BACKGROUND Breast cancer is the main cause of women cancer mortality. Therefore, precise prediction of patients' risk level is the major concern in therapeutic strategies. Although statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability. Therefore, the aim of this study is to compare 'Model-Base...

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

URLs are very much on the network of computer systems. Moreover, nowadays all activities use an online system. Starting from social media, and marketplaces to group chat applications. An early prevention system malicious URL attacks is needed counteract large number circulating in Previously detection based blacklisting UURLs Previously, Blacklisting Heuristic could not recognize new type witho...

Bridge deck deterioration modeling is critical to infrastructure management. Deterioration traditionally done using deterministic models, stochastic and recently basic machine learning methods. The advanced learning-based survival such as random forest, have not been adapted for use in This paper introduces forest models bridge compare their performance with a commonly used traditional model, t...

In this article we introduce Random Survival Forests, an ensemble tree method for the analysis of right censored survival data. As is well known, constructing ensembles from base learners, such as trees, can significantly improve learning performance. Recently, Breiman showed that ensemble learning can be further improved by injecting randomization into the base learning process, a method calle...

the present work was designed to classify and differentiate between the dehalogenase enzyme to non–dehalogenases (other hydrolases) by taking the amino acid propensity at the core, surface and both the parts. the data sets were made on an individual basis by selecting the 3d structures of protein available in the pdb (protein data bank). the prediction of the core amino acid were predicted by i...