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A convolutional network for the classification of sleep stages
(M D P I AG, 2018-09-14)
[Abstract] The classification of sleep stages is a crucial task in the context of sleep medicine. It involves the analysis of multiple signals thus being tedious and complex. Even for a trained physician scoring a whole ...
An Intelligent and Collaborative Multiagent System in a 3D Environment
(MDPI AG, 2020-08-21)
[Abstract] Multiagent systems (MASs) allow facing complex, heterogeneous, distributed problems difficult to solve by only one software agent. The world of video games provides problems and suitable environments for the use ...
A Machine Learning Solution for Distributed Environments and Edge Computing
(MDPI AG, 2019-08-09)
[Abstract] In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive ...
On the Effectiveness of Convolutional Autoencoders on Image-Based Personalized Recommender Systems
(MDPI AG, 2020-08-19)
[Abstract]
Over the years, the success of recommender systems has become remarkable. Due to the massive arrival of options that a consumer can have at his/her reach, a collaborative environment was generated, where users ...
Feature Selection in Big Image Datasets
(MDPI AG, 2020-08-24)
[Abstract]
In computer vision, current feature extraction techniques generate high dimensional data. Both convolutional neural networks and traditional approaches like keypoint detectors are used as extractors of high-level ...
Interpretable market segmentation on high dimension data
(M D P I AG, 2018-09-17)
[Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, ...
Adaptive Real-Time Method for Anomaly Detection Using Machine Learning
(MDPI AG, 2020-08-20)
[Abstract]
Anomaly detection is a sub-area of machine learning that deals with the development of methods to distinguish among normal and anomalous data. Due to the frequent use of anomaly-detection systems in monitoring ...
Automatic detection of EEG arousals
(ESANN, 2016-04-27)
[Abstract] Fragmented sleep is commonly caused by arousals that can be
detected with the observation of electroencephalographic (EEG) signals.
As this is a time consuming task, automatization processes are required. ...
Regression Tree Based Explanation for Anomaly Detection Algorithm
(MDPI AG, 2020-08-18)
[Abstract]
This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate ...
Feature Selection With Limited Bit Depth Mutual Information for Embedded Systems
(MDPI AG, 2018-09-17)
[Abstract] Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big ...