Protecting big data mining association rules using fuzzy system

Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process

CLOUD-BASED DEVELOPMENT OF SMART AND CONNECTED DATA IN HEALTHCARE APPLICATION

ABSTRACT There is a need of data integration in cloud

Beneficial Image Preprocessing by Contrast Enhancement Techniquefor SEM Images

In this paper a morphological filtering algorithm using an exposure thresholding and measures of central tendency hasbeen proposed for solving the low contrast of Scanning Electron Microscopic (SEM) images of composite materials foraccurate Filler Content Estimation. SEM image of a composite material comprises visible morphological structures likefillers such as silica nanoparticles. The SEM image analysis via segmentation will assist in the study of distribution of thesestructures. The estimation of the filler content is more accurate only when the SEM images have proper contrast for analysisif not the results lead to less accuracy. To overcome this drawback, we have proposed a preprocessing technique to increasethe contrast of SEM images. So that the preprocessed image can be used for post processing namely segmentation and hencethe error is less for filler content estimation. We introduced the transformations using morphological processing to extractthe bright and darker features of the images. The optimum threshold value is determined by the image exposure. A detailedcomparative analysis with other existing techniques has been performed to prove the superior performance of the proposedmethod

Beneficial Image Preprocessing by Contrast Enhancement Technique for SEM Images

832-836In this paper a morphological filtering algorithm using an exposure thresholding and measures of central tendency has been proposed for solving the low contrast of Scanning Electron Microscopic (SEM) images of composite materials for accurate Filler Content Estimation. SEM image of a composite material comprises visible morphological structures like fillers such as silica nanoparticles. The SEM image analysis via segmentation will assist in the study of distribution of these structures. The estimation of the filler content is more accurate only when the SEM images have proper contrast for analysis if not the results lead to less accuracy. To overcome this drawback, we have proposed a preprocessing technique to increase the contrast of SEM images. So that the preprocessed image can be used for post processing namely segmentation and hence the error is less for filler content estimation. We introduced the transformations using morphological processing to extract the bright and darker features of the images. The optimum threshold value is determined by the image exposure. A detailed comparative analysis with other existing techniques has been performed to prove the superior performance of the proposed method