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About

About

Bruno Veloso. Completed the Mestrado integrado in Engenharia Eletrotécnica e de Computadores in 2012/10/31 by Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto, Licenciatura in Engenharia Eletrotécnica e de Computadores in 2010/07/31 by Instituto Politécnico do Porto Instituto Superior de Engenharia do Porto and Doctor in Telematics Engineering in 2017/09/11 by Universidade de Vigo. Is Assistant Professor in Universidade Portucalense Infante Dom Henrique, Researcher in Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência, Coordinator of the Bsc in Informatics Engineering in Universidade Portucalense Infante Dom Henrique and Coordinator of the Msc Data Science in Universidade Portucalense Infante Dom Henrique. Published 10 articles in journals. Has 8 section(s) of books and 2 book(s). Organized 5 event(s). Participated in 5 event(s). Supervised 1 MSc dissertation(s) e co-supervised 5. Has received 1 awards and/or honors. Participates and/or participated as Master Student Fellow in 1 project(s), Other in 1 project(s), PhD Student Fellow in 1 project(s) and Researcher in 4 project(s). Works in the area(s) of Engineering and Technology with emphasis on Electrotechnical Engineering, Electronics and Informatics. In their professional activities interacted with 41 collaborator(s) co-authorship of scientific papers.

Interest
Topics
Details

Details

  • Name

    Bruno Miguel Veloso
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st March 2013
002
Publications

2021

Classification and Recommendation With Data Streams

Authors
Veloso, B; Gama, J; Malheiro, B;

Publication
Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management

Abstract
Nowadays, with the exponential growth of data stream sources (e.g., Internet of Things [IoT], social networks, crowdsourcing platforms, and personal mobile devices), data stream processing has become indispensable for online classification, recommendation, and evaluation. Its main goal is to maintain dynamic models updated, holding the captured patterns, to make accurate predictions. The foundations of data streams algorithms are incremental processing, in order to reduce the computational resources required to process large quantities of data, and relevance model updating. This article addresses data stream knowledge processing, covering classification, recommendation, and evaluation; describing existing algorithms/techniques; and identifying open challenges.

2021

Crowdsourced Data Stream Mining for Tourism Recommendation

Authors
Leal, F; Veloso, B; Malheiro, B; Burguillo, JC;

Publication
Advances in Intelligent Systems and Computing - Trends and Applications in Information Systems and Technologies

Abstract

2021

Hyperparameter self-tuning for data streams

Authors
Veloso, B; Gama, J; Malheiro, B; Vinagre, J;

Publication
Information Fusion

Abstract

2021

Improving Student Engagement With Project-Based Learning: A Case Study in Software Engineering

Authors
Morais, P; Ferreira, MJ; Veloso, B;

Publication
IEEE REVISTA IBEROAMERICANA DE TECNOLOGIAS DEL APRENDIZAJE-IEEE RITA

Abstract
In the area of Information and Communication Technologies, in addition to the problem of engagement, students often have difficulties in learning subjects related to modeling and programming. The reasons for these difficulties are well known and described in the literature, pointing to difficulties in abstraction and logic thinking. Knowing that the value of flexible and personalized learning, teachers are changing the way they teach, using different active learning methodologies, such as flipped classroom, project-based learning, and peer instruction. This paper describes an experiment conducted to improve the learning experiences of the students enrolled in the Computer Science bachelor's degree course, attending three curricular units: Information Systems Development, Data Structures, and Web Languages and Technologies. The approach followed by the teachers used project-based learning as an active learning methodology. This methodology allows us to achieve four main objectives: (i) improve student engagement; (ii) improve learning outcomes achievement (iii) increase the course success rate and (iv) allow students to experience the need for the software development lifecycle, feeling that software engineering is not a block-based process but depending on previous activity, often leads to the need to go back in the process. The results obtained with the use of the active methodology were well accepted by the students and allowed both teachers and students to reach the objectives set.

2020

A 2020 perspective on “Online guest profiling and hotel recommendation”

Authors
Veloso, BM; Leal, F; Malheiro, B; Carlos Burguillo, JC;

Publication
Electronic Commerce Research and Applications

Abstract