Gandhi Institute for Technological Advancement

Bhubaneshwar, India

Gandhi Institute for Technological Advancement

Bhubaneshwar, India
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Panda M.,Gandhi Institute for Technological Advancement | Abraham A.,Machine Intellegence Reseacrh Labs. MIR Labs. | Patra M.R.,Berhampur University
Procedia Engineering | Year: 2012

Intrusion detection is an emerging area of research in the computer security and networks with the growing usage of internet in everyday life. Most intrusion detection systems (IDSs) mostly use a single classifier algorithm to classify the network traffic data as normal behaviour or anomalous. However, these single classifier systems fail to provide the best possible attack detection rate with low false alarm rate. In this paper, we propose to use a hybrid intelligent approach using combination of classifiers in order to make the decision intelligently, so that the overall performance of the resultant model is enhanced. The general procedure in this is to follow the supervised or un-supervised data filtering with classifier or clusterer first on the whole training dataset and then the output is applied to another classifier to classify the data. We use 2-class classification strategy along with 10-fold cross validation method to produce the final classification results in terms of normal or intrusion. Experimental results on NSL-KDD dataset, an improved version of KDDCup 1999 dataset show that our proposed approach is efficient with high detection rate and low false alarm rate.


Muduli N.,Gandhi Engineering College | Palai G.,Gandhi Institute for Technological Advancement | Tripathy S.K.,National Institute of Science and Technology
Optik | Year: 2014

The effect of ellipticity on birefringence in a hexagonal photonic crystal fiber having elliptical air holes with Kerr nonlinearity is investigated, with and without defect using finite difference time domain (FDTD) simulations. It is found that the birefringence increases with the increase of ellipticity. Further this analysis is extended to a double defect structure, where two adjacent air holes are omitted horizontally from the hexagonal structure. This double defect structure is found to have more birefringence than the structure without defect. It is raveled that birefringence due to no defect is more for lower value of ellipticity; however at higher value of ellipticity, birefringence due to double defect is more than the one that could be due to no defect. © 2014 Elsevier GmbH. All rights reserved.


Nayak S.K.,Centurion University of Technology and Management | Padhy S.K.,Siksha ‘O’ Anusandhan University | Panigrahi S.P.,Gandhi Institute for Technological Advancement
Future Generation Computer Systems | Year: 2012

This paper deals with the problem of dynamic task scheduling in grid environment of multi-processors. First, this paper formulates task scheduling as an optimization problem and then optimizes with a novel hybrid optimization algorithm. The proposed algorithm combines the merits of Genetic Algorithm and Bacteria Foraging optimization. The simulation result proves the superior performance with the proposed algorithm. © 2011 Elsevier B.V. All rights reserved.


Samanta C.K.,BIET | Padhy S.K.,Siksha ‘O’ Anusandhan University | Panigrahi S.P.,Gandhi Institute for Technological Advancement | Panigrahi B.K.,Indian Institute of Technology Delhi
IET Electrical Systems in Transportation | Year: 2013

This study deals with energy management (EM) in hybrid electric vehicles. This study designs EM as an optimisation problem, then, optimises it using particle swarm optimisation (PSO) and some of its hybridisations. This study will be first in the literature to introduce PSO to the problem of EM in electric field. Moreover, this study proposes some novel applications of hybrid PSO, such as PSO-DE and PSO-QI. Encouraging simulation results obtained in this study that may attract for a case study for practical implementations. © The Institution of Engineering and Technology 2013.


Mohapatra S.K.,Siksha ‘O’ Anusandhan University | Pradhan M.,Gandhi Institute for Technological Advancement
IEEE International Conference on Computer Communication and Control, IC4 2015 | Year: 2015

Software testing is one of the important stages of software development. In software development, developers always depend on testing to reveal bugs. In the maintenance stage test suite size grow because of integration of new technique. Addition of new technique force to create new test case which increase the size of test suite. In regression testing new test case may be added to the test suite during the whole testing process. These additions of test cases create possibility of presence of redundant test cases. Due to limitation of time and resource, reduction techniques should be used to identify and remove them. Research shows that a subset of the test case in a suit may still satisfy all the test objectives which is called as representative set. Redundant test case increase the execution cost of the test suite, in spite of NP-completeness of the problem there are few good reduction techniques have been available. In this paper a new approach for test case reduction is proposed. This algorithm use genetic algorithm technique iteratively with varying chromosome length to reduce test case in a test suit by finding representative set of test cases that are fulfill the testing criteria. © 2015 IEEE.


