Person:
Vela Pérez, María

First Name

María

Last Name

Vela Pérez

URI

https://hdl.handle.net/20.500.14352/74842

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Ciencias Económicas y Empresariales

Department

Economía Financiera, Actuarial y Estadística

Area

Estadística e Investigación Operativa

Identifiers

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Now showing 1 - 10 of 13

Prediction of Opinion Keywords and Their Sentiment Strength Score Using Latent Space Learning Methods
(Applied Sciences, 2020) García Cuesta, Esteban; Gómez Vergel, Daniel; Gracia Expósito, Luis; López López, Jose M.; Vela Pérez, María
Most item-shopping websites give people the opportunity to express their thoughts and opinions on items available for purchasing. This information often includes both ratings and text reviews expressing somehow their tastes and can be used to predict their future opinions on items not yet reviewed. Whereas most recommendation systems have focused exclusively on ranking the items based on rating predictions or user-modeling approaches, we propose an adapted recommendation system based on the prediction of opinion keywords assigned to different item characteristics and their sentiment strength scores. This proposal makes use of natural language processing (NLP) tools for analyzing the text reviews and is based on the assumption that there exist common user tastes which can be represented by latent review topics models. This approach has two main advantages: is able to predict interpretable textual keywords and its associated sentiment (positive/negative) which will help to elaborate a more precise recommendation and justify it, and allows the use of different dictionary sizes to balance performance and user opinion interpretability. To prove the feasibility of the adapted recommendation system, we have tested the capabilities of our method to predict the sentiment strength score of item characteristics not previously reviewed. The experimental results have been performed with real datasets and the obtained F1 score ranges from 66% to 77% depending on the dataset used. Moreover, the results show that the method can generalize well and can be applied to combined domain independent datasets.
A simple but complex enough -SIR type model to be used with COVID-19 real data. Application to the case of Italy
(Physica D: nonlinear phenomena, 2021) Fernández, M. R.; Kubik, Alicja Barbara; Ivorra, Benjamín Pierre Paul; Vela Pérez, María; Ramos Del Olmo, Ángel Manuel
Since the start of the COVID-19 pandemic in China many models have appeared in the literature, trying to simulate its dynamics. Focusing on modeling the biological and sociological mechanisms which influence the disease spread, the basic reference example is the SIR model. However, it is too simple to be able to model those mechanisms (including the three main types of control measures: social distancing, contact tracing and health system measures) to fit real data and to simulate possible future scenarios. A question, then, arises: how much and how do we need to complexify a SIR model? We develop a -SEIHQRD model, which may be the simplest one satisfying the mentioned requirements for arbitrary territories and can be simplified in particular cases. We show its very good performance in the Italian case and study different future scenarios.
Double-strand break repair through homologous recombination in autosomal-recessive BCL10 deficiency
(Journal of Allergy and Clinical Immunology, 2019) García-Gómez, Sonia et al.; Pérez de Diego, Rebeca; Sánchez Ramón, Silvia María; Vela Pérez, María; Recio Hoyas, María José
Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China
(Communications in Nonlinear Science and Numerical Simulation, 2020) Ivorra, Benjamín Pierre Paul; Ferrández, M.R.; Vela Pérez, María; Ramos, Ángel Manuel
In this paper we develop a mathematical model for the spread of the coronavirus disease 2019 (COVID-19). We use a compartmental model (but not a SIR, SEIR or other general purpose model) and take into account the known special characteristics of this disease, as the existence of infectious undetected cases. We study the particular case of China (including Chinese Mainland, Macao, Hong-Kong and Taiwan, as done by the World Health Organization in its reports about COVID-19), the country spreading the disease, and use its reported data to identify the modelparameters, which can be of interest for estimating the spread of COVID-19 in other countries. The model is also able to estimate the needs of beds in hospitals for intensive care units. Finally, we also study the behavior of the outputs returned by our model when considering incomplete data (by truncating them at some dates before and after the peak of daily reported cases). By comparing those results with real observation we can estimate the error produced by the model when identifying the parameters at early stages of the epidemic.
Project number: 245
Big data en educación II: metodologías adaptativas en el proceso de enseñanza-aprendizaje desde el diagnóstico del estudiante
