Dynamic classifier auditing by unsupervised anomaly detection methods: an application in packaging industry predictive maintenance

Autor: Mateo, Fernando, Vila-Francés, Joan, Soria-Olivas, Emilio, Gómez-Sanchis, Marcelino Martínez-Sober Juan, Serrano-López, Antonio-José

Predictive maintenance in manufacturing industry applications is a challenging research field. Packaging machines are widely used in a large number of logistic companies' warehouses and must be working uninterruptedly. Traditionally, preventive maint

Externí odkaz: http://arxiv.org/abs/2405.11960

Comparison between Machine Learning and Deep Learning Approaches for the Detection of Toxic Comments on Social Networks

Autor: Andrea Bonetti, Marcelino Martínez-Sober, Julio C. Torres, Jose M. Vega, Sebastien Pellerin, Joan Vila-Francés

Publikováno v: Applied Sciences, Vol 13, Iss 10, p 6038 (2023)

The way we communicate has been revolutionised by the widespread use of social networks. Any kind of online message can reach anyone in the world almost instantly. The speed with which information spreads is undoubtedly the strength of social network

Externí odkaz: https://doaj.org/article/87b88b2fac7f461cacc6b0f0098998df

Analysis of the Pre and Post-COVID-19 Lockdown Use of Smartphone Apps in Spain

Autor: Alberto Solano, Ivan Fayos, Alberto Urueña, Marcelino Martínez-Sober, Fernando Mateo, Emilio Soria-Olivas

Publikováno v: Applied Sciences, Vol 11, Iss 13, p 5807 (2021)

The global pandemic of COVID-19 has changed our daily habits and has undoubtedly affected our smartphone usage time. This paper attempts to characterize the changes in the time of use of smartphones and their applications between the pre-lockdown and

Externí odkaz: https://doaj.org/article/4135505f99b74ce9b95a2a71d4c4efe1

Approaching sales forecasting using recurrent neural networks and transformers

Autor: Iván Vallés-Pérez, Emilio Soria-Olivas, Marcelino Martínez-Sober, Antonio J. Serrano-López, Juan Gómez-Sanchís, Fernando Mateo

Publikováno v: Vallés-Pérez, I., Soria-Olivas, E., Martínez-Sober, M., Serrano-López, A. J., Gómez-Sanchís, J., & Mateo, F. (2022). Approaching sales forecasting using recurrent neural networks and transformers. Expert Systems with Applications, 201, 116993.

Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory placement, network planning, etc). We develop three alte

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::432d97b8cb76554fbe46c6bcfd255e64
https://hdl.handle.net/10550/88380

A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time

Autor: S. Martínez-Sanchis, Marcelino Martínez-Sober, J. A. Solves-Llorens, Antonio J. Serrano-López, José D. Martín-Guerrero, F. Martínez-Martínez, Carlos Monserrat, D. Lorente, Maria J. Ruperez-Moreno

Publikováno v: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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[EN] This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21a2622a5395bb04cfd0bd568c4ed350
https://doi.org/10.1016/j.compbiomed.2017.09.019