Internet has changed the course of our living. It has become the most beneficial antecedent or source of information. Today almost everything is found on internet. Everyday millions of people post their ideas, reviews, stories about the services, products or other persons. The size of data is increasing tremendously. It is very difficult to analyze that amount of data and figure out the emotions or sentiments posed by people. Emotion detection is such a technique where we can judge people’s ideas and extract the emotion towards an entity or service. We have used subjective lexicon-based approach to bench the emoticons expressed by the ideas of the people. The data set that we have mainly focused is very cross and noisy. We have used Facebook data in Urdu and Kashmiri language. Both languages are very cross domain. These languages can be written in English alphabet that makes them more challenging to analyses. Our approach resolves the challenge to the maximum possible way. The results shown by our method on this kind of data set are better than any other approach. Our analysis on this type of dataset will help the local businessmen of these areas to grow and flourish. The analysis will give some insights what the local feel about the entity or product so that the manufacturers can design or build it that way and try to enhance its qualities.
A Mobile Ad-hoc Network (MANET) is a set of remote versatile hubs shaping an element self-sufficient system. Hubs speak with one another without the mediation of concentrated access focuses or base stations. In MANET various issues have been encountered that degrades the performance of the network. These problem are congestion occurs in the network sue to peer to peer communication. Sometime a single node received large amount data than that a node can transmit further to destination. This issue get extend to data loss due to congestion occurred on a node. Optimum path assures guaranteed delivery of data from source to destination. In this paper a novel approach that is based early detection of congestion at each node has been used for congestion avoidance in the proposed work. Priority and probability has been assigned for all the nodes so that data can be transmitted in effective manner. On the basis of various parameters it has been analyzed that proposed approach provides better congestion avoidance as compare to existing approaches.
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