(2019) Hernández Estrada, Adolfo; García Pérez, Enrique; Fernández-Cid Enríquez, Matilde; Vela Pérez, María; Peñaloza Figueroa, Juan Luis; Martínez Rodríguez, María Elena; Arteaga Martínez, Blanca; Macías Sánchez, Jesús; Martín Apaolaza, Nirian; Fernández-Crehuet Santos, José María; Pérez Martín, María; Mateos-Aparicio Morales, Gregoria; Fernández Molina, María Elia; Dorado Sánchez, Juan; Ruozzi López, Alberto; Martíns Pinto, Ana Rita; Martínez de La Fuente, Jorge Iván; Andrés García, Ángel de; Carrasco Pradas, Mª Desamparados; Álvarez Sáez, Manuel; Ferrer Caja, José María; Aparicio Sánchez, María Del Socorro; Barreal Pernas, Jesús; Jannes, Gil
Project number: 298
Análisis de las causas del abandono de los estudiantes del Grado en Administración y Dirección de Empresas, Grado en Economía y Grado en Finanzas, Banca y Seguros durante el primer año de estudios universitarios
(2023) Rivero Rodríguez, Carlos; Alonso Guinea, Fernando; Bonilla Díaz, Moisés; Camiña Centeno, Ester; Colcas Sánchez, Darío Percy; Di Meglio Berg, Gisela Amanda; Díaz-Bustamante Ventisca, Mónica; Fernández Sánchez, Rafael; Fossas Olalla, Marta; Gallego Martínez-Alcocer, Jorge; García Goñi, Manuel; Martínez Rodríguez, María Elena; Ortega Ortega, Marta; Paz Antolín, María José; Plaza Llorente, María Teresa; Reques Gómez, María Elena; Sánchez Quirós, Isabel; Vela Pérez, María; Viegas Herrera, Ricardo
El objetivo es el análisis de las causas del abandono de los estudios de grado de los estudiantes de la Facultad durante el primer curso de sus estudios universitarios que pueden ser responsabilidad y que están bajo el control de la Facultad.
Project number: 301
Estrategia de gamificación aplicada a métodos estadísticos en el proceso de aprendizaje en enseñanzas turísticas
(2020) Barreal Pernas, Jesús; Jannes, Gil; Albert García, Carlos; Vela Pérez, María; Gutiérrez Salinero, María Ángeles; López Fernández, Angel Luis; Arroyo Barrigüete, José Luis; Montiel Ben Allal, Nora; Agustín Herrera, Alma; Suarez-Varela Marti, Olga; Krak, Darya; González Gutiérrez, Nebiur; Ferrando Aguirre, Daniel; López Domingo, Belén; Lluna Taverner, Francisco José; Cisneros Martínez, José David
Project number: 232
Las redes sociales como modo de establecer un vínculo estable con los egresados de las titulaciones de la Facultad de Ciencias Económicas y Empresariales
(2022) Rivero Rodríguez, Carlos; Alonso Guinea, Fernando; Díaz-Bustamante Ventisca, Mónica; Fernández Sánchez, Rafael; Fossas Olalla, Marta; Martínez Alós, Paloma Isabel; Martínez Rodríguez, María Elena; Sánchez Quirós, Isabel; Vela Pérez, María
El objetivo es desarrollar, a través de las redes sociales, herramientas que establezcan un vínculo estable con los egresados de las titulaciones de la Facultad, facilitando el seguimiento de su inserción laboral y desarrollo profesional
Project number: 355
Diseño y puesta en marcha en la Facultad de Ciencias Económicas y Empresariales de un plan de formación para el profesorado para la adaptación a la enseñanza semipresencial en el curso 2020-2021
(2021) Rivero Rodríguez, Carlos; Alonso Guinea, Fernando; Camacho Miñano, María del Mar; Fernández Sánchez, Rafael; Fossas Olalla, Marta; Martínez Alós, Paloma Isabel; Martínez Rodríguez, María Elena; Sánchez Quirós, Isabel; Vela Pérez, María
Modelling the COVID-19 pandemic: variants and vaccines
(2022) Kubik Gladys, Alicia Barbara; Ferrández, Miriam R.; Vela Pérez, María; Ivorra, Benjamín Pierre Paul; Ramos Del Olmo, Ángel Manuel
On December 2019, a new virus emerged and started to spread through the Chinese city of Wuhan, the SARS-CoV-2. On 30 January 2020, the WHO declared the COVID-19as a public health emergency of international concern. On 11 March 2020, it is declared the first pandemic caused by a coronavirus. Since then, lifestyle has been notably conditioned to this fact, and researchers have been working quickly and hard to improve the understanding of this unknown disease and shed some light on this situation. This last year, new SARS-CoV-2 variants have emerged and most of the European population is fully vaccinated against COVID-19. Here, we present a θ-SIR model that has been tested with real data during this pandemic. It is an improvement of previous models (see [1, 2]) -now we incorporate new compartments to consider vaccination and divide each infectious compartment depending on the amount of different SARS-CoV-2 variants, to finally apply it to the territory of Italy (see [3]). Compartmental models are very used models to treat epidemics mathematically. One of the main advantages of these models is the fact that its parameters are directly related to real biological processes, and hence they may give intuition about the functioning of some unknown processes (for example, the real magnitude of the pandemic studying the evolution of the asymptomatic cases, or estimations of the number of beds needed). The main result of the article presented here (i.e., [3]) is the definition of an effective reproduction number Rt for each variant – this led us to foresee the high probability of the variant Alpha generating a third wave in Italy, which finally happened.