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models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning
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). Development and validation of machine learning models for calculating occupational health indicators. Integration, management, and analysis of data from wearable monitoring devices. Experimental evaluation
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-uniform access distributions, specifically Zipf-type distributions, to model situations in which certain elements are accessed more frequently.; Add new metrics and configuration options that allow for a
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of essential components for creating a decentralized data space that allows data sharing in an interoperable way, both at the model level and at the data transfer protocol level. Empirical studies will be used
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identified solutions will be conducted, focusing on hardware-rooted identity, decentralized authentication, trust management models, secure communications, Artificial Intelligence-based intrusion detection
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. OBJECTIVES: Traditional dropout prediction models tend to underperform precisely in those educational settings where public intervention is most critical: university programmes with small student cohorts and