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Rautray R.,Siksha ‘O’ Anusandhan University | Balabantaray R.C.,IIIT
Physica A: Statistical Mechanics and its Applications | Year: 2017

Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it is categorized as single document or multi document summarization. Rather than single document, multi document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in the study. © 2017


Kumar N.,IIIT | Mathuria A.,DAIICT
ACM International Conference Proceeding Series | Year: 2017

In secure data outsourcing, even a single unauthorized write operation may heavily ruin the data owner's business operations. Another potential issue is that the untrusted service provider may return stale data to mislead the readers. Existing work on secure write access does not consider misbehavior by users with write authorization to the outsourced data Vles. The data owner may not wish to allow such users to modify their own written data Vles after a Vxed amount of time. The other type of misbehavior is that a user with revoked access to a resource can modify corresponding latest versions of the resource written by him in collusion with the service provider. Also, the weak freshness guarantee (i.e., the < k, t >-staleness) is not appropriate in many time-sensitive applications. In this work, we address the misbehavior by users who attempt to modify the own written data Vles. Such misbehavior is detected by the data owner using an audit-based mechanism. The viability of the proposed mechanism is veriVed by implementing the proposed protocols in Microsoft's Azure platform. A strong freshness guarantee is addressed using proof messages from the service provider so that a read request will return the latest version of the data Vle at least until the time when the data Vle is sent from the service provider to a reader. © 2017 ACM.


Mohapatra P.,IIIT | Chakravarty S.,Orissa Engineering College, Bhubaneswar | Dash P.K.,Siksha ‘O’ Anusandhan University
Swarm and Evolutionary Computation | Year: 2016

Microarray gene expression based medical data classification has remained as one of the most challenging research areas in the field of bioinformatics, machine learning and pattern classification. This paper proposes two variations of kernel ridge regression (KRR), namely wavelet kernel ridge regression (WKRR) and radial basis kernel ridge regression (RKRR) for classification of microarray medical datasets. Microarray medical datasets contain irrelevant and redundant genes which cause high number of gene expression i.e. dimensionality and small sample sizes. To overcome the curse of dimensionality of the microarray datasets, modified cat swarm optimization (MCSO), a naturally inspired evolutionary algorithm, is used to select the most relevant features from the datasets. The adequacies of the classifiers are demonstrated by employing four from each binary and multi-class microarray medical datasets. Breast cancer, prostate cancer, colon tumor, leukemia datasets belong to the former and leukemia1, leukemia2, SRBCT, brain tumor1 to the latter. A number of useful performance evaluation measures including accuracy, sensitivity, specificity, confusion matrix, Gmean, F-score and the area under the receiver operating characteristic (ROC) curve are considered to examine the efficacy of the model. Other models like simple ridge regression (RR), online sequential ridge regression (OSRR), support vector machine radial basis function (SVMRBF), support vector machine polynomial (SVMPoly) and random forest are studied and analyzed for comparison. The experimental results demonstrate that KRR outperforms other models irrespective of the datasets and WKRR produces better results as compared to RKRR. Finally, when the results are compared on the basis of binary and multi-class datasets, it is found that binary class yields a little bit better result as compared to the multiclass irrespective of models. © 2016 Elsevier B.V.


Mohapatra P.,IIIT | Chakravarty S.,Orissa Engineering College, Bhubaneswar | Dash P.K.,Siksha ‘O’ Anusandhan University
Swarm and Evolutionary Computation | Year: 2015

Machine learning techniques are being increasingly used for detection and diagnosis of diseases for its accuracy and efficiency in pattern classification. In this paper, improved cuckoo search based extreme learning machine (ICSELM) is proposed to classify binary medical datasets. Extreme learning machine (ELM) is widely used as a learning algorithm for training single layer feed forward neural networks (SLFN) in the field of classification. However, to make the model more stable, an evolutionary algorithm improved cuckoo search (ICS) is used to pre-train ELM by selecting the input weights and hidden biases. Like ELM, Moore-Penrose (MP) generalized inverse is used in ICSELM to analytically determines the output weights. To evaluate the effectiveness of the proposed model, four benchmark datasets, i.e. Breast Cancer, Diabetes, Bupa and Hepatitis from the UCI Repository of Machine Learning are used. A number of useful performance evaluation measures including accuracy, sensitivity, specificity, confusion matrix, Gmean, F-score and norm of the output weights as well as the area under the receiver operating characteristic (ROC) curve are computed. The results are analyzed and compared with both ELM based models like ELM, on-line sequential extreme learning algorithm (OSELM), CSELM and other artificial neural networks i.e. multi-layered perceptron (MLP), MLPCS, MLPICS and radial basis function neural network (RBFNN), RBFNNCS, RBFNNICS. The experimental results demonstrate that the ICSELM model outperforms other models. © 2015 Elsevier B.V.


Kar A.,IIIT | Chandra M.,BIT Mesra
International Journal of Computational Vision and Robotics | Year: 2013

The structural complexity and overall performance of the adaptive filter depend on its structure. The number of taps is one of the most important structural parameters of the liner adaptive filter. In practice the system length is not known a priori and has to be estimated from the knowledge of the input and output signals. In a system identification framework the tap-length estimation algorithm automatically adapts the filter order to the suitable optimum value which makes the variable order adaptive filter a best identifier of the unknown plant. In this paper an improved pseudo-fractional tap-length selection algorithm is proposed to find out the optimum tap-length which best balances the complexity and steady state performance. The performance analysis is presented to formulate steady state tap-length in correspondence with the proposed algorithm. Simulations and results are provided to observe the analysis and to make a comparison with the existing tap-length learning methods. Copyright © 2013 Inderscience Enterprises Ltd.


