R. Pradeepa

Periyar University

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Co-Authors

Valentin Félix

Groupe de Recherches sur l'Energétique des Milieux Ionisés

S Immaculate Joy

Saveetha University

A. Kumaravel

Bharath University

Polamreddy Mohan

Saveetha University

R. Jayaraman

Periyar University

M Pushpalatha

G Hareesh

Naval Physical & Oceanographic Laboratory

K. Vimala Devi

Vellore Institute of Technology University

T. Ananda Babu

Andhra University

M. Pushpalatha

M S Ramaiah University of Applied Sciences

Cooperative Institutions

Naval Physical & Oceanographic Laboratory

SRM Institute of Science and Technology

National Institute of Technology Calicut

Indian Institute of Technology Palakkad

Indian Institute of Technology Madras

Cochin University of Science and Technology

Vellore Institute of Technology University

Defence Research and Development Organisation

Madurai Medical College

Anna University, Chennai

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Effectiveness of Testcase Prioritization using APFD Metric: Survey

R. Pradeepa K. Vimala Devi

Test case prioritization techniques involve scheduling over test cases in an order that improves the performance of regression testing. It is inefficient to re execute every test cases for every program function if once change occurs. Test case prioritization is to be scheduled based on higher priority than lower priority to meet some performance goal (i.e. increase in the effectiveness of testing). The performance goals are 1. rate of fault detection (how quickly faults are detected) 2. Rate of code coverage at fastest rate,3) Rate of increase of confidence in reliability during the testing process to improve the software quality. The problem of test case selection can be solved by prioritizing the test case. The main aim of my paper is to determine the effectiveness of prioritized and nonprioritized test case with the help of APFD(Average Percentage Faults Detected).

Regression testing

Prioritization

Risk-based testing

Test Management Approach

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Citations (8)

Artificial Neural Network (ANN) Based DDoS Attack Detection Model on Software Defined Networking (SDN)

International Journal of Recent Technology and Engineering (IJRTE) (2019)

R. Pradeepa M Pushpalatha

Software Defined Networking (SDN) is a modern emerging technology in networking. The great advantage of this network is, decoupling of the carrier plane and the control plane as well as which provides centralized control. A Controller is the intelligent part of SDN. It offers several benefits such as network programmability, dynamic computing, and cost-effective, high bandwidth. However, SDN has many security issues. The DDoS attack on SDN is a significant issue, and various proposals have been proposed for the detection and prevention of attacks. The main objective of this proposal is to detect DDoS attacks with the help of SDN techniques. In this proposal, a deep learning based Artificial Neural Network (ANN) model is used to detect the DDoS attacks. This can reduce learning time as well as detection time. To evaluate our model we use different machine learning algorithms and deep learning algorithm with different optimizers to train the network traffic which is generated in Mininet emulator and evaluates the results by various metrics such as detection rate, accuracy score, and confusion matrix with classification report. The result shows less detection time (4Secs) with a high accuracy score of 92% in our proposed Artificial Neural Network (ANN) model.

Forwarding plane

10.35940/ijrte.b3670.078219

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Comparative Study of Multiclass Classifiers for Underwater Target Classification

T. Ananda Babu R. Pradeepa

Underwater target classification is a complex task, due to the difficulty in identifying non-overlapping and stable feature set. Moreover, choosing the discriminating algorithm for classification using these features is highly demanding. It is required to choose the right approach and the technique, or the best combinations of techniques from a large set of options available in the literature for the specific problem. The paper addresses this issue by comparing different approaches and techniques for multiclass classification using a particular feature derived from the real data sets. A number of performance metrices are used to compare the performance.

Multiclass classification

Feature (linguistics)

Statistical classification

Multi-label classification

10.1109/icacc.2013.85

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A comparative study on quiescent beam pattern design techniques

G.R Anupama R. Pradeepa Valentin Félix

This paper discusses a number of beam pattern design algorithms for non adaptive beamforming, compares them and assesses their performances for different array geometries. Three techniques for designing a beam pattern are considered of which, the Dolph-Chebychev shading belongs to the traditional class and the Griffiths and Tseng's iterative algorithm as well as the Bell and Van Trees' algorithm belong to adaptive array approach. The comparative study brings out the advantages and limitations of these techniques, aiding the selection of an appropriate pattern synthesis algorithm.

