No abstract

Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.

Abstract:Trust is an important part in a social network from security point of view. Online video sharing systems is the most popular and provide features that allow users to post a video in a web page. These features provide opportunities for a user to introduce polluted content into the system. Spammersmay post an unrelated video aiming at increasing the likelihood of the responsebeing viewed by a larger number of users. Multimedia recommendation system recommends video based on the user behavior which reduces network overhead and speed up the recommendation process. The proposed approach can recommend desired services with high precision, high recall and low response delay. To avoid the explosion of networkoverhead, user-behavior-based clustering is performed. If unrelated content is displayed in our web page, then we spam that video content. If more users spam all video content from same provider, then the provider will be deleted from server.