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Chatterjee R.,NITTTR | Mandal J.K.,Kalyani University
Proceedings of 2016 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2016 | Year: 2016

Confidence Based Assessment (CBA) is a novel technique for learners' assessment that measures the knowledge as well as confidence level. CBA is an integral part of Confidence Based Learning (CBL) that provides input for deficiency diagnosis (DD) of a learner. This paper proposes design and implementation of CBA using xml and web based tools that maps deficiency of a learner in a format suitable for the system. In the proposal a novel two dimensional assessment methodologies has been adopted which comes to be a better technique compared to the existing one dimensional assessments. © 2016 IEEE.

Tuteja A.,Mm University | Gujral R.,Mm University | Thalia S.,NITTTR
ACE 2010 - 2010 International Conference on Advances in Computer Engineering | Year: 2010

Mobile Ad-Hoc networks are highly dynamic networks characterized by the absence of physical infrastructure. Nodes of these networks functions as a routers which discovers and maintains the routes to other nodes in the network. In such networks, nodes are able to move and synchronize with their neighbors. Due to mobility, connections in the network can change dynamically and nodes can be added and removed at any time. In this paper, we are going to compare Mobile Ad-Hoc network routing protocols DSDV, AODV and DSR using network simulator NS2.34. We have compared the performance of three protocols together and individually too. The performance matrix includes PDR (Packet Delivery Ratio), Throughput, End to End Delay, Routing overhead. We are comparing the performance of routing protocols when packet size changes, when time interval between packet sending changes, when mobility of nodes changes. © 2010 IEEE.

Krishna C.R.,NITTTR | Yadav P.S.,NITTTR
Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2014 | Year: 2014

In past few years a rapid growth of interest in Underwater Wireless Sensor Networks (UWSNs) have been observed due to its vital range of scientific exploration like early warning system for natural disasters, ecosystem monitoring, oil drilling, tactical military surveillance, undersea explorations and assisted navigation. Now research orientation is towards solving the issues related to the large scale UWSNs such as reliable transport, routing and localization. In this paper we propose a hybrid localization scheme, which is integration of two different localization schemes. The aim of scheme is to achieve high localization ratio with relatively low localization error. The performance comparison of proposed scheme with integrating schemes will be done via simulation results. © 2014 IEEE.

Kalra M.,NITTTR | Singh S.,Panjab University
Egyptian Informatics Journal | Year: 2015

Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on-demand access to shared pool of resources over Internet in a self-service, dynamically scalable and metered manner. Cloud computing is still in its infancy, so to reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map tasks to appropriate resources that optimize one or more objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes a long time to find an optimal solution. There are no algorithms which may produce optimal solution within polynomial time to solve these problems. In cloud environment, it is preferable to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey and comparative analysis of various scheduling algorithms for cloud and grid environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), and two novel techniques: League Championship Algorithm (LCA) and BAT algorithm. © 2015.

Baraskar S.S.,NITTTR | Banwait S.S.,NITTTR | Laroiya S.C.,NITTTR
Materials and Manufacturing Processes | Year: 2013

Electrical discharge machining (EDM) is a widely used process in manufacturing industries for high-precision machining of all types of conductive materials. Material of any hardness can be machined as long as material can conduct electricity. Proper selection of input parameters is one of the most important aspects in the die sinking EDM, as these conditions determine important characteristics such as surface roughness and material removal rate (MRR). In the present work, empirical models have been developed for relating the surface roughness and MRR to machining parameters like pulse-on time, pulse-off time, and discharge current. Response surface methodology (RSM) has been applied for developing the models using the technique of design of experiments (DOE) and multilinear regression analysis. The developed empirical models are used for optimization. Since the influence of machining parameters on surface roughness and MRR are conflicting in nature, there is no single combination of machining parameters, which provides the best machining performance. A multiobjective optimization method, nondominating sorting genetic algorithm-II, is used to obtain the Pareto-optimal set of solutions. Copyright © 2013 Taylor and Francis Group, LLC.