Adaptive beamformer

Beam pattern

10.1109/indcon.2012.6420709

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A Survey on Classification of Feature Selection Strategies

International Journal of Trend in Scientific Research and Development (2018)

R. Pradeepa K. Palanivel

Feature selection is an important part of machine learning.The Feature selection refers to the process of reducing the inputs for processing and analysis, or of finding the most meaningful inputs.A related term, feature engineering (or feature extraction), refers to the process of extracting useful information or features from existing data.Mining of particular information related to a concept is done on the basis of the feature of the data.The accessing of these features hence for data retrieval can be termed as the feature extraction mechanism.Different type of feature extraction methods used.In this paper, the different feature selection methodologies are examined in terms of need and method adopted for feature selection.The three types of method are mainly available, such as Shannon's Entropy, Bayesian with K2 Prior and Bayesi Dirichlet with uniform prior (default).The objectives of this survey paper is to identify the existing contribution made by using their above mentioned algorithms and the result obtained.

Feature (linguistics)

10.31142/ijtsrd9632

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IPR: Intelligent Proactive Routing model toward DDoS attack handling in SDN

The Journal of Supercomputing (2021)

R. Pradeepa M. Pushpalatha

Unavailability

Provisioning

10.1007/s11227-021-03750-3

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Citations (6)

EM based GLRT Detector for MIMO Active Sonar using Bistatic Reverberation Model

R. Pradeepa Rubin Jose Peter Lakshmi Narasimhan Theagarajan Arunkumar K.P.G Hareesh

It is known that the performance of sonar is limited by reverberation, especially, in shallow waters. We study the active MIMO (Multiple Input Multiple Output) sonar detection problem in scenarios limited by reverberation. The bistatic reverberation envelop at the hydrophone of the MIMO array is modeled as delayed and attenuated returns of the transmitted signals from the scattering patches at the ocean floor. Based on this bistatic reverberation ocean channel model, we derive a single snapshot generalized likelihood ratio test (GLRT) detector for MIMO sonar. In the proposed detection algorithm, an EM (Expectation-Maximization) based technique is employed for estimating the scattering strength coefficients in the reverberation model. The theoretical limits on the achievable performance of GLRT and the detection threshold are derived for the narrowband case. The performance of the proposed algorithm is compared with other techniques such as covariance matching and matched filtering. Simulations show that the performance of the proposed detector is quite close to the theoretical limit and outperforms other detection techniques.

Narrowband

Target strength

10.1109/oceanslimerick52467.2023.10244593

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SDN enabled SPIN routing protocol for wireless sensor networks

R. Pradeepa M. Pushpalatha

The wireless sensor networks are highly dynamic in nature and their storage, processing and energy resources are limited. For these conditions routing protocols are required to maintain the network with reliable and secure communication. Lot of protocols are designed for routing but none of the protocol is proposed security as a main task. We advocate that Software Defined Network (SDN) enabled SPIN routing protocol through novel design for security and energy efficient wireless sensor network. The feature of SDN such as programmability, abstraction, and openness can enhance security in wireless sensor networks. The hardware independent SDN with TinyOS is designed and implemented, the results concerning memory footprint, energy consumption, PDR and transmission delay latency presented.

Zone Routing Protocol

10.1109/wispnet.2016.7566211

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Citations (10)

Test Case Prioritization Based On Faults

International Journal of Scientific Engineering and Technology (2014)

R. Pradeepa K. Vimala Devi

Regression testing is one of the important process during the development stage, but it is the most critical activities of software development and maintenance. Whenever software is modified, the comparison between the new and old test case has to be done from the first, to avoid unwanted changes. It is impractical to re-execute all the test case if any changes in the program. Due to this time, cost and space is wasted, this problem can be overcome by prioritization. Test Case Prioritization (TCP) is a technique to schedule and execute the test case with higher priority, according to rate of fault detection. Main goal of TCP is 1. to increase the rate of fault detection, 2. to increase the effectiveness of test suites, 3. to decrease the cost of regression testing. The severe faults can be detected on the basis of different prioritization technique as prioritization, non-prioritization, random prioritization and selection prioritization basis with the help of Average Percentage of Fault Detected (APFD) metrics.

Prioritization

Regression testing

Test plan

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Citations (1)

A novel algorithm for intercept sonar signal detector

E. N. Sreedavy R. Pradeepa Valentin Félix

A novel algorithm for detecting intercept signals and acoustic transients is presented in this paper. The purpose of intercept sonar is to detect the active transmissions from ships, submarines and torpedoes. Since the intercept signal parameters are not known apriori, energy detection based method is proposed. In this approach, the signal energy estimate is compared with the background noise energy estimate. The comparison is done using Non-parametric Page Test with an adaptive threshold. The method also gives an estimate of pulse width of the signal received. The detection performance of the scheme is evaluated for various SNRs, by computer simulation for different types of intercept signals.

SIGNAL (programming language)

Detection theory

10.1109/sympol.2009.5664738

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Citations (4)