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The aim of the clustering is representing the huge amount of data objects by a smaller number of clusters or groups based on similarity. It is a task of good data analysis tool that required a rapid and precise partitioning the vast amount of data sets. The clustering problem is bring simplicity in modelling data and plays major role in the process of data mining and knowledge discovery. In the early stage, there are many conventional algorithm are used to solve the problem of data clustering. But, those conventional algorithms do not meet the requirement of clustering problem. Hence, the nature-inspired based approaches have been applied to fulfil the requirements data clustering problem and it can manage the shortcoming of conventional data clustering algorithm. This present paper is conducting a comprehensive review about the data clustering problem, discussed some of the machine learning datasets and performance metrics. This survey paper can helps to researcher in to the next steps in future.

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With the introduction of VR(Virtual Reality) based simulators, surgeons keep learning these procedures that are different from traditional surgery procedures. In fact VR environments are promising medium to carry out training and practicing surgery techniques efficiently. This paper investigates the key component of simulation of multiple layers of 3D soft tissues of human skin. CUDA based GPU computing is adopted to speed up the simulation performance. The parallel computation is achieved using necessary data structures and algorithms. The performance evaluation of the model is done using vtkPython and CUDA programming language implementations. The comparative analysis of the models performance shows that there is a significant increase in speed up at a fraction of the cost with GPU equivalent to ten times the traditional CPU cores.

In wireless scenarios, the image is transmitted over the wireless channel block by block. Due to severe fading, we may lose an entire block, even several consecutive blocks of an image. We aim to reconstruct the lost data using correlation between the lost block and its neighbors. The basic idea is to first automatically classify the block as textured or structured (containing edges), and then fill-in the missing block with information propagated from the surrounding pixels. If the lost block contained structure, it is reconstructed using an image inpainting algorithm, while texture synthesis is used for the textured blocks. We also combine this approach with JPEG compression itself, where the encoder voluntarily skips blocks, and these are reconstructed at the decoder in the same fashion as in the wireless scenario. The switch between the two schemes is done in a fully automatic fashion based on the surrounding available blocks. The performance of this method is tested for various images and combinations of lost blocks.