Mohapatra S.K.,Siksha ‘O’ Anusandhan University | Prasad S.,Gandhi Institute for Technological Advancement
2014 International Conference on Computing for Sustainable Global Development, INDIACom 2014 | Year: 2014

Software testing is one of the important stages of software development. In software development, developers always depend on testing to reveal bugs. In the maintenance stage test suite size grow because of integration of new technique. Addition of new technique force to create new test case which increase the size of test suite. In regression testing new test case may be added to the test suite during the whole testing process. These additions of test cases create possibility of presence of redundant test cases. Due to limitation of time and resource, reduction techniques should be used to identify and remove them. Research shows that a subset of the test case in a suit may still satisfy all the test objectives which is called as representative set. Redundant test case increase the execution cost of the test suite, in spite of NP-completeness of the problem there are few good reduction techniques have been available. In this paper a new approach for test case reduction is proposed. This algorithm use genetic algorithm technique iteratively with varying chromosome length to reduce test case in a test suit by finding representative set of test cases that are fulfill the testing criteria. © 2014 IEEE.


Panda M.,Gandhi Institute for Technological Advancement
Sensors and Transducers | Year: 2011

The enormous growth of wireless sensor network (WSN) research has opined challenges about their ease in implementation and performance evaluation. Efficient swarm intelligence based routing protocols that can be used to obtain the application specific service guarantee are the key design issues in designing a WSN model. In this paper, an experimental testbed is designed with 100 sensor nodes deployed in a dense environment to address the scalability and performance issues of WSN. In this paper, we use Flooded Piggyback (FP) and SC-MCBR ant colony based routing along with AODV and MCBR Tree in order to design an efficient WSN model. Finally, simulation results are presented with various performance measures to understand the efficacy of the proposed WSN design. © 2011 IFSA.


Dash S.K.,Gandhi Institute for Technological Advancement | Mohanty S.,Gandhi Institute for Technological Advancement
2nd International Conference on Electronics and Communication Systems, ICECS 2015 | Year: 2015

An ideal multi-objective optimization method for economic emission load dispatch (EELD) with non-linear fuel cost and emission level functions in power system operation is presented. In this paper, the problem treats economy, emission, and transmission line security as vital objectives. The load constraints and operating constraints are taken into account. Assuming goals for individual objective functions, the multi-objective problem is converted into a unique-objective optimization by the goal-attainment method, which is then taken care of by the simulated annealing (SA) technique. The solution can offer a best compromising solution in a sense close to the requirements of the system designer. Results for 30-bus, 57-bus, 118-bus IEEE test case system have been utilized to demonstrate the applicability and authenticity of the proposed method. © 2015 IEEE.


Dash S.K.,Gandhi Institute for Technological Advancement | Panda C.K.,Gandhi Institute for Technological Advancement
2nd International Conference on Electronics and Communication Systems, ICECS 2015 | Year: 2015

With the advent of stochastic search algorithms, the simulated annealing [2] and the genetic algorithms [5] were devoted to solving the highly non-linear economic dispatch problems without restrictions to the shape of fuel cost functions. Yang et al [6], have developed an efficient general economic dispatch algorithm for units with non-smooth fuel cost functions based on EP technique. In this work the authors have compared the results of ED problems when solved by genetic algorithm, simulated annealing and EP. They have shown that the EP method is able to give a cheaper schedule at a less computation time. Hanzhenget al [7], described a solution method for unit commitment using Lagrangian relaxation combined with evolutionary programming. Hotaet al [8], have developed an evolutionary programming based algorithm for solution of short-term hydrothermal scheduling problem. They have also shown that when compared to simulated annealing based algorithm for short-term hydrothermal scheduling, EP based algorithm is able to obtain a cheaper hydrothermal schedule at reduced execution time. In this paper, a novel evolutionary programming (EP) based neuro-fuzzy technique is proposed to solve the multi-objective generation dispatch problem with non-smooth characteristic functions i.e., fuel cost and emission level functions. The stochastic mechanics, which combine offspring creation based on the performance of current trial solutions and competition & selection based on successive generations, form a considerably robust scheme for large-scale real-valued combinatorial optimization. The weaknesses of the algorithms mentioned above are circumvented. The proposed EP approach is capable of not only solving the multi-objective generation dispatch problem with any type of fuel cost and emission level functions, analytical or empirical curves, but also obtaining the global or near global minimum solution considering transmission losses within the reasonable execution time. Encoding and decoding schemes essential in the genetic algorithm approach are not needed; considerable computation time can thus be saved. © 2015 IEEE.


Mohapatra S.K.,Siksha ‘O’ Anusandhan University | Prasad S.,Gandhi Institute for Technological Advancement
Proceedings - 2013 International Conference on Machine Intelligence Research and Advancement, ICMIRA 2013 | Year: 2014

To improve the effectiveness of certain performance goals, test case prioritization techniques are used. These technique schedule the test cases in particular order for execution so as to increase the efficacy in meeting the performance goals. For every change in the program it is considered inefficient to re-execute each and every test case. Test case prioritization techniques arrange the test cases within a test suite in such a way that the most important test case is executed first. This process enhances the effectiveness of testing. This algorithm during time constraint execution has been shown to have detected maximum number fault while including the sever test cases. © 2013 IEEE.

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