DBSCAN is a base algorithm for density based clustering. It can detect the clusters of different shapes and sizes from the large amount of data which contains noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. In this paper, we propose a density varied DBSCAN algorithm which is capable to handle local density variation within the cluster. It calculates the growing cluster density mean and then the cluster density variance for any core object, which is supposed to be expended further, by considering density of its -neighborhood with respect to cluster density mean. If cluster density variance for a core object is less than or equal to a threshold value and also satisfying the cluster similarity index, then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results. General TermsDensity Based Clustering

The presence of diseases in several kinds of fruits is the major factor of production and the economic degradation of the agricultural industry worldwide. An approach for the apple disease classification using color-, texture-and shape-based features is investigated and experimentally verified in this paper. The primary steps of the introduced image processing-based method are as follows: (1) infected fruit part detection is done with the help of K-means clustering method, (2) color-, texture-and shape-based features are computed over the segmented image and combined to form the single descriptor, and (3) multi-class support vector machine is used to classify the apples into one of the infected or healthy categories. Apple fruit is taken as the test case in this study with three categories of diseases, namely blotch, rot and scab as well as healthy apples. The experimentation points out that the introduced method is better as compared to the individual features. It also points out that shape feature is not better suited for this purpose.

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Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image.Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.

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