Java is widely deployed on a variety of processor architectures. Consequently, an understanding of microarchitecture level Java performance is critical to optimize current systems and to aid design and development of future processor architectures for Java. Although this is facilitated by a rich set of processor performance counters featured on several contemporary processors, complex processor...

In this article we discuss how we have predicted the third generation (3G) customers using logistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables ...

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in heat production or energy expenditure (EE). Multivariate adaptive regression splines (MARS) is a nonparametric method that estimates complex nonlinear relationships by a series of spline functions of the independent predictors. The specific aim of this study is to construc...

A Bayesian approach to non-intrusive quality assessment of narrow-band speech is presented. The speech features used to assess quality are the sample mean and variance of bandpowers evaluated from the temporal envelope in the channels of an auditory filter-bank. Bayesian multivariate adaptive regression splines (BMARS) is used to map features into quality ratings. The proposed combination of fe...

Multivariate adaptive regression splines (MARS) are a useful tool to identify linear and nonlinear effects and interactions between two covariates. In this dissertation a new proposal to model survival type data with MARS is introduced. Martingale and deviance residuals of a Cox PH model are used as response in a common MARS approach to model functional forms of covariate effects as well as pos...

Multivariate Adaptive Regression Splines (MARS) is a supervised learning model in machine learning, not obtained by an ensemble method. Ensemble methods are gathered from samples comprising hundreds or thousands of learners that serve the common purpose improving stability and accuracy algorithms. This study presented REMARS (Random MARS), new MARS selection approach using Random Forest (RF) al...

Past few years have witnessed a growing recognition of soft computing technologies for the construction of intelligent and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we r...

Introductory Engineering Mathematics (a skill builder for engineers) involves developing problem-solving attributes throughout the teaching period. Therefore, prediction of students’ final course grades with continuous assessment marks is a useful toolkit degree program educators. Predictive models are practical tools used to evaluate effectiveness as well assessing progression and implementing...