K-nearest neighbor algorithm

and discontinuities also can be avoided. Finally proposed a new method using both Thresholding image segmentation and edge detection segmentation algorithm based on the Proportion of Foreground to Background. After this the basic principal is define in this paper. Proportion of Foreground to Background Algorithm combine the traditional iterative algorithm. Anita Khanna, Dr. Manish Shrivastva(2012) In their paper they used unsupervised technique of segmentation which is simple and give satisfactory

filter-based algorithm was proposed in (Chen, Liang & Yu, 2014) to answer top-k query in a wireless sensor network. It evaluate top-k query in wireless sensor networks to maximize the network lifetime. Also, an online algorithm was proposed for answering time-dependent top-k queries with different values of k. Different approaches were proposed in (B. Chen et al., 2014; H. Chen, 2014; Li et al., 2014; Sandhya & Devi, 2013) for answering top-k queries. The safe interval which used for getting the top-k data

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

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

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 effect of our attack detection algorithm. We are able to enhance the prediction of locations in presence of attack by using our detection scheme. Keywords—WiFi fingerprinting, Localization, RSSI, Security, Cyber-physical

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

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

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 privacy

Table of Contents Table of Contents 1 1. Introduction 2 2. Literature Review 2 3. Classification Techniques in Machine Learning 3 3.1 K-nearest Neighbor 3 3.2 Support Vector Machine 4 3.3 Naïve Bayes Classifier 5 References 8 Introduction Dams are important structures to supply water for irrigation or drinking, to control flood, and to generate electricity. The safety analysis of concrete gravity dams in seismic regions is significant due to the high potential of life and economic losses if

Playing, Speech Recognition, etc. There are many different approaches to supervised learning. Some of these are widely applicable and others are applicable only to certain specific set of problems. Some of these are Naïve Bayes classifier, Nearest Neighbor Algorithm, Decision Trees, Support Vector Machines, Gaussian Processes, Boosting, and Artificial Neural