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The discussion in the paper is regarding to the recognition of handwritten Devanagari vowels by means of a classifier named as K-NN (K-Nearest Neighbour). Before applying classifier, feature extortion is accomplished for extracting the feature points (FP) i.e. also known as division points (DP). In this paper the feature extortion is perform through recursive sub division technique, which is first time implemented on Devanagari vowels. K-NN classifier is functioned for the learning and the testing phases, through which the recognition go ahead to the high performances in terms of recognition rate, pre-processing and classification speed. Authors tested the described approach using the ISI (Indian Statistical Institute), Kolkata"s handwritten Devanagari vowels database containing 9191 samples, which is divided into 1:3 as testing and training samples respectively. In the recognition process using K-NN classifier 88 vowels are total wrongly identified out of 2281vowels. The recognition rate comes out to be 96.14%.

Cloud services is one of the most rapidly growing services over internet, which at the same time also faces serious security challenges. Recently, several cloud-storage service providers started to provide encryption protection to client data in the cloud. However, encryption imposes significant limits on data us .In this paper, we report security related risks and their monitoring in cloud computing. Cloud services data can be accessed only by authorized users. The security of data will be in control of the data owner. This paper shows the security challenges and monitoring and management of these security risks involved in cloud computing services. And a methodology is proposed for performing security risk assessment for cloud computing architectures presenting some of the initial results.