International Conference on Computing, Communication and Automation, ICCCA 2015 | Year: 2015

Cloud Computing is a major area of research. Nature Inspired Algorithms (NIAs) form the major portion of research going on in the Cloud today. NIAs as the name suggests are the algorithms whose source of inspiration is nature. NIAs can further be classified into algorithms based on Swarm Intelligence (SI), Biological Phenomena (called Bio-inspired BI), Physics and Chemistry systems or based on some other things. SI based algorithms are called intelligent because they are known to learn and improve their performance by observing the output on previous moves made by them. NIAs provide an efficient solution to many real-world optimization problems which are categorized to be NP-Hard Problems. NIAs have a huge list of applications and most of them prove to be more efficient than other algorithms and thus are many a time used in combination to other algorithms in order to improve performance and thus deliver a better QoS. The paper intends to review, classify and briefly describe various NIAs and the principle behind each algorithm so as to inspire further research. The paper also lists the applications of various NIAs. NIAs also find their application in Cluster and Grid Computing. © 2015 IEEE.

Mehra R.,NITTTR | Singh A.,RGGI
Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013 | Year: 2013

Recently received signal strength (RSS)-based distance estimation technique has been proposed as a low complexity, low-cost solution for mobile communication node with minimum RSSI error. After investigating the existing algorithm of location technique, it is observed that the distribution of RSSI-value at each sample point is fluctuant even in the same position due to shadow fading effect. Therefore, here present a novel method for RSSI error reduction in distance estimation using recursive least square (RLS)-algorithm to the existing deterministic algorithms. The proposed method collects RSSI-values from the mobile communication node to build the probability model. Once the probability models are estimated for different standard deviation related to path loss exponent using adaptive filtering in real time, it is possible to accurately determine the distance between the mobile communication node and fixed communication node. From simulation results it is shown, that the accuracy of RSSI-value for mobile communication node in real time distance estimation is improved in changing environments. © 2013 IEEE.

Srivastav N.,NITTTR | Challa R.K.,NITTTR
Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013 | Year: 2013

The threat from spammers, attackers and criminal enterprises has grown with the expansion of Internet, thus, intrusion detection systems (IDS)have become a core component of computer network due to prevalence of such threats. In this paper, we present layered framework integrated with neural network to build an effective intrusion detection system. This system has experimented with Knowledge Discovery & Data Mining(KDD) 1999 dataset. The systems are compared with existing approaches of intrusion detection which either uses neural network or based on layered framework. The results show that the proposed system has high attack detection accuracy and less false alarm rate. © 2013 IEEE.

Bhateja V.,SRMCEM | Devi S.,NITTTR
ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology | Year: 2011

Minute calcium deposits that appear as bright spots on mammograms are known as microcalcifications. Calcifications in ducts and lobules are common markers of malignancy; therefore its detection plays a vital role in early detection of breast cancer. This paper proposes a novel framework for edge detection of microcalcifications using a combination of non-linear enhancement operator and morphological filter. The contrast improvement of the region of interest for better visualization of mammographic abnormalities is obtained using the proposed non-linear operator. This enhancement operator is versatile in approach, producing promising results for dense as well as non dense breast tissues. The enhanced ROI is then convolved with the proposed mask of two stage iterative discrete morphological gradient operator with variable sized structuring elements leading to improved edge detection of microcalcifications. © 2011 IEEE.

Bhateja V.,SRMCEM | Devi S.,NITTTR
ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology | Year: 2011

Breast cancer is often diagnosed at its early stage as a non-palpable suspicious mass on a mammogram that may be cancerous at a later stage. Masses can be seen in both mediolateral and craniocaudal mammographic views. Masses with diameters less than 1 cm are generally benign, while those with diameters above 2 cm require further investigation. This paper presents a novel approach for enhancement of suspicious masses in mammographic images using the proposed nonlinear transformation function. Significant improvement in contrast of masses along with the suppression of background tissues is obtained by tuning the parameters of the proposed transformation function in the specified range. The selection of parameters is almost invariant of the type of background tissues and severity of the abnormality, giving significantly improved results even for denser mammographic images. The applicability of the proposed method is tested on mammograms with circumscribed, ill-defined and spiculated masses. © 2011 IEEE.

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