Images include information about human body which is used for different purposes such as medical, security and other plans. Compression of images is used in some applications such as profiling data and transmission systems. Regard to importance of images information, lossless or lossy compression is preferred. Lossless compressions are JPEG, JPEGLS and JPEG2000 are few well-known methods for lossless compression. We will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and provides good compression ratio, peak signal noise ratio and minimum mean square error. In real time application which needs hardware implementation, low complex algorithm accelerates compression process. In this paper, the authors use differential pulse code modulation for image compression lossless and near-lossless compression method is introduced which is efficient due to its high compression ratio and simplicity. This method consists of a new transformation method called Enhanced DPCM Transformation (EDT) which has a good energy compaction and a suitable Huffman encoding. After introducing this compression method, it is applied on different images from Corel dataset for experimental results and analysis. Also we compare it with other existing methods with respect to parameter compression ratio, peak signal noise ratio and mean square error.
This paper deals with comparison of the rule classifiers based on the evaluation parameters. The classification is a step by step procedure for designating a given piece of input data into any one of the given categories. We have used five Rule classifiers algorithms, namely Decision Table, JRIP, PART, OneR and ZeroR for experimental setup. The hepatitis datasets are used for calculating the performance by using the split percentage parameter. Finally find out the comparative analysis based on the performance factors such as the classification accuracy, Kappa statistic and execution time is performed on all the algorithms. The goal of this paper is to find out which classier is best to other rule classifier in WEKA under hepatitis data.
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