Background and Objectives: According to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance. Methods: The novelty of this paper is using a statistical method consists of Plackett-Burman design, and Taguchi for the first time to specify not only important parameters, but also optimal levels for them. Minitab and Design Expert ...

Using computer simulation of a solvent-free, coarse-grained model for amphiphilic membranes, we study the excess free energy of hourglass-shaped connections (i.e., stalks) between two apposed bilayer membranes. In order to calculate the free energy by simulation in the canonical ensemble, we reversibly transfer two apposed bilayers into a configuration with a stalk in three steps. First, we gra...

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...

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...

The work in unsupervised learning centered on clustering has been extended with new paradigms to address the demands raised by real-world problems. In this regard, unsupervised feature selection has been proposed to remove noisy attributes that could mislead the clustering procedures. Additionally, semi-supervision has been integrated within existing paradigms because some background informatio...

The thermal properties of black holes in the presence of quantum elds can be revealed through solutions of the semi-classical Einstein equation. We present a brief but self-contained review of the main features of the semi-classical back reaction problem for a black hole in the microcanonical ensemble. The solutions, obtained for conformal scalars, massless spinors and U(1) gauge bosons, are us...

We review the first successes and failures of a "new wave" of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of "enhanced", dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capt...

In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augm...

A critical problem in clustering research is the definition of a proper metric to measure distances between points. Semi-supervised clustering uses the information provided by the user, usually defined in terms of constraints, to guide the search of clusters. Learning effective metrics using constraints in high dimensional spaces remains an open challenge. This is because the number of paramete...

Résumé. Nous présentons un algorithme génétique semi-interactif de sélection de dimensions dans les grands ensembles de données pour la détection d'individus atypiques (outliers). Les ensembles de données possédant un nombre élevé de dimensions posent de nombreux problèmes aux algorithmes de fouille de données, une solution est d'effectuer un pré-traitement afin de ne retenir que les dimensions...