Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model

The purpose of chromium plating is the creation of a hard and wear-resistant layer of chromium over a metallic surface. The principal feature of chromium plating is its endurance in the face of the wear and corrosion. This industrial process has a vast range of applications in many different areas. In the performance of this process, some difficulties can be found. Some of the most common are melt deposition, milky white chromium deposition, rough or sandy chromium deposition and lack of toughness of the layer or wear and lack of thickness of the layer deposited. This study builds a novel nonparametric method relied on the statistical machine learning that employs a hybrid support vector machines (SVMs) model for the hard chromium layer thickness forecast. The SVM hyperparameters optimization was made with the help of the Particle Swarm Optimizer (PSO). The outcomes indicate that PSO/SVM–based model together with radial basis function (RBF) kernel has permitted to foretell the thickness of the chromium layer created in this industrial process satisfactorily. Thus, two kinds of outcomes have been obtained: firstly, this model permits to determine the ranking of relevance of the seven independent input variables investigated in this industrial process. Finally, the high achievement and lack of complexity of the model indicate that the PSO/SVM method is very interesting compared to other conventional foretelling techniques, since a coefficient of determination of 0.9952 is acquired

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual's sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer

A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers

Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor) adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS) and compares it with the well-known technique called multivariate imputation by chained equations (MICE). The results obtained demonstrate how the proposed method outperforms the MICE algorithm.Ministerio de Economía y Competitividad; AYA2014-57648-PAsturias (Comunidad Autónoma). Consejería de Economía y Empleo; FC-15-GRUPIN14-01

Answering Multiple-Choice Questions in Which Examinees Doubt What the True Answer Is among Different Options.

This research explores the results that an examinee would obtain if taking a multiple-choice questions test in which they have doubts as to what the true answer is among different options. This problem is analyzed by making use of combinatorics and analytical and sampling methodologies. The Spanish exam through which doctors become medical specialists has been employed as an example. Although it is difficult to imagine that there are candidates who respond randomly to all the questions of such an exam, it is common that they may doubt over what the correct answer is in some questions. The exam consists of a total of 210 multiple-choice questions with 4 answer options. The cut-off mark is calculated as one-third of the average of the 10 best marks in the exam. According to the results obtained, it is possible to affirm that in the case of doubting over two or three of the four possible answers in certain group questions, answering all of them will in most cases lead to obtaining a positive result. Moreover, in the case of doubting between two answer options in all the questions of the MIR test, it would be possible to exceed the cut-off mark.post-print642 K

A New Algorithm for Multivariate Genome Wide Association Studies Based on Differential Evolution and Extreme Learning Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants, whose aim is to find those that are linked to a certain trait or illness. Due to the multivariate nature of these kinds of studies, machine learning methodologies have been already applied in them, showing good performance. This work presents a new methodology for GWAS that makes use of extreme learning machines and differential evolution. The proposed methodology was tested with the help of the genetic information (370,750 single-nucleotide polymorphisms) of 2049 individuals, 1076 of whom suffer from colorectal cancer. The possible relationship of 10 different pathways with this illness was tested. The results achieved showed that the proposed methodology is suitable for detecting relevant pathways for the trait under analysis with a lower computational cost than other machine learning methodologies previously proposed

Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees

One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage

A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers

The storage of data is a key process in the study of electrical power networks related to the search for harmonics and the finding of a lack of balance among phases. The presence of missing data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, current in each phase and power factor) affects any time series study in a negative way that has to be addressed. When this occurs, missing data imputation algorithms are required. These algorithms are able to substitute the data that are missing for estimated values. This research presents a new algorithm for the missing data imputation method based on Self-Organized Maps Neural Networks and Mahalanobis distances and compares it not only with a well-known technique called Multivariate Imputation by Chained Equations (MICE) but also with an algorithm previously proposed by the authors called Adaptive Assignation Algorithm (AAA). The results obtained demonstrate how the proposed method outperforms both algorithms.Ministerio de Economía y Competitividad, AYA2014-57648-PAsturias (Comunidad Autónoma). Consejería de Economía y Empleo, FC-15-GRUPIN14-01

Missing data imputation of solar radiation data under different atmospheric conditions

[Abstract] Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.Ministerio de Economía y Competitividad; AYA2010-1851

A parametric model of the LARCODEMS heavy media separator by means of multivariate adaptive regression splines

Modeling of a cylindrical heavy media separator has been conducted in order to predict its optimum operating parameters. As far as it is known by the authors, this is the first application in the literature. The aim of the present research is to predict the separation efficiency based on the adjustment of the device’s dimensions and media flow rates. A variety of heavy media separators exist that are extensively used to separate particles by density. There is a growing importance in their application in the recycling sector. The cylindrical variety is reported to be the most suited for processing a large range of particle sizes, but optimizing its operating parameters remains to be documented. The multivariate adaptive regression splines methodology has been applied in order to predict the separation efficiencies using, as inputs, the device dimension and media flow rate variables. The results obtained show that it is possible to predict the device separation efficiency according to laboratory experiments performed and, therefore, forecast results obtainable with different operating conditions