k-nearest neighbour technique
A binary neural k-nearest neighbour technique
... AURA k-NN detailed in (Weeks et ...AURA k-NN is 97% for the REAL data set and 99% for the IBM data set when the first 25 nearest neighbours are ...50 nearest neighbours are compared and 84% ...
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A Review of Data Classification Using K-Nearest Neighbour Algorithm
... Data mining is the extraction of veiled information from large database. Classification is a data mining task of forecasting the value of a categorical variable by building a model based on one or more numerical and/or ...
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Location And Query Privacy In K Nearest Neighbour Queries
... the technique of Paulet et ...Their technique is built on hardware-assisted PIR [23], which relies on a trusted third party (TTP) to define thesecret and the permutation of the ...anonymity k, this ...
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Localisation of partial discharge sources using radio fingerprinting technique
... fingerprinting technique is proposed. This technique uses the Received Signal Strength (RSS) extracted from PD measurements gathered using RF ...proposed technique models the complex spatial ...
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Automated identification of Monogeneans using digital image processing and K nearest neighbour approaches
... Images of four species of monogeneans namely Sinodi- plectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. were used in this study. The performance of the system was evaluated ...
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An Effective Candidate Refinement Approach For High Dimensional Of K-Nearest Neighbour Search
... probing technique addresses the problem that despite the fact that the average overall performance is tuned for most efficient, the variance of the performance is extraordinarily ...
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Prediction Using Back Propagation and k-Nearest Neighbour (k-NN) Algorithm
... recognized technique and school of effects counting necessary and technical analysis, has developed in up to date ...these technique and apparatus are fully depended on different ...and technique for ...
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Towards Detecting Deception using K Nearest Neighbour Model
... [11] examined the hypotheses that (1) a systematic analysis of nonverbal behaviour could be useful in the detection of deceit and (2) that lie detection would be most accurate if both verbal and nonverbal indicators of ...
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ANNF Estimation Technique and Methods for Finding nearest Neighbour Field
... conventional k-nearest neighbor algorithms can be used effectively on these feature ...Conventional k-nearest neighbor algorithms do not take into consideration any of the image ...
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Improving the Accuracy of K-Nearest Neighbour Method in Long Lead Hydrological Forecasting
... 3. Evolutionary optimization algorithms: Evolution- ary optimization methods are indeed the advanced form of the previous technique (trial and error pro- cess). In the current research, Honey-Bee Mating ...
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Scalable Distributed Processing of K nearest Neighbour Queries over Moving Objects
... self-destruction technique is used data securely provided to the user and provides usability, ...of k-nearest neighbor (k-NN) queries over moving objects within a geographic ...
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Heart Disease Prediction System Using K Nearest Neighbour Classification Technique
... The existing system using Support Vector Machine(SVM)[3], it propose a system for heart disease prediction. It was not provide accurate results and taks more time to train the database images[5]. The heart beat parameter ...
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A Novel Approach to Fuzzy-Based Facial Feature Extraction and Face Recognition
... extraction technique that maximizes class separability along row and column directions ...extraction technique, named fuzzy generalized two-dimensional Fisher’s linear discriminant analysis (FG-2DLDA) ...
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Improving classification performance of k nearest neighbour by hybrid clustering and feature selection for non communicable disease prediction
... hybrid k-means as clustering technique, Weight SVM as feature selection technique and k-nearest neighbour as classifier ...that k-means + weight by SVM + k-nn ...
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Freeman chain code as representation in offline signature verification system
... Verification is the process of testing either a claimed signature is genuine or forgery. In our case, there are 15 signatures per class, eleven from them are trained and four are tested. Verification involved loading the ...
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AdaBoost for Concrete Type of Keywords Annotation
... The remainder of this paper is organised as follows. Section 2 reviews related work in the field of Content-based image retrieval (CBIR) and its challenges with respect to current approaches. Section 3 provides an ...
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Prediction Of Phishing Websites And Analysis Of Various Classification Techniques
... From the above chart, it is clear that the False Positive Rate (FPR), False Discovery Rate (FDR) is high for the Decision Tree technique. Similarly the False Omission Rate (FOR) of the Support Vector Machine ...
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Title: An Enhanced Model for the Classification of Mined Data
... both K-Nearest Neighbour (KNN) Algorithm and Euclidean Distance Classifier for text mining and classification using data mining that requires fewer documents for ...
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DENSITY ESTIMATION FOR STATISTICS AND DATA ANALYSIS
... function K determines the shape of the bumps while the window width h determines their ...-1 K{(x - X i )/h} are shown as well as the estimate constructed by adding them ...
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Consensus speed optimisation with finite leadership perturbation in k-nearest neighbour networks
... the k-NNR structure as its starting point and focus on how to achieve a highly responsive system by perturbing the system through supply- ing leadership ...
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