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.
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).
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
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