The availability of aerial and satellite imageries has greatly reduced the costs time associated with gully mapping, especially in remote locations. Regardless, accurate identification gullies from images remains an open issue despite amount literature addressing this problem. main objective work was to investigate performance support vector machines (SVM) random forest (RF) algorithms extracti...

In comparison to all other malignancies, breast cancer is the most commonform of cancer, among women. Breast prediction has been studied by a number researchers, and considered as serious threat Clinicians are finding it difficult create treatment approach that will help patients live longer, due lack solid predictive models which predicts outcome in early stages analyzing history patient’s dat...

In many applications, we have access to the complete dataset but are only interested in prediction of a particular region predictor variables. A standard approach is find globally best modeling method from set candidate methods. However, it perhaps rare reality that one uniformly better than others. natural for this scenario apply weighted L2 loss performance assessment reflect region-specific ...

In this paper we present an empirical comparison between several paradigms coming from Statistics and Artiicial Intelligence for solving a supervised classiication problem. The empirically compared paradigms are Bayesian Networks, Rule Induction and Logistic Regression. The problem to tackle is the prediction of women survival diagnosed with breast cancer taking into account four predictor vari...

Tuning the regularisation and kernel hyperparameters is a vital step in optimising the generalisation performance of kernel methods, such as the support vector machine (SVM). This is most often performed by minimising a resampling/cross-validation based model selection criterion, however there seems little practical guidance on the most suitable form of resampling. This paper presents the resul...

MOTIVATION In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to this process: feature selection, model selection and prediction assessment. With a focus on prediction assessment, we compare several methods for estimating the 'tr...

Since the introduction of Computed Tomographic Colonography (CTC), research has mainly focused on visualization and navigation techniques. Recently, efforts have shifted towards computer aided detection (CAD) of polyps. We propose a new approach to CAD in CT images that attempts to model the way a radiologist recognizes a polyp using optical flow fields (OFF). Features extracted from OFFs are u...

In this work, we have proposed an on-line computer-aided diagnostic system called "UroImage" that classifies a Transrectal Ultrasound (TRUS) image into cancerous or non-cancerous with the help of non-linear Higher Order Spectra (HOS) features and Discrete Wavelet Transform (DWT) coefficients. The UroImage system consists of an on-line system where five significant features (one DWT-based featur...

One of the most widely used standard procedures for model evaluation in classification and regression is K-fold cross-validation (CV). However, when it comes to time series forecasting, because of the inherent serial correlation and potential non-stationarity of the data, its application is not straightforward and often omitted by practitioners in favour of an out-of-sample (OOS) evaluation. In...