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webtunix paper publications
Webtunix - Sentimental analysis using fuzzy and naive bayes

Sentimental Analysis is the best way to judge people's opinion regarding a particular post. In this paper we present analysis for sentiment behavior of Twitter data. The proposed work utilizes the naive Bayes and fuzzy Classifier to classify Tweets into positive, negative or neural behavior of a particular person. We present experimental evaluation of our dataset and classification results which proved that combined proposed method is more efficient in terms of Accuracy, Precision and Recall.

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This paper proposed a Clustering approach based routing protocol for VANETs. The proposed algorithm is a distributed clustering algorithm together with OLSR Routing protocol, which possesses excellent Data dissemination rate, where Data dissemination is defined by throughput of protocol.In addition, the algorithm is also perform excellent in terms of End to End delay and exhibits a reasonable overhead. The algorithm is achieved by utilizing a new clustering technique based protocol called OLSR-C (OC).

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Webtunix - Clustering approach based routing protocol for VANETs
Webtunix - Adaptive Feedback Canceller for Hearing Aids

This chapter is focused on the implementation aspects of adaptive feedback canceller algorithms and their computational complexity when reducing misalignment and convergence rates. When an adaptive algorithm filter was used for modeling the acoustic feedback, there was wide misalignment due to a fixed step size. Through the use of the prediction–error method (PEM), the bias in the algorithm for an adaptive filter was reduced. The PEM used a variable step size and a full range of adaptive filters were used as a trade-off between the misalignment and the convergence speed.

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