K-Nearest-Neighbors (KNN)
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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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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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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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 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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A Comparative Study between the K Nearest Neighbors and the Multi Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition
... In this paper, an experimental evaluation on cursive handwritten Arabic numerals recognition was introduced by using in the preprocessing phase the median filter, the thresholding, the centering and the normalization ...
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Title: WHO ARE the LIKELIEST SURVIVORS of BREAST CANCER- An ANALYSIS by K NEAREST NEIGHBORS ALGORITHM
... Since ensemble procedure is far more complex than traditional methods and traditional methods give a good base level from which it is possible to improve and create new ensembles, it is customary to use ensemble ...
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On Secured Blockchain Technology For K Nearest Neighbors Algorithm
... length k and the debasement of the course of action execution of KNN is accurately made by method for the affectability of the decision of ...if k is close to nothing, and various peculiarities inside the ...
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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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A Completion on Fruit Recognition System Using K Nearest Neighbors Algorithm
... After training data system, ready to use.KNN find out the shortest distance between the feature values of test fruit training fruit images. While KNN algorithm finds out the closest example to input fruit then allocate ...
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Applying Supervised Machine Learning Algorithms for Analytics of Sensor Data
... its neighbors, with the object being assigned to the class most common among its k-nearest ...its k-nearest ...locate k “close” instances in the training data for each test ...
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On the Mutual Nearest Neighbors Estimate in Regression
... the k nearest neighbors has a cost in O ((d + log ...mutual nearest neighbors, for each of these k nearest neighbors, one has also to see if x is one of its ...
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IJCSMC, Vol. 6, Issue. 1, January 2017, pg.43 – 51 A Weighted Voting of K-Nearest Neighbor Algorithm for Diabetes Mellitus
... the k nearest neighbors of each query ...cascading K mean and K nearest Neighbor algorithm for categorization of diabetic patients in their ...
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A Machine Learning Approach to Forecast Bitcoin Prices
... Regression, K-Nearest Neighbors, Ridge Regression, Lasso Regression, Polynomial Regression, Linear Support Vector Machine, and Kernel Support Vector ...
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Interpreting Neural Networks with Nearest Neighbors
... Local model interpretation methods explain individual predictions by assigning an impor- tance value to each input feature. This value is often determined by measuring the change in confidence when a feature is removed. ...
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An Efficient movie recommendation algorithm based on improved k-clique
... The idea of a collaborative filtering approach is to collect and analyze a large amount of information about user actions and settings and then predict which users will favor their similarity with other users [17]. The ...
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Comparative Study for Text Document Classification Using Different Machine Learning Algorithms
... Classification is a supervised learning method: the goal is finding the labels of the unknown object. In the real world, the tedious amounts of manual works are required to label the unknown documents. The system is ...
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Distance Based Method for Outlier Detection of Body Sensor Networks
... We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE) to calculate the probability of the distance to k nearest ...
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Breast Cancer Diagnosis by using k Nearest Neighbor with Different Distances and Classification Rules
... In this paper, we study and analyze several distances and differ- ent values of the nearest neighbors parameter k, by using dif- ferent classification rules in the k-nearest ...
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