Recommenders are central in current applications to help the user find useful information spread in large amounts of data. Most Recommenders are more effective when huge amounts of user data are available in order to ...

Data stream mining needs to deal with scenarios where data distribution can change over time. As a result, different learning algorithms can be more suitable in different time periods. This paper proposes micro-MetaStream, ...

Participants in hypothetical surveys or referenda typically express higher values for goods than do participants faced with similar choices in which the stakes involve real money. Previous meta-analyses have confirmed ...

In the integrated learning model, STEM (Science, Technology, Engineering and Mathematics), teaching and learning processes are based on active, manipulative, constructivist and discovery-oriented methodologies. The practical ...

Dynamic real-world applications that generate data continuously have introduced new challenges for the machine learning community, since the concepts to be learned are likely to change over time. In such scenarios, an ...

In the last few years, metaheuristic algorithms have been used for solving several problems in engineering, biology, physics, among others, since many of them can be modeled as being optimization tasks. Metaheuristic methods ...