k-nn classification
Comparative Study of Retinal Blood Vessel Segmentation based on SVM and K NN Classification
... and k-nearest neighbour, a supervised technique widely used for structured prediction in a number of machine learning ...that k-nn classification of this method is suitable then svm, a feature ...
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K-NN CLASSIFICATION OVER SECURE ENCRYPTED RELATIONAL DATA IN OUTSOURCED ENVIRONMENT
... PRIVACY-PRESERVING PRIMITIVES:-Here we present a set of generic sub-protocols that will be used in constructing our proposed k-NN protocol as given in [3].All of the below protocols are considered under ...
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Detection and Classification of Apple Fruit Disease using K-NN Classification and GLCM Features
... This paper surveys the methodologies utilized for apple fruit diseases detection, segmentation of infected apple fruit part and classification of diseases by using image processing. Likewise states summary of ...
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PRIVACY PRESERVING TECHNIQUE USING K-NN CLASSIFICATION IN OUTSOURCES ENVIRONMENT
... In our most recent work [10], we proposed a novel secure k-nearest neighbor query protocol over encrypted data that protects data confidentiality, user’s query privacy, and hides data access patterns. More ...
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Comparing pixel-based and object-based algorithms for classifying land use of arid basins (Case study: Mokhtaran Basin, Iran)
... object-oriented classification where the focus was found on the difference between classification accuracies (Dean, ...the K-Nearest Neighbor (K-NN) and indicated that the overall ...
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Segmentation of Lung Nodule Image Using Global Thresholding and Classification by K-NN
... In k-NN classification the neighbours are chosen to which the objects property value is ...of k-NN algorithm is Feature ...using k-NN which includes feature extraction and ...
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Blur Classification and Deblurring of Images
... the k closest training examples in the feature ...whether K-NN is used for classification or regression. In k-NN classification, the output is a class ...its k ...
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Research of liver cancer detection based on improved K NN algorithm
... effective classification algorithm, k-Nearest Neighbor (K-NN) algorithm is widely used in many ...of classification in Liver Cancer Detection, the Principal Component Analysis (PCA) ...
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Implementation Of An Efficient Hybrid Classification Model For Heart Disease Prediction
... the classification approach will be applied for the heart disease ...of k-NN classification is applied for the heart disease ...The k-NN classifier will define the number of ...
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On the Use of k NN in Anomaly Detection
... The k -Nearest Neighbor’s algorithm ( k -NN) is a non-parametric method used for classification ...the k closest training examples in the feature space. The output in k- ...
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Color Based Image Segmentation using Classification of K Nn With Contour Analysis Method
... on K-NN ...Color classification is also applied to the ...using K-NN classification and further work will be implemented in the ...
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Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM
... Nandhini and Jaya [12] compared the Colour Based Binary Image Segmentation CBBIS and Particle Swarm Optimization PSO techniques. They classified mango, potato, pumpkin, and onion. CBBIS is well suited for food quality. ...
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Performance Comparison of EMD based Noise Classification for different SNR using GMM and k-NN Classifiers
... noise classification using Empirical Mode Decomposition ...for classification and proposes an optimized best suitable feature set for classification of different noisy environments with variation in ...
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Application Of Dimensionality Reduction On Classification Of Colon Cancer Using Ica And K-Nn Algorithm
... using k-Nearest Neighbor algorithm to classify colon cancer dataset which contains DNA microarray gene expression data with 2000 features and 62 ...the classification performance in terms of accuracy, ...
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Dynamic k NN with Attribute Weighting for Automatic Web Page Classification(Dk NNwAW)
... of k obtained range from as low as 27 to as high as ...traditional k-NN would not only be impractical, but would also present a situation where choosing a static value of k would be a ...
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Indian Sign Language Recognition Using Gabor Feature Extraction
... by K-NN is high, but computational complexity also increases and requires high memory storage as algorithm stores the training data, There are two steps mainly in K-NN, selection K ...
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Half-Against-Half Structure with SVM and k-NN Classifiers in Benthic Macroinvertebrate Image Classification
... traditional k-Nearest Neighbour (k-NN) [3] ...basic k-NN classifier is to find the k nearest examples from the training data for a test ...the k nearest neighbours assigns ...
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AN ENHANCED APPROACH FOR OUTLIER DETECTION AND CLASSIFICATION IN CATEGORICAL DATA USING CLASSIC K NN ALGORITHM
... improve classification performance in most of cases and decrease cost of ...supervised classification technique has to be proposed for dimensionality reduction in mammogram ...
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Automatic Thematic Classification of the Titles of the Seimas Votes
... 4.2 Automatic classification of documents Out of every (10) feature matrices 750 documents were selected for training and testing of the clas- sifiers (see 2.3 for the details). In order to achieve greater ...
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Real time Processor for the Authentication of Affected Herbal Medicinal Leaves Using Image Processing Techniques- Review
... In the proposed project automatic identification of leaves gained more awareness in the current scenario and proves to be a very important in many areas including agriculture, forestry and pharmaceutical industries. This ...
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