[PDF] Top 20 Towards Detecting Deception using K Nearest Neighbour Model
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Towards Detecting Deception using K Nearest Neighbour Model
... guilt. Detecting deception remains a difficult task [8] as no perfect method has been found for the detection ...Although detecting deception remains difficult, investigators increase the odds ... See full document
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Automated identification of Monogeneans using digital image processing and K nearest neighbour approaches
... The performance of feature extraction and selection techniques depends on the type of a system’s classifiers and the quality of the data [12]. In order to achieve a high performance classification, not all features are ... See full document
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Extreme Precipitation Events Simulation Using An Improved K-Nearest Neighbour Weather Generating Model
... simplicity, nearest neighbour algorithms are considered very versatile and ...the nearest neighbour approach involves simultaneous sampling of the weather variables like precipitation and ... See full document
7
Storm Identification in the Rainfall Data Using Singular Value Decomposition and K-Nearest Neighbour Classification
... a model for relevance feature ...(terms) using singular value ...patterns using K Nearest ...paradigm using Hadoop framework as the dataset is available in large scale and hence ... See full document
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ON MODELING TRACES IN A COMPUTING ENVIRONMENT FOR HUMAN LEARNING BASED INDICATORS
... network model which is developed by Erdil et ...the model which is formed by EPUNN ...meta-model using a machine learning classifier, k nearest neighbour classifier ... See full document
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Location And Query Privacy In K Nearest Neighbour Queries
... server in D tuples and / or user requests. A server may be curious either because it is simply curious or because it has been compromised to become curious in the name of a third party without its explicit knowledge. In ... See full document
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k Nearest Neighbour Join using Map Reduce for effective Smart Travel Guide
... Description: This paper proposes a join model that simplifies creation of and reasoning about joins in Map Reduce techniques which enable efficient parallel execution of arbitrary theta-joins. Everything is ... See full document
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Probabilistic Classification from a K Nearest Neighbour Classifier
... fine-grained model can provide more de- tail, while a course-grained model allows more statistics to accumulate per element which provides more reliable ... See full document
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Title: An Enhanced Model for the Classification of Mined Data
... the model which expresses the feature of the sentence, the K-Nearest Neighbour and Euclidean Distance Classifier has been proposed, each sentence can be represented as a vector Si = (wi1;wi2; ... See full document
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Improving classification performance of k nearest neighbour by hybrid clustering and feature selection for non communicable disease prediction
... In this research, the experiment equipped with infrastructure consists Rapid Miner Toolkit and XLSTAT. Rapidminer is an open-source system composed of some data mining algorithms to analyze automatically a large data ... See full document
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Diabetes Diagnosis using Machine Learning Algorithms
... system using Machine Learning which has the ability to predict whether the patient has diabetes or ...classification model for diabetes using decision tree, Naïve Bayes, Support vector machine and ... See full document
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Heart Disease Prediction System Using K Nearest Neighbour Classification Technique
... of detecting cardiac diseases using support vector machine (SVM) is ...proposed model disease are modelled using the time domain features of ECG signal which are extracted using BIOPAC ... See full document
8
Sales Forecasting using Linear Regression and K-Nearest Neighbour
... social model will be made to characterize the social or non-social structures vide mapping among the ...distance using Euclidean function will be designated diverse ascribe gatherings to assess the closest ... See full document
6
Prediction Using Back Propagation and k-Nearest Neighbour (k-NN) Algorithm
... companies using back propagation and k-nearest neighbor algorithm, to help out executive, investors, user and choice makers in making valuable ...best model by analyzing various parameter of ... See full document
5
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 ... See full document
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Experience with Rule Induction and k Nearest Neighbour Methods for Interface Agents that Learn
... Abstract—Interface agents are being developed to assist users with a variety of tasks. To perform effectively, such agents need knowledge of user preferences. An agent architecture has been developed which observes a ... See full document
7
Human Object Behavior Monitoring System based on Machine Learning Algorithm
... Feature extraction is a notable form of dimensionality reduction. It is done after pre-processing techniques in character recognition. It is the process by which certain features of curiosity within an image are ... See full document
8
Consensus speed optimisation with finite leadership perturbation in k-nearest neighbour networks
... to k-nearest neighbour (k-NNR) connection rules, by allocating a finite leadership resource amongst selected ...perturbed k-NNR network is identi- fied as the largest element of the ... See full document
6
Discretisation of Data in a Binary Neural k-Nearest Neighbour Algorithm
... algorithms using all data with each of the bin counts (10-100 inclusive in steps of ...AURA k-NN in turn to predict the t+ 1 attribute value for the selected prediction attribute for that data ...10 ... See full document
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A Study on Content Based Image Retrieval System using K-NN Algorithm and Mobile Agents
... of K-NN algorithm. K Nearest Neighbour (i.e, K-NN) is an algorithms which is simple to understand but works incredibly well in practic ... See full document
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