Chemical substances are essential in all aspects of human life, and understanding their properties is for developing chemical systems. The species can be accurately obtained by experiments or ab initio computational calculations; however, these time-consuming costly. In this work, machine learning models (ML) estimating entropy, S, constant pressure heat capacity, Cp, at 298.15 K, developed alk...

In recent years, the cryptographic community has explored new approaches of power analysis based on machine learning models such as Support Vector Machine (SVM), MultiLayer Perceptron (MLP) or Random Forest (RF). Realized experiments proved that the method based on MLP can provide almost 100% success rate after optimization. Nevertheless, this description of results is based on the first order ...

Atomic structure models of multi-principal-element alloys (or high-entropy alloys) composed of four to eight componential elements in both BCC and FCC lattice structures are built according to the principle of maximum entropy. With the concept of entropic force, the maximum-entropy configurations of these phases are generated through the use of Monte Carlo computer simulation. The efficiency of...

Random Forests (Breiman 2001) (RF) are a fully non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF are a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. Random Forests for survival (Ishwaran and Kogalur 2007; Ishwaran, Kogalur, Blackstone, and Lauer 2008) ...

The Lovász Local Lemma is known to have an extension for cases where independence is missing but negative dependencies are under control. We show that this is often the case for random injections, and we provide easy-to-check conditions for the non-trivial task of verifying a negative dependency graph for random injections. As an application, we prove existence results for hypergraph packing an...

Recently, rotation forest has been extended to regression and survival analysis problems. However, due to intensive computation incurred by principal component analysis, rotation forest often fails when high-dimensional or big data are confronted. In this study, we extend rotation forest to high dimensional censored time-to-event data analysis by combing random subspace, bagging and rotation fo...

The experiments aimed to compare the performance of random subspace and random forest models with bagging ensembles and single models in respect of its predictive accuracy were conducted using two popular algorithms M5 tree and multilayer perceptron. All tests were carried out in the WEKA data mining system within the framework of 10-fold cross-validation and repeated holdout splits. A comprehe...