k nearest neighbors
ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K NEAREST NEIGHBORS MEASURES
... Vol 9, Issue 5, 2016 Online 2455 3891 Print 0974 2441 ASSESSMENT OF EPILEPSY CLASSIFICATION USING TECHNIQUES SUCH AS SINGULAR VALUE DECOMPOSITION, APPROXIMATE ENTROPY, AND WEIGHTED K NEAREST NEIGHBORS[.] ...
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Detection Model for Daniel of Service Attacks using Random Forest and k Nearest Neighbors
... and k-Nearest Neighbor for feature selection and classification ...algorithm k -Nearest Neighbors that have been widely used for ...
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A Comparative Study between the K Nearest Neighbors and the Multi Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition
... In fact, a optical character recognition system can be divided into three principal steps. The first one is a pre-processing which is used for enhancing the image quality, in this context we have interested in this work ...
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Software Development Effort Duration and Cost Estimation using Linear Regression and K Nearest Neighbors Machine Learning Algorithms
... and K-nearest neighbor's machine learning techniques in this ...to K-nearest neighbors on the data sets COCOMO81, COCOMONASA, COCOMONASA_2 by having higher correlation coefficient value ...
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Title: WHO ARE the LIKELIEST SURVIVORS of BREAST CANCER- An ANALYSIS by K NEAREST NEIGHBORS ALGORITHM
... Abstract— As deaths caused by breast cancer have become a major concern, researchers are trying to design & implement algorithms so that preventive measures can be adopted in an effective way. Hence, the aim of this ...
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A Novel Hybrid Approach for Email Spam Detection based on Scatter Search Algorithm and K-Nearest Neighbors
... Abstract: Because cyberspace and Internet predominate in the life of users, in addition to business opportunities and time reductions, threats like information theft, penetration into systems, etc. are included in the ...
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kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data
... the k nearest neighbors together with their computed distance values will be emitted to the reduce ...final k nearest neighbors from the list provided by the ...
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Improving Convolutional Neural Networks’ Accuracy in Noisy Environments Using k-Nearest Neighbors
... Extracting neural codes and then applying Support Vector Machines (SVM) or k-Nearest-Neighbors (kNN) is a common transfer learning technique [12]. Some previous works also used this strategy on the ...
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Combining Akaike’s Information Criterion (AIC) and the Golden Section Search Technique to find Optimal Numbers of K Nearest Neighbors
... of nearest neighbors, which is usually obtained by recurring experiments for different values of K till the minimum error rate is ...of nearest neighbors to be used for KNN for ...
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Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data
... Abstract—One of the best-known and most effective methods in supervised classification is the k nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where ...
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Pattern Recognition Using K-Nearest Neighbors (KNN) Technique
... Artificial intelligence is the intelligence of machines and the branch of computer science which aims to create it .AI is defined as the study and design of intelligent agents where an intelligent agent is a system that ...
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Rate Movie App: Implementation of K-Nearest Neighbors Algorithm in the Development of Decision Support System for Philippine Movie Rating and Classification
... experiment, K-Nearest Neighbors worked well with the Philippine movie ...dataset. K-Nearest Neighbors outperformed ...the K-Nearest Neighbors algorithm ...
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Compressed kNN: K-Nearest Neighbors with Data Compression
... (k-nearest neighbors) classification algorithm is one of the most widely used non-parametric classification methods, however it is limited due to memory consumption related to the size of the ...
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Local K-Nearest Neighbors Model using Z-Order R-Tree for Big Data
... ABSTRACT: K-nearest neighbors classification and regression is widely used in data mining due to its simplicity and ...the k most similar instances. Finding the value k is application ...
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Simulation of Agricultural Logistic Processes with k-Nearest Neighbors Algorithm
... the k-Nearest Neighbors algorithm (kNN) is ...The k-Nearest Neighbors algorithm which has been determined during the examination, makes it possible, to estimate the key ...
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Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data
... The k-nearest neighbors algorithm is characterized as a simple yet effective data min- ing ...the k-nearest neighbors rule has become a core algorithm to identify and correct ...
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k-Nearest Neighbors Queries in Time-Dependent Road Networks
... processing k-nearest neighbors (kNN) queries in road networks considering traffic conditions, in particular the case where the speed of moving objects is ...the k points of interest ...
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On Secured Blockchain Technology For K Nearest Neighbors Algorithm
... the utilization of some other magnificence of increased Petri nets. An adjustment of the communicate spreading calculation to include an replacing module has established an wonderful improvement in records scattering ...
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A novel method for the detection of R peaks in ECG based on K Nearest Neighbors and Particle Swarm Optimization
... calculate K R-peaks near the next detected R-peak. In this study, K was set to 7 based on experimentation ...selected K R-peaks, which Rpeak(i) repre- sents each detected R-peak amplitude ...
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The Performance of K Nearest Neighbors on Malignant and Benign Classes: Sensitivity, Specificity, and Accuracy Analysis for Breast Cancer Diagnosis
... of nearest neighbors (K) mostly affects KNN ...of neighbors generates a more complex classifier that decides by fewer ...of neighbors makes more robust ...of neighbors selects ...
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