Nearest neighbor search

combines diversity and visual data mining techniques to improve retrieval efficiency. It includes the user into the processing path, to interactively distort the search space in the image description process, forcing the elements that he/she considers more similar to be closer and elements considered less similar to be farther in the search space. Thus, DiVI allows inducing in the space the intuitive perception of similarity lacking in the numeric evaluation of the distance function. It also allows

In the second quadrant, Fairfield becomes the center of outdoorsy people when its neighbors like Holroyd and Penrith are also in a high rate. Move to the third quadrant, more areas are covered by green and yellow, representing that the proportion for outdoorsy people here is about 20% in average. It is strange that ratio of City of Sydney is at lowest level while in Waverley at highest level who are actually neighbors. This phenomenon appeared in the proposal1 as we supposed that users in City are

2.2. RELATED WORK 2. 2.1. SECURE k-NEAREST NEIGHBOR TECHNIQUES Retrieving the k-Nearest Neighbors to a given query (q) is one of the most fundamental problems in many application domains such as similarity search, pattern recognition, and data mining. In the literature, many techniques have been proposed to address the SkNN problem, which can be classified into two categories based on whether the data are encrypted or not: centralized and distributed. Centralized Methods: In the centralized methods

is easier than updating nodes in (H. Chen, 2014; Li et al., 2014). In (Li et al., 2014) it is important to build the Voronoi Diagram (VD) to answer the bi-chromatic reverse k-nearest neighbor (BRkNN) query without the need to compute the BRkNN answer set for each data point. Li et al. (2014) address the k nearest neighbors’ answers which not addressed in the rest. Thus algorithms which proposed in (Li et al., 2014) outperform the rest in location-based applications. Quantile filter-based algorithm

Mei wang, Xiao-Wei Wu, Hsiung-Cheng Lin, Jian-ping wang(2012) discussed the idea of following. Today medical images are widely used. To achieve a clear and noise free images Image-segmentation is essential point. In this paper a new image segmentation method that is proportion of foreground to background is used also called PFB. Using this method on human brain image we experiment the result that the number of iteration steps for the threshold T is reduced. Using this method over segmentation and

Based on the objectives of the experiment, it is important to describe the credit datasets, the classiers, the combination techniques of the classiers, and lastly the software used in carrying out the experiments.. 3.1 Datasets To access the prediction accuracy of the four classiers and their combinations in the two class classication problem analysis, two real life datasets taken from the University of California, Irvine(UCI) repository were used. These datasets are described below [28]. 3.1

Gender Recognition and Android Development Summer Internship Report TBI Online, Noida Prakhar Singh IV Year, ECW Acknowledgement The internship I had with TBI Online was a great chance for me to work with and learn from a professional environment. I am very grateful that I was provided with this opportunity. I consider myself lucky for having a chance to meet so many wonderful people and professionals who mentored me throughout my internship period. I am using this opportunity

Abstract—Social data analysis could be a kind of analysis during Which individuals add a social, cooperative context to form sense of knowledge Social data analysis includes 2 main constituent parts: 1) knowledge generated from social networking sites (or through social applications), and 2) refined analysis of that knowledge, in several cases requiring period (or close to real-time) knowledge analytic, measurements that perceive and suitably weigh factors like influence, reach, and contentedness

Abstract—Accurately localization using WiFi fingerprinting is an important issue. As many sensors can be exposed to attacks, we need to be sure about the validity of the received data and to get the accurate position inspite of the presence of attack. We address the problem of detecting malicious attacks to the sensors of a WiFi fingerprinting network. Here, we suggest a novel algorithm to detect the attack using statistical measurement. We expose our data to a spoofing attack algorithm to see the

processing and drastically reduce the internal workload to fully understand the benefits. “Random space perturbation (RASP) processing” method provides security and various query processing services to provide confidentiality in the cloud. The (K-Nearest Neighbour) KNN-R algorithm is used here to convert the range query to the KNN query. Users have been certified by using the randomly generated key value provided by the administrator subsequent to successful registration by the client thus maintaining