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Knowledge discovery is also known as Data mining in databases, in recent years that technique plays a major role in research area. Data mining in healthcare domain has noteworthy usage in real world. The mining method can enable the healthcare field for the enhancement of institutionalization of its administrations and become quicker with best in class technologies. Innovation utilization isn't restricted to basic leadership in undertakings, yet spread to different social statuses in all fields. In this paper a novel approach for the detection of brain tumor is proposed. The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used. Discrete wavelet transform (DWT) is used for transforming input image data set, in which RGB color of input data image has been converted into gray scale. Then it has been classified using KNN after that the error avoiding algorithm has been carried out. This will help to differentiate tumor cells and the normal cells. The presence of tumor in brain image is detected using parametric analysis by simulation.

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Cellular manufacturing has been effective in implementing Group Technology Philosophy so as to design a manufacturing system. In cellular manufacturing, it is imperative for the machines to be grouped and the parts to be assigned as part families depending on the level of similarity. Several research works have been carried out on the challenges faced in cell formation and their variants. Yet, based on zero-one machine-product occurrence matrix as the study's contribution data, only a small number of researchers have come out with a solution for overcoming feasibility evaluation and cell arrangement problems. An innovative similarity coefficient approach was proposed in the study to integrate feasibility evaluation, cell creation and intra-machine cell layout design by taking operation sequence into account. For tackling the challenges, an efficient heuristic solution has been proposed to use the similarity coefficient. The purpose of this study is to use eigenvalues of the resemblance matrix of coefficient and Kaiser's law to determine the appropriate sum of cells in the machine-product occurrence matrix. It is followed by rearranging the machines as well as the product depending on similarity value. The intra-machine cell layout design depending on flow matrix is attained. In addition, the proposed approach takes into consideration the intercellular movements, backtracking movements, number of operation and voids. The performance of the proposed approach has been assessed with renowned bench mark problem derived from previous literature and the findings have been contrasted to the CASE and CLASS algorithm. From the computational result, it could be inferred that the suggested approach is found to offer better (or) equal solution than the prevailing method. To add more, a real-world industrial case has been suggested to make evident the manner in which the proposed approach functions and the benefits attained by implementing the same.