Informatics Publishing Limited

S. Bharathi ¹, P. Deepa Shenoy ², K. R. Venugopal ³, L. M. Patnaik ⁴

Affiliations
1 Department of MCA, Dr Ambedkar Institute of Technology, Bangalore, IN
2 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, IN
3 University Visvesvaraya College of Engineering, Bangalore University, Bangalore, IN
4 Indian Institute of Science, Bangalore, IN

Abstract

The task of indentifying similar objects within the querying image remains challenging. It is due to viewpoint and lighting changes, deformation and partial occlusions that may exist across different examples. In this framework we focus on combination methods that ensemble multiple descriptors at multiple spatial resolution levels. Ensemble PHOG (Pyramid histogram orientation and gradient) and SIFT (Scale invariant feature transformation) descriptors are used for the feature extraction to achieve the good classification accuracy. Within a region local feature was captured by the distribution over edge orientation, and spatial layout by tiling the image into regions at multiple resolutions. The SIFT features are extracted for each PHOG block. These features are trained using SOM network. Later SVM and Neural network classifiers are used for classification. Results demonstrating the effectiveness of the proposed technique are provided using confusion matrix, transition matrix and other accuracy measures. Area of different land cover regions are calculated, which can be used for land use changes.

Keywords

PHOG, SIFT, Classification, Satellite Image.

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J. S. Saleema ¹, P. Deepa Shenoy ², K. R. Venugopal ³, L. M. Patnaik ⁴

Affiliations
1 Dept. of Comp. Sci., Christ University, Bangalore, IN
2 Department of CSE, University Visvesvaraya College of Engineering, Bangalore, IN
3 University Visvesvaraya College of Engineering, Bangalore, IN
4 Indian Institute of Science, Bangalore, IN

Abstract

Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Disease prognosis is identified at the treatment stage and at the recurrence stage. Conventional cancer prediction method deals only with the survival or mortality of the patients, but not with other labels such as severity of the disease through metastasis or multi-primary, stage, grade, etc. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is twofold. One is to build a prediction model to find the prominent variables by using the standard classifiers and the second is to improve the prediction accuracy through various sampling techniques. The proposed prediction model consist of three phases namely, basic level pre-processing, problem specific processing and modeling classifiers. Problem specific processing phase deals with feature extraction, sampling and response variable selection. The well known classification algorithms (Decision Tree, Naive Bayes and KNN) have been used to model the classifiers for prediction analysis. Apart from the available incident data from SEER (Breast, Colorectal and Respiratory Cancer data) a new mixed combination of the three in equal proportion have been generated for the experimentation. Feature selection through correlation and information gain reduced the attributes to 37 from the raw size of 118. Patient survival, age at diagnosis, stage and multiple primaries in the given order has been identified as the prominent response variable, where as grade performed very low in the experimentation. The performances of various sampling techniques have been studied with the data set size ranging from 500 to 30000 samples for the four prominent labels identified in the previous step. The result shows that the balanced stratified sampling technique always maintains consistency in the performance. Also classifier model with decision tree algorithm optimizes the performance compared to the other algorithms. All the results of the models are tabulated in this paper.

Keywords

Classifier, Pre-Processing, Prognosis Prediction, SEER.

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Chetana Hegde ¹, P. Deepa Shenoy ², K. R. Venugopal ², L. M. Patnaik ³

Affiliations
1 RNS Institute of Technology, Bangalore, IN
2 University Visveswaraya College of Engineering, Bangalore University, Bangalore, IN
3 DIAT, Pune, IN

Abstract

Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like Finger Knuckle Print (FKP) of a person is unique and secure. In this paper, we propose a human authentication system based on FKP image of a person. Depending on the security level required by an organization that implements the proposed system, we provide two modes of security viz. basic mode and advanced mode. In the basic mode, the Radon Transform is applied on pre-processed FKP image and Eigen values are computed. Then we compute the correlation coefficient between the set of Eigen values stored in the database and that of input image to authenticate a person. For advanced level of security, we apply Gabor Wavelet on pre-processed FKP image. The magnitude values of the Gabor Wavelet Transform are computed. Then the correlation coefficient between the set of magnitude values stored in the database and that of input image is used to authenticate a person. For real time implementation, suitable GUI can be developed. The basic mode of security system is found to have FAR as 6.79% and FRR as 0.0517%. The advanced mode has the FAR of about 3.07% and FRR as 1.14%.

Keywords

Correlation Coefficient, Eigen Values, FAR, FRR, Gabor Wavelet Transform, Magnitude Values, Radon Transform, ROC Curve.

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D. Annapurna ¹, K. B. Raja ², K. R. Venugopal ², L. M. Patnaik ³

Affiliations
1 Department of Information Science and Engineering, PES Institute of Technology South Campus, Hosur Road, Bangalore 560100, IN
2 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore 560 001, IN
3 Indian Institute of Science, Bangalore, IN

Abstract

Hybrid protocol combines the advantages of proactive and reactive routing in adhoc network. The routing is initially established with some proactively prospected routes and then serves the demand from additionally activated nodes through reactive flooding. In this paper we propose A Quality Hybrid Service Discovery Protocol (QHSDP) for discovering services. A broadcast mechanism is used to get the service and routing information of the nodes present inside the zone. The routing and service information reduces the packet flooding in the network hence reducing collision and increasing packet delivery efficiency. Reduced control packets in turn reduces the battery power consumption. A query message is bordercasted through the peripheral nodes to the nodes outside the zone. This makes the discovery procedure more sclable, hence increasing the node's coverage and reducing the latency in the proposed technology compared to the existing technology.

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