Several Lucentia research colleagues have recently published two articles within the framework of the BALLADEER and AETHER-UA projects
Lucentia researchers have just had two publications published in June and July. Both articles, although different from each other, are part of the Balladeer and Aether-UA projects.
The first paper, entitled “A Data-Driven Methodology for Guiding the Selection of Preprocessing Techniques in a Machine Learning Pipeline”, was presented at the 35th International Conference on Advanced Information Systems Engineering. In this #paper, Jorge García, Alejandro Maté and Juan Carlos Trujillo, propose a novel #methodology for improving the selection of #preprocessing techniques, both from the point of view of the #MachineLearning model and the characteristics of the data.
The second article, entitled “Feature engineering of EEG applied to mental disorders: a systematic mapping study”, has been published in the scientific journal Applied Intelligence. The authors, Sandra García, Jorge García, Miguel Ángel Teruel, Alejandro Maté and Juan Carlos Trujillo, through the review and analysis of more than 900 articles, present a #SystematicMappingStudy (SMS) focused on #FeatureEngineering from #EEG (electroencephalography) data used to identify #mental #disorders. In addition, they compiled, for each paper, the mental disorder analysed, all the Feature Engineering techniques used and the #MachineLearning and #DeepLearning algorithms applied for classification.
Lucentia’s #Balladeer project, in which both publications are framed, seeks to use advances in #neuroscience to offer more accurate and effective tools in the #diagnosis of neuropsychiatric disorders, specifically #ADHD.
On the other hand, the #Aether-UA project is part of the work between five Spanish universities whose main objective is to develop technological solutions for relevant problems of society in agriculture, medicine, #dataanalysis, smart cities, industry 4.0 and risk management.
Both papers can be found in “A Data-Driven Methodology for Guiding the Selection of Preprocessing Techniques in a Machine Learning Pipeline” and in “Feature engineering of EEG applied to mental disorders: a systematic mapping study”.
The Balladeer project (PROMETEO/2021/088) is funded, within the Prometeo programme, by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana. The Aether-UA project (PID2020-112540RB-C43) is funded by the Spanish Ministry of Science and Innovation.