To determine the maturity of Strawberry fruit can be seen in color fruit. The ripe strawberries red and those that are not yet green. ripeness strawberries, classification carried out on using feature extraction waena. results classified K-Nearest Neighbors (KNN) method. first method strawberry is (1) Hue Saturation Value (HSV) method, (2) KNN. From implementation results, success rate KNN 76%

The “nearest neighbor” relation, or more generally the “k nearest neighbors” relation, defined for a set of points in a metric space, has found many uses in computational geometry and clustering analysis, yet surprisingly little is known about some of its basic properties. In this paper, we consider some natural questions that are motivated by geometric embedding problems. We derive bounds on t...

Heart failure is a type of disease that has the largest number patients in world. Based on information from data center, there were 229,696 people with heart 2013. Lack public knowledge about what indications person having make main cause not handled properly by patients. In this study, classification was carried out using KNN algorithm because it simple calculation and fast time. This study on...

k is the most important parameter in a text categorization system based on k-Nearest Neighbor algorithm (kNN).In the classification process, k nearest documents to the test one in the training set are determined firstly. Then, the predication can be made according to the category distribution among these k nearest neighbors. Generally speaking, the class distribution in the training set is unev...

background : kernel smoothing method is a non-parametric or graphical method for statistical estimation. in the present study was used a kernel smoothing method for finding the death hazard rates of patients with acute myocardial infarction. methods : by employing non-parametric regression methods, the curve estimation, may have some complexity.  in this article, four indices of epanechnikov, b...

-K-Nearest Neighbor is used broadly in text classification, but it has one deficiency—computational efficiency. In this paper, we propose a heuristic search way to find out the k nearest neighbors quickly. Simulated annealing algorithm and inverted array are used to help find out the expected neighbors. Our experimental results demonstrate a significant improvement in classification computation...

Classification based on k-nearest neighbors (kNN classification) is one of the most widely used classification methods. The number k of nearest neighbors used for achieving a high accuracy in classification is given in advance and is highly dependent on the data set used. If the size of data set is large, the sequential or binary search of NNs is inapplicable due to the increased computational ...

This paper presents a method that combines Mutual Information and k-Nearest Neighbors approximator for time series prediction. Mutual Information is used for input selection. K-Nearest Neighbors approximator is used to improve the input selection and to provide a simple but accurate prediction method. Due to its simplicity the method is repeated to build a large number of models that are used f...