Ana Suárez Sánchez

Genetic Algorithm Based on Support Vector Machines for Computer Vision Syndrome Classification

The inclusion in workplaces of video display terminals has introduced multiple benefits in the organization of the work. Nevertheless, it also implies a series of risks for the health of the workers, since it can cause ocular and visual disorders, among others.

Determination and study of lead times of metallic components in the aerospace industry through a Cox-type hazard model

The aim of this paper is the analysis of the factors that have influence on the lead time of batches of metallic components of aerospace engines. The approach used in this article employs Cox-type hazard models, which are a well-recognized statistical technique for exploring the relationship between a time variable, in this case the lead time, usually called survival variable, and several explanatory variables (covariates). A model that estimates the lead time of different components has been developed using some sample batches, and its validity is checked with a different sample of similar components.

A Hybrid Algorithm for the Prediction of Computer Vision Syndrome in Health Personnel Based on Trees and Evolutionary Algorithms

In the last decades, the use of video display terminals in workplaces has become more and more common. Despite their remarkable advantages, they imply a series of risks for the health of the workers, as they can be responsible for ocular and visual disorders.

A Hybrid Algorithm for the Assessment of the Influence of Risk Factors in the Development of Upper Limb Musculoskeletal Disorders

A hybrid model based on genetic algorithms, classification trees and multivariate adaptive regression splines is applied to identify the risk factors that have the strongest influence on the development of an upper limb musculoskeletal disorder using the data of the Spanish Seventh National Survey on Working Conditions. The study is performed among a sample of workers from the extractive and manufacturing industry sector, where upper limb have been the most frequently reported disorders during 2016.

Genetic algorithm based on support vector machines for computer vision syndrome classification in health personnel

The inclusion in workplaces of video display terminals has brought multiple benefits for the organization of work. Nevertheless, it also implies a series of risks for the health of the workers, since it can cause ocular and visual disorders, among others. In this research, a group of eye and vision-related problems associated with prolonged computer use (known as computer vision syndrome) are studied. The aim is to select the characteristics of the subject that are most relevant for the occurrence of this syndrome, and then, to develop a classification model for its prediction. The estimate of this problem is made by means of support vector machines for classification. This machine learning technique will be trained with the support of a genetic algorithm. This provides the training of the support vector machine with different patterns of parameters, improving its performance. The model performance is verified in terms of the area under the ROC curve, which leads to a model with high accuracy in the classification of the syndrome.

Comparison of the Periimplant Bone Stress Distribution on Three Fixed Partial Supported Prosthesis Designs Under Different Loading. A 3D Finite Element Analysis

Background: Despite high success and survival rates of implant supported prosthesis therapy, biomechanical complications such as periimplant bone resorption continue to exist due to occlusal overloading.