Sinha N.,IIIT | Babu R.V.,Indian Institute of Science
Proceedings - International Conference on Image Processing, ICIP | Year: 2012

Automatic eye screening for conditions like diabetic retinopathy critically hinges on detection and localization of Optic disk (OD). In this paper, we present a novel scale-embedded dictionary-based method that poses the problem of OD localization as that of classification, carried out in sparse representation framework. A dictionary is created with manually marked fixed-sized sub-images that contain OD at the center, for multiple scales. For a given test image, all subimages are sparsely represented as a linear combination of OD dictionary elements. A confidence measure indicating the likelihood of the presence of OD is obtained from these coefficients. Red channel and gray intensity images are processed independently, and their respective confidence measures are fused to form a confidence map. A blob detector is run on the confidence map, whose peak response is considered to be at the location of the OD. The proposed method is evaluated on publicly available databases such as DIARETDB0, DIARETDB1 and DRIVE. The OD was correctly localized in 253 out of 259 images, with an average computation time of 3.8 seconds/image and accuracy of 97.6%. Comparisons with two existing techniques are also discussed. © 2012 IEEE.


The structure of an adaptive time varying linear filter largely depends on its tap-length and the delay units connected to it. The no of taps is one of the most important structural parameters of the liner adaptive filter. Determining the system order or length is not a trivial task. Fixing the tap-length at a fixed value sometimes results in unavoidable issues with the adaptive design like insufficient modeling and adaptation noise. On the other hand a dynamic tap-length adaptation algorithm automatically finds the optimum order of the adaptive filter to have a tradeoff between the convergence and steady state error. It is always difficult to get satisfactory performance in high noise environment employing an adaptive filter for any identification problem. High noise decreases the Signal to noise ratio and sometimes creates wandering issues. In this chapter an improved pseudo-fractional tap-length selection algorithm has been proposed and analyzed to find out the optimum tap-length which best balances the complexity and steady state performance specifically in high noise environment. A steady-state performance analysis has been presented to formulate the steady state tap-length in correspondence with the proposed algorithm. Simulations and results are provided to observe the analysis and to make a comparison with the existing tap-length learning methods. © Springer International Publishing Switzerland 2014.


Singh V.P.,Babu Banarsi das National Institute of Technology and Management | Mohanty S.R.,Motilal Nehru National Institute of Technology | Kishor N.,Motilal Nehru National Institute of Technology | Ray P.K.,IIIT
International Journal of Electrical Power and Energy Systems | Year: 2013

This paper presents a study on isolated hybrid distributed generation (DG) system for improving the frequency deviation profile. The hybrid DG system consists of wind turbine generator (WTG), diesel engine generator (DEG), aqua-electrolyzer (AE), fuel cell (FC) along with energy storage units. The frequency control problem is addressed for DG system connected with superconducting magnetic energy storage (SMES) or ultra-capacitor (UC). The particle swarm optimization (PSO) based loop shaping of H-infinity controller is used and compared with those obtained by genetic algorithm (GA) to minimize the frequency deviation. The frequency stabilizing performance is analyzed under different disturbances. Also, the controller robustness in terms of system parameter uncertainties is tested for changes in parameter up to ±30% from its nominal value. The results demonstrate minimum frequency deviation as achieved by proposed controller with use of UC in hybrid DG system. © 2012 Elsevier Ltd. All rights reserved.


Saikia G.,IIIT
2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014 | Year: 2014

Functionalities of the hand of the iCub simulator have been extended so as to make the simulator robot make grasp forms like a human hand. This is achieved by sending signal for one of the six common types of human hand grasps, viz. power, pinch, precision, hook, oblique and palm-up from a server to a kinematic solver which then calculates the joint angles for each finger of the robot and feeds these results to a motion controller which finally moves the finger actuators to form the grasp. A geometric approach to solve the manipulator kinematics is used owing to almost planar nature of the finger manipulators of the iCub hand and small number of independent joint variables. This © 2014 IEEE.


Sahoo H.K.,IIIT | Dash P.K.,Soa University | Rath N.P.,VSSUT
Applied Soft Computing Journal | Year: 2013

This paper proposes NARX (nonlinear autoregressive model with exogenous input) model structures with functional expansion of input patterns by using low complexity ANN (artificial neural network) for nonlinear system identification. Chebyshev polynomials, Legendre polynomials, trigonometric expansions using sine and cosine functions as well as wavelet basis functions are used for the functional expansion of input patterns. The past input and output samples are modeled as a nonlinear NARX process and robust H∞ filter is proposed as the learning algorithm for the neural network to identify the unknown plants. H∞ filtering approach is based on the state space modeling of model parameters and evaluation of Jacobian matrices. This approach is the robustification of Kalman filter which exhibits robust characteristics and fast convergence properties. Comparison results for different nonlinear dynamic plants with forgetting factor recursive least square (FFRLS) and extended Kalman filter (EKF) algorithms demonstrate the effectiveness of the proposed approach. © 2013 Elsevier B.V. All rights reserved.

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