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K-NN based classifiers

DEEP NEURAL CLASSIFICATION AND LOGIT REGRESSION BASED ENERGY EFFICIENT ROUTING 
IN WIRELESS SENSOR NETWORK

DEEP NEURAL CLASSIFICATION AND LOGIT REGRESSION BASED ENERGY EFFICIENT ROUTING IN WIRELESS SENSOR NETWORK

... promised based on four performance metrics: Accuracy, Recall, Precision, and ...implemented classifiers on RapidMiner tool demonstrated good performance in majority of the ...hand, k-NN showed ...

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An environmental audio based context recognition system using smartphones

An environmental audio based context recognition system using smartphones

... behavior, classifiers can be divided into two groups: classi- fiers that use supervised learning (supervised classification) and unsupervised learning (unsupervised ...fier. Based on these examples, which ...

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A Review on Plant Texture Analysis Using Leaf Images

A Review on Plant Texture Analysis Using Leaf Images

... skeleton based on the combination of wavelet transform (WT) and Gaussian ...the classifiers, a nearest neighbour classifier (1­NN), a k ­nearest neighbor classifier (k ­NN) and a radial ...

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A multiple-point spatially weighted k-NN method for object-based classification

A multiple-point spatially weighted k-NN method for object-based classification

... The k-NN method is based on spectral information only, while the geostatistical weighting is added in the gk-NN method, so these two benchmarks were used to deduce directly the influence of ...

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Feature Selection for Cancer Classification:
 An SVM based Approach

Feature Selection for Cancer Classification: An SVM based Approach

... (SVM), K-Nearest Neighbor (k-NN) and Naive Bayes (NB) ...selection based on using SVM in three different ...model based on the training ...via K-Means into 3, 4 and 5 clusters. ...

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An analytical report of Perceptual, Spectrum and Cepstral Feature-based Bat Classification in Kalakad Mundanthurai Tiger Reserve with K-NN and Bayes Classifiers.

An analytical report of Perceptual, Spectrum and Cepstral Feature-based Bat Classification in Kalakad Mundanthurai Tiger Reserve with K-NN and Bayes Classifiers.

... are based on categorization of calls by clearly defined criteria, multivariate analysis if spectral and temporal parameters of calls and machine learning including Artificial Neural ...

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On the Use of k NN in Anomaly Detection

On the Use of k NN in Anomaly Detection

... Other important classifiers are neural networks [5], and Support Vector Ma- chines (SVM) [6]. Artificial Neural networks (Ann) are system that emulates the way the human brain learns. SVMs are supervised learning ...

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Performance Comparison of EMD based Noise Classification for different SNR using GMM and k-NN Classifiers

Performance Comparison of EMD based Noise Classification for different SNR using GMM and k-NN Classifiers

... The k-NN algorithm (k-Nearest Neighbor) can be classed as a nonlinear non-parametric classification ...is based on very simple principle that similar data are close to each other in the ...

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Comparison of Naive Basian and K NN Classifier

Comparison of Naive Basian and K NN Classifier

... between k-nearest neighbor and naïve basin classifier based on the subset of ...that K-NN classifier gets competition with naïve basian ...performance K-NN classifier is far ...

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Real time Processor for the Authentication of Affected Herbal Medicinal Leaves Using Image Processing Techniques- Review

Real time Processor for the Authentication of Affected Herbal Medicinal Leaves Using Image Processing Techniques- Review

... two classifiers in common use— classification trees and k- nearest-neighbor ...the k-NN rule just among those training items in the same leaf as the test ...with k-NN, and it ...

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Performance Analysis of SVM, k-NN and BPNN Classifiers for Motor Imagery

Performance Analysis of SVM, k-NN and BPNN Classifiers for Motor Imagery

... The number of types of ANNs and their uses is very high. An ANN which learns using the back propagation algorithm for learning the appropriate weights, is one of the most common models used in NNs, and many others are ...

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An Hybrid Intrusion Detection Approach based on SVM Classification and k NN

An Hybrid Intrusion Detection Approach based on SVM Classification and k NN

... In the field of intrusion detection, just a couple of open datasets are accessible to assess the execution of IDSs. The NSL-KDD dataset in Table 1 is a viable benchmark, which enhanced the celebrated KDDCup99 dataset by ...

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A REVIEW ON PLANT RECOGNITION AND CLASSIFICATION TECHNIQUES USING LEAF IMAGES

A REVIEW ON PLANT RECOGNITION AND CLASSIFICATION TECHNIQUES USING LEAF IMAGES

... database based on digital morphological feature is ...and k-NN classifiers, it can be found that the MMC classifier can not only save the storage space but also reduce the classification ...

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Automatic ROI detection and classification of the Achilles tendon ultrasound images

Automatic ROI detection and classification of the Achilles tendon ultrasound images

... various classifiers have been tested in order to determine the most appropriate one for our application based on the final ...SVM, k- Nearest Neighbors (k-NN), Decision Trees (DT) and ...

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Ensemble Neural Network and K-NN
          Classifiers for Intrusion Detection

Ensemble Neural Network and K-NN Classifiers for Intrusion Detection

... is based on the principle that the characteristics of intrusion are different from normal behavior In general, IDS can be divided into two categories:anomaly detection and misuse(signature) ...

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Performance Analysis of Certain Classifiers for Liver CT Images

Performance Analysis of Certain Classifiers for Liver CT Images

... six classifiers for CT image ...abnormal based on textural features and based on these features the performance of each classifier has been evaluated for the parameters such as accuracy, specificity ...

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Statistical Data Classification Using Instance Based Learning Algorithm

Statistical Data Classification Using Instance Based Learning Algorithm

... categorized based on working method of supervised classification ...memory based learning itself as instance based learning ...basis k-factor .where k will be ...

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Research of liver cancer detection based on improved K NN algorithm

Research of liver cancer detection based on improved K NN algorithm

... (3) The different distance models, reflecting relationships among features, have different degree of influence in the progress of classification. The selection of different distance model will obtain different accuracy ...

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Effectiveness of Different Similarity Measures for          Text Classification and Clustering

Effectiveness of Different Similarity Measures for Text Classification and Clustering

... term based model represents document as a two-way model with the aid of ...term based similarity measure for text processing)takes less time when running in parallel, less space when running in series and ...

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A Cuisine Based Recommender System Using k NN And Mapreduce Approach

A Cuisine Based Recommender System Using k NN And Mapreduce Approach

... content based filtering also called as knowledge based models and the combination of the former and the later is the hybrid ...user based collaborative filtering recommender algorithm using Hadoop ...

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