ABSTRACT:Ensemble classifier refers to a group of individual classifiers that are cooperatively trained on data set in a supervised classification problem. In this paper we present a review of commonly used ensemble classifiers in the literature. Some ensemble classifiers are also developed targeting specific applications. We also present some application driven ensemble classifiers in this paper.

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In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications.

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Abstract:Harmonic broadcasting is a broadcasting scheme to reduce the bandwidth requirement in video-on-demand service where a video is divided into segments of equal size and each segment is repeatedly transmitted over a number of channels having bandwidth that follows a harmonic series. As the bandwidth of channels differ from each other and a user can join at any time to these multicast channels, a synchronisation problem between download and playback is created. To deal with this issue, some schemes have been proposed, however, at the cost of additional or wastage of bandwidth. In this paper, we propose an adaptive harmonic broadcasting scheme that deals with the synchronisation problem at the same average bandwidth consumption as traditional harmonic broadcasting scheme. We present several analytical results to exhibit the efficiency of the proposed scheme over the existing ones.