for HMM models of each mood. We train and test the system using corpora of the temporal sequences of tagged emotional utterances by six male and six female adult Indians in English and Hindi language. Our system achieved an average f-measure rating for all moods of approximately 78.33%. © 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

Aneja S.,JIIT Noida | Lal S.,JIIT Noida
Proceedings of 2014 3rd International Conference on Parallel, Distributed and Grid Computing, PDGC 2014 | Year: 2014

Asthma is a lung disease caused by the inflammation and narrowing of the airways that causes recurrent attacks of breathlessness and wheezing, and often can be life-threatening. Around 15-20 million people are suffering from asthma in India[1]. This paper aims at analyzing various data mining techniques for the prediction of asthma. The observations show that the fusion approach of naive bayes and neural network proved to be the best among classification algorithms in the diagnosis of asthma. This methodology is evaluated using 1024 raw data obtained from a city hospital. The proposed approach helps patients in their diagnosis of asthma. © 2014 IEEE.

Purwar A.,JIIT Noida | Singh S.K.,JIIT Noida
Expert Systems with Applications | Year: 2015

Accurate prediction in the presence of large number of missing values in the data set has always been a challenging problem. Most of hybrid models to address this challenge have either deleted the missing instances from the data set (popularly known as case deletion) or have used some default way to fill the missing values. This paper, presents a novel hybrid prediction model with missing value imputation (HPM-MI) that analyze various imputation techniques using simple K-means clustering and apply the best one to a data set. The proposed hybrid model is the first one to use combination of K-means clustering with Multilayer Perceptron. K-means clustering is also used to validate class labels of given data (incorrectly classified instances are deleted i.e. pattern extracted from original data) before applying classifier. The proposed system has significantly improved data quality by use of best imputation technique after quantitative analysis of eleven imputation approaches. The efficiency of proposed model as predictive classification system is investigated on three benchmark medical data sets namely Pima Indians Diabetes, Wisconsin Breast Cancer, and Hepatitis from the UCI Repository of Machine Learning. In addition to accuracy, sensitivity, specificity; kappa statistics and the area under ROC are also computed. The experimental results show HPM-MI has produced accuracy, sensitivity, specificity, kappa and ROC as 99.82%, 100%, 99.74%, 0.996 and 1.0 respectively for Pima Indian Diabetes data set, 99.39%, 99.31%, 99.54%, 0.986, and 1.0 respectively for breast cancer data set and 99.08%, 100%, 96.55%, 0.978 and 0.99 respectively for Hepatitis data set. Results are best in comparison with existing methods. Further, the performance of our model is measured and analyzed as function of missing rate and train-test ratio using 2D synthetic data set and Wisconsin Diagnostics Breast Cancer Data Sets. Results are promising and therefore the proposed model will be very useful in prediction for medical domain especially when numbers of missing value are large in the data set. © 2015 Elsevier Ltd. All rights reserved.

Lal S.,JIIT Noida | Sureka A.,ABB
ACM International Conference Proceeding Series | Year: 2016

Software logging is an important software development practice which is used to trace important software execution points. This execution information can provide important insight to developer while software debugging. Inspite of many benefits logging is often done in an ad-hoc manner based only on knowledge and experience of software developer because of lack of formal guidelines and training required for making strategic logging decision. It is known that appropriate logging is beneficial for developers but inappropriate logging can have adverse effect on the system. Excessive logging can not only cause performance and cost overhead, it can also lessen the benefit of logging by producing tons of useless logs. Sparse logging can make logging ineffective by leaving out important information. In order to lessen the load of software developers and to improve the quality of software logging, in this work we propose 'LogOpt' tool to help developers in making informed logging decision. LogOpt uses static features from source code to make catch block logging decision. LogOpt is a machine learning based framework which learns the characteristics of logged and unlogged training instance to make informed logging decision. We manually analyze snippets of logged and unlogged source code and extracted 46 distinguishing features important in making logging decision. We evaluated LogOpt on two large open source projects Apache Tomcat and CloudStack (nearly 1.41M LOC). Results show that LogOpt is effective for automated logging task. © 2016 ACM.

Kwatra P.,JIIT Noida
2013 International Conference on Signal Processing and Communication, ICSC 2013 | Year: 2013

In this work, we aim at exploring new Go-back-N protocols for underwater communications. Beyond the literature survey on various ARQ protocols in propagation delay intensive environments, we have studied the degradation in the channel parameters. Then the new variable window go back N protocol has been proposed and implemented specially for underwater communications. Quantification of probability of error has also been done to a small extent and results compared with the practical scenario. © 2013 IEEE.

3rd International Conference on Signal Processing and Integrated Networks, SPIN 2016 | Year: 2016

Orthogonal Frequency Division Multiplexing (OFDM) which is widely used transmission technique for all 4G communication systems faces a major issue of Peak to average power ratio (PAPR). Partial Transmit Sequence (PTS) is the most preferred technique for the reduction of PAPR. But it involves complex searching algorithms for finding the most optimal combinations of OFDM signals. Increased complexity with any increase in the number of sub-blocks is a major drawback of PTS. In this paper, Iterative-Grouping and image-PTS (IGI-PTS) technique is proposed which mainly focuses on reducing the computational complexity involved in search of optimal phase factors. It is combination of two basic grouping and imaging techniques and further using iterations to simplify the searching process when the numbers of sub-blocks are in significantly high. © 2016 IEEE.

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