Predicting water contamination by statistical models is a useful tool to manage health risk in recreational beaches. Extreme contamination events, i.e. those exceeding normative are generally rare with respect to bathing conditions and thus the data ...
The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this purpose, the primary source of information is the signal strength of packets received by a set of routers. The fingerpr...
Crowdsourcing is a new mode of value creation in which organizations leverage numerous Internet users to accomplish tasks. However, because these workers have different backgrounds and intentions, crowdsourcing suffers from quality concerns. In the l...
The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the data and training time have been major roadblocks ...
COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high thr...
BACKGROUND: The aim of this study was to investigate the potential use of renal ultrasonography radiomics features in the histologic classification of glomerulopathy.
Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that ...
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse an...
Neural networks : the official journal of the International Neural Network Society
Jul 22, 2021
Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of ...
Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data a...
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