Current approaches to RNA structure prediction range from physics-based methods, which rely on thousands of experimentally measured thermodynamic parameters, to machine-learning (ML) techniques. While the methods for parameter estimation are successfully shifting toward ML-based approaches, the model parameterizations so far remained fairly constant. We study the potential contribution of incre...

In this work, the time series modeling was used to predict the Tazareh coal mine risks. For this purpose, initially, a monthly analysis of the risk constituents including frequency index and incidence severity index was performed. Next, a monthly time series diagram related to each one of these indices was for a nine year period of time from 2005 to 2013. After extrusion of the trend, seasonali...

Soft sensors for the on-line estimation of kerosene 95 % distillation end point (D95) in crude distillation unit (CDU) are developed. Experimental data are acquired from the refinery distributed control system (DCS) and include on-line available continuously measured variables and laboratory data which are consistently sampled four times a day. Additional laboratory data of kerosene D95 for the...

abstract: converting point data to continuous one is the first step in order to use them in scorpan flowchart. for this purpose, different geostatistic methods are available which at present research regression kriging with local variogram was applied. for mapping apparent electrical conductivity at the area covering 72000ha located in ardakan region, 700 readings in horizontal and vertical mod...

‎financial market modeling and prediction is a difficult problem and drastic changes of the price causes nonlinear dynamic that makes the price prediction one of the most challenging tasks for economists‎. ‎since markets always have been interesting for traders‎, ‎many traders with various beliefs are highly active in a market‎. ‎the competition among two agents of traders‎, ‎namely trend follo...

Ensembles have been shown to provide better generalization performance than single models. However, the creation, selection and combination of individual predictors is critical to the success of an ensemble, as each individual model needs to be both accurate and diverse. In this paper we present a hybrid multi-objective evolutionary algorithm that trains and optimizes the structure of recurrent...

BACKGROUND In boron neutron capture therapy, blood boron concentration is the key factor to calculate radiation dose, however, blood sampling is difficult during neutron irradiation. MATERIALS AND METHODS The prediction of blood boron concentrations for BNCT treatment planning has been prospectively investigated using patient data obtained at first craniotomy after the infusion of a low dose ...

energy management is one of the main ways of the efficient use of energy resources. the prediction of crop yields based on energy inputs can help farmers and policymakers to estimate the level of production. required data for study were randomly collected from 70 broiler farms in north west of iran. the input energies were included human labour, machinery, fuel, feed and electricity and the out...

In this paper, we propose a support vector machine (SVM) ensemble classification method. Firstly, dataset is preprocessed by Wilcoxon rank sum test to filter irrelevant genes. Then one SVM is trained using the training set, and is tested by the training set itself to get prediction results. Those samples with error prediction result or low confidence are selected to train the second SVM, and al...

Reversible image watermarking enables the embedding of copyright or useful information in a host image without any loss of information. Here a novel technique to improve the embedding capacity i.e. reversible watermarking using an adaptive prediction error expansion & pixel selection is proposed. This work is an improvement in conventional Prediction Error Expansion by adding two new techniques...