Rodríguez Aguilar, Román
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Preferred name
Rodríguez Aguilar, Román
Official Name
Rodríguez Aguilar, Román
Alternative Name
rrodrigueza
Main Affiliation
ORCID
Scopus Author ID
56717663000
Researcher ID
AAO-6158-2020

Research Output
Now showing 1 - 6 of 6
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Machine Learning Applied to the Measurement of Quality in Health Services in Mexico: The Case of the Social Protection in Health System

2018 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Vasant, Pandian

To propose a satisfaction indicator of users of health services affiliated to the Social Protection System in Health (SPSS). Identify the effect of the main factors that are directly related to the satisfaction level and perception of quality of health services. A machine-learning model based on Logistic Models and Principal Components was developed to estimate a satisfaction index. The survey data collected for the “SPSS 2014 User’s Satisfaction Study” was used, considering a sample of 28,290 users. The proposed model shows, in general, the positive perception of quality of health services (national average 0.0756). There are factors statistically significant that influence these results, the good perception of infrastructure (OR:2.12; CI 95%:1.9–2.36); the gratuity of the service provided (OR:1.98; CI 95%: 1.42–2.76); and full medicines supply (OR:1.81; CI 95%:1.91–2.36). The proposed index can be used as an indicator for improving health care quality of the population covered by the SPSS. © 2019, Springer Nature Switzerland AG.

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Financial Fraud Detection Through Artificial Intelligence

2020 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Vasant, Pandian , Litvinchev, Igor

The present work shows the analysis and modeling of a database with information about the various credit card transactions. The objective is to detect transactions that are fraudulent. In the modeling process, the “Ridge and Lasso”, “Boosting” and “Random Forest” techniques were applied in the modeling and variables selection. The results show that the accuracy of the models was very high, so the metric “Recall” was chosen as a second criterion for selecting the best model. This metric measures the percentage of positive values of the variable “fraud”. It is concluded that the best model is that of “Boosting” with 1,500 trees and a K-Folds of 10 that presented the best results in both training and validation. © 2020, Springer Nature Switzerland AG.

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Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle

2019 , Vasant, Pandian , Marmolejo Saucedo, José Antonio , Litvinchev, Igor , Rodríguez Aguilar, Román

Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques. © 2021 Springer Nature Switzerland AG. Part of Springer Nature.

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Evaluation of inequality and technical efficiency of federal health financing for population without social security per Federal Entity, 2004–2012 in México

2017 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Vasant, Pandian

The main goal of this paper is evaluate inequality and technical efficiency of federal health financing for population without social security per federative entity. Were estimated two inequality measures of federal financing for population without social security per Federal Entity: the Gini coefficient and the Theil entropy index. Four stochastic frontier models were calculated to measure technical efficiency of health production per federative entity (2004–2012). Information on health expenditure, physical and human resources yielded by the Ministry of Health through SINAIS was used. The federal financing for population without social security has reduced inequality among Federal Entities due to the incorporation of the SPSS (Mexican system of social protection in terms of health). The estimate of technical efficiency of Federal Entities through stochastic frontiers shows that most of these entities have health production inefficiencies both at outpatient and hospital levels, being the outpatient level the one with more inefficiency. Not necessarily entities that receive greater resources produce more health. The existence of multiple financing sources has limited the effect of the SPSS to reduce inequality in financing for population without social security among Federal Entities. More health resources are needed in order to face demographic and epidemiological transitions, but it is necessary to spend the available resources in a more efficient way. There are three main lines of action in terms of financing: structuring financing sources and improving the allocation mechanisms; and strengthening evaluation and monitoring resources exercise.

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Quantum-behaved bat algorithm for solving the economic load dispatch problem considering a valve-point effect

2020 , Vasant, Pandian , Parvez Mahdi, Fahad , Marmolejo Saucedo, José Antonio , Litvinchev, Igor , Rodríguez Aguilar, Román , Watada, Junzo

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

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Estimation of Electricity Prices in the Mexican Market

2019 , Rodríguez Aguilar, Román , Marmolejo Saucedo, José Antonio , Vasant, Pandian

This paper presents an alpha stable regression model to estimate prices in the Mexican Electric Market. This market began operations in February 2016. The observed prices show great fluctuations in the observed data due to diverse aspects, a seasonality of the demand, the availability of fuel and the problems of congestion in the electrical network. It is relevant in a market context to have a price estimation as accurate as possible for the decision making of supply and demand. This paper proposes a methodology of the price estimation through the application of stable alpha regressions, since the behavior of the electric market has shown the presence of heavy tails in its price distribution. © Springer Nature

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