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Deepa Shenoy, P.
- Dynamic Traffic Splitting in a Multi-Radio Multi-Hop Network
Abstract Views :328 |
PDF Views:2
Authors
K. Kiran
1,
Nazima Begum
1,
R. Ramya
1,
Abhishek Alfred Singh
1,
P. Deepa Shenoy
1,
K. R. Venugopal
2,
Lalit M. Patnaik
3
Affiliations
1 Department of CSE, University Visvesvaraya College of Engineering, Bangalore, IN
2 University Visvesvaraya College of Engineering, Bangalore, IN
3 Indian Institute of Science, Bangalore, IN
1 Department of CSE, University Visvesvaraya College of Engineering, Bangalore, IN
2 University Visvesvaraya College of Engineering, Bangalore, IN
3 Indian Institute of Science, Bangalore, IN
Source
Networking and Communication Engineering, Vol 6, No 3 (2014), Pagination: 85-92Abstract
WiFi and WiMAX are two popular wireless standards, where WiFi is a wireless LAN standard and WiMAX is a wireless MAN standard. WiFi (IEEE 802.11) provides coverage of few hundred feet whereas WiMAX (IEEE 802.16) covers a range of upto 40 miles. Nowadays multiple radios are equipped within a single device. One way to achieve high performance in a network consisting of such multi-radio devices is by splitting traffic over the multiple radios. Research work has shown that traffic splitting has a positive impact on network throughput. In this work, our objective is to split the data traffic dynamically in an ad-hoc network with such hybrid nodes equipped with WiFi and WiMAX radios. We have used Bee-Hive Routing Protocol to make routing decisions in the network and analyze the effect of traffic splitting on network throughput.Keywords
Bee-Hive Routing Protocol, Dynamic Traffic Splitting, Hybrid Node, LAN, MAN, WiFi, WiMAX.
- Ensemble PHOG and SIFT Features Extraction Techniques to Classify High Resolution Satellite Images
Abstract Views :323 |
PDF Views:2
Authors
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
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
Source
Data Mining and Knowledge Engineering, Vol 6, No 5 (2014), Pagination: 199-206Abstract
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.- Cancer Prognosis Prediction Model Using Data Mining Techniques
Abstract Views :374 |
PDF Views:2
Authors
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
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
Source
Data Mining and Knowledge Engineering, Vol 6, No 1 (2014), Pagination: 21-29Abstract
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.- FKP Biometrics for Human Authentication
Abstract Views :278 |
PDF Views:2
Authors
Affiliations
1 RNS Institute of Technology, Bangalore, IN
2 University Visveswaraya College of Engineering, Bangalore University, Bangalore, IN
3 DIAT, Pune, IN
1 RNS Institute of Technology, Bangalore, IN
2 University Visveswaraya College of Engineering, Bangalore University, Bangalore, IN
3 DIAT, Pune, IN
Source
Biometrics and Bioinformatics, Vol 3, No 5 (2011), Pagination: 238-244Abstract
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.- A Brief Survey on Privacy Preserving Data Mining Techniques
Abstract Views :548 |
PDF Views:2
Authors
Affiliations
1 Visvesvaraya Technological University, Belagavi-590 018, IN
2 University Visvesvaraya College of Engineering, Bangalore University, Bangalore, IN
3 BMS College of Engineering, Bangalore-560019, IN
1 Visvesvaraya Technological University, Belagavi-590 018, IN
2 University Visvesvaraya College of Engineering, Bangalore University, Bangalore, IN
3 BMS College of Engineering, Bangalore-560019, IN
Source
Data Mining and Knowledge Engineering, Vol 8, No 9 (2016), Pagination: 267-273Abstract
With the onset of the digital revolution, organizations are increasingly maintaining a huge amount of information on their databases and use data mining tools to extract useful information for their business intelligence. The problem with the availability of the digital information is the scarce privacy leakage. In many business domains, leakage of personal information of the client either directly or through data mining tools can lead to loss of competitive edge of the company, loss of revenue and customer churn. Companies are pushing for encryption and other data transformation methods to keep the data private. But mining tools which invoke algorithms like clustering, classification etc. may not work properly on the transformed data. In this paper, we analyze the privacy preserving data mining solutions and privacy leakage in them through indirect means. The main objective of this paper is to identify the open areas of research on privacy-preserving data mining.
Keywords
Transformation Strategy, Privacy Preserving Data Mining, Cryptography, Wavelet Transformation, Correlation Analysis.- PTMIBSS:Profiling Top Most Influential Blogger Using Synonym Substitution Approach
Abstract Views :453 |
PDF Views:6
Authors
G. U. Vasanthakumar
1,
R. Priyanka
1,
K. C. Vanitha Raj
1,
S. Bhavani
1,
B. R. Asha Rani
1,
P. Deepa Shenoy
1,
K. R. Venugopal
1
Affiliations
1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, IN
1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 2 (2017), Pagination: 1408-1420Abstract
Users of Online Social Network (OSN) communicate with each other, exchange information and spread rapidly influencing others in the network for taking various decisions. Blog sites allow their users to create and publish thoughts on various topics of their interest in the form of blogs/blog documents, catching the attention and letting readers to perform various activities on them. Based on the content of the blog documents posted by the user, they become popular. In this work, a novel method to profile Top Most Influential Blogger (TMIB) is proposed based on content analysis. Content of blog documents of bloggers under consideration in the blog network are compared and analyzed. Term Frequency and Inverse Document Frequency (TF-IDF) of blog documents under consideration are obtained and their Cosine Similarity score is computed. Synonyms are substituted against those unmatched keywords if the Cosine Similarity score so computed is below the threshold and an improved Cosine Similarity score of those documents under consideration is obtained. Computing the Influence Score after Synonym substitution (ISaS) of those bloggers under conflict, the top most influential blogger is profiled. The simulation results demonstrate that the proposed Profiling Top Most Influential Blogger using Synonym Substitution (PTMIBSS) algorithm is adequately accurate in determining the top most influential blogger at any instant of time considered.Keywords
Blog Document, Content Analysis, Cosine Similarity Score, Influential Blogger, Profiling.References
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