Rodríguez Aguilar, Román
Thumbnail Image
Preferred name
Rodríguez Aguilar, Román
Official Name
Rodríguez Aguilar, Román
Alternative Name
Rodríguez-Aguilar, Román
Rodríguez-Aguilar, R.
Aguilar, R.R.
Rodriguez-Aguiler, R.
Rodríguez, R.
Roman, R.A.
Main Affiliation
ORCID
Scopus Author ID
56717663000
Researcher ID
AAO-6158-2020

Research Output
Now showing 1 - 4 of 4
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

Correction to: Data Analysis and Optimization for Engineering and Computing Problems

2020 , Vasant, Pandian , Litvinchev, Igor , Marmolejo Saucedo, José Antonio , Rodríguez Aguilar, Román , Martínez Ríos, Félix Orlando

This book was inadvertently published without updating the following (or with the following error) © Springer Nature Switzerland AG 2020

View
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

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.

View