We investigate machine learning methods for diagnostic screening of heart disease. Coronary heart disease is the leading cause of death in the US, causing more deaths than all types of cancers combined. Early diagnosis of heart disease in women is harder than it is in men and typically requires the administration of several clinical tests on the patient. Most risk stratification methods aggrega...

An essential part of urban natural systems, urban green spaces play a crucial role in mitigating the urban heat island effect (UHI). The UHI effect refers to the phenomenon where the temperature within a city is higher than that of the surrounding rural areas. The effects of the spatial composition and configuration of urban green spaces on urban land surface temperature (LST) have recently bee...

The main aim of this paper is to analyse the performance customer behaviour in online shopping by using precision value.A novel Self-organizing map with sample size 10 and k nearest neighbour was iterated at different times for predicting accuracy percentage behaviour. minimum power analysis fixed as 0.8 maximum accepted error 0.5. result proved that got significant results 96% compared K Neare...

The nearest neighbor algorithm and its derivatives are often quite successful at learning a concept from a training set and providing good generalization on subsequent input vectors. However, these techniques often retain the entire training set in memory, resulting in large memory requirements and slow execution speed, as well as a sensitivity to noise. This paper provides a discussion of issu...

A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of Gray Scale and Silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and Nearest Neighbor Classifier are used fo...

In this paper we evaluate an instance-based spam filter based on the SVM nearest neighbor (SVM-NN) classifier, which combines the ideas of SVM and k-nearest neighbor. To label a message the classifier first finds k nearest labeled messages, and then an SVM model is trained on these k samples and used to label the unknown sample. Here we present preliminary results of the comparison of SVM-NN wi...

The instance-based k-nearest neighbor algorithm (KNN)[1] is an effective classification model. Its classification is simply based on a vote within the neighborhood, consisting of k nearest neighbors of the test instance. Recently, researchers have been interested in deploying a more sophisticated local model, such as naive Bayes, within the neighborhood. It is expected that there are no strong ...

This paper proposes a method to recognize scene categories using bags of visual words obtained hierarchically partitioning into subregion the input images. Specifically, for each subregion the Textons distribution and the extension of the corresponding subregion are taken into account. The bags of visual words computed on the subregions are weighted and used to represent the whole scene. The cl...

The automatic categorisation of web documents is becoming crucial for organising the huge amount of information available in the Internet. We are facing a new challenge due to the fact that web documents have a rich structure and are highly heterogeneous. Two ways to respond to this challenge are (1) using a representation of the content of web documents that captures these two characteristics ...

In this paper, we solve the k-dimensional all nearest neighbor (kD_ANN) problem, where k= 2 or 3, on a linear array with a reconfigurable pipelined bus system (LARPBS) from image processing perspective. First, for a two-dimensional (2D) binary image of size N ×N , we devise an algorithm for solving the 2D_ANN problem using a LARPBS of size N2+ , where 0< >1. Then, for a three-dimensional (3D) b...