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Publicações

2024

Recent Advances in Learning from Data Streams

Autores
Gama, J;

Publicação
Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024, Volume 1: KDIR, Porto, Portugal, November 17-19, 2024.

Abstract

2024

Gamification in Mobile Ticketing Systems: A Review

Autores
Ferreira, MC; Gouveia, D; Dias, TG;

Publicação
INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 4, WORLDCIST 2023

Abstract
This review is an analysis of the literature on public transport and mobile ticketing systems and their gamification. The review is divided into three main topics: (i) Behavioral Change in relation to Public Transport, (ii) Gamification, and (iii) Gamification in Public Transport and Mobile Ticketing. This study shows the diversity of the theme of gamification applied to the transport sector and demonstrates its potential to attract and retain more customers for more sustainable means of transport.

2024

Design and Integration of an Elastic Sensor Sheet for Pressure Ulcer Prediction: Materials, Methods, and Network Connections

Autores
Amini, MM; Sheikholeslami, DF; Dionísio, R; Heravi, A; Faghihi, M;

Publicação
Eurosensors 2023

Abstract

2024

New heuristics for the 2-stage assembly scheduling problem with total earliness and tardiness minimisation: A computational evaluation

Autores
Talens, C; Valente, JMS; Fernandez-Viagas, V;

Publicação
COMPUTERS & OPERATIONS RESEARCH

Abstract
Traditionally, scheduling literature has focused mainly on solving problems related to processing jobs with non- assembly operations. Despite the growing interest in the assembly literature in recent years, knowledge of the problem is still in its early stages in many aspects. In this regard, we are not aware of any previous contributions that address the assembly scheduling problem with just-in-time objectives. To fill this gap, this paper studies the 2-stage assembly scheduling problem minimising the sum of total earliness and total tardiness. We first analyse the relationship between the decision problem and the generation of the due dates of the jobs, and identify the equivalences with different related decision problems depending on the instances. The properties and conclusions obtained in the analysis are applied to design two constructive heuristics and a composite heuristic. To evaluate our proposals, different heuristics from the state-of-the-art of related scheduling problems are adapted, and a computational evaluation is carried out. The excellent behaviour of the proposed algorithms is demonstrated by an extensive computational evaluation.

2024

Positioning Cyber-Physical Systems and Digital Twins in Industry 4.0

Autores
Pires, F; Melo, V; Queiroz, J; Moreira, AP; de la Prieta, F; Estévez, E; Leitao, P;

Publicação
2024 IEEE 7TH INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER-PHYSICAL SYSTEMS, ICPS 2024

Abstract
Industry 4.0 has brought innovative concepts and technologies that have greatly improved the development of more intelligent, flexible and reconfigurable systems. Two of these concepts, Cyber-Physical Systems (CPSs) and Digital Twins (DTs), have gained significant attention from various stakeholders, e.g., researchers, industry practitioners, and governmental organizations. Both are vital to support the digitalisation of products, machines, and systems, and they focus on the integration of physical and cyber processes, where one affects the other through feedback loops. Having this in mind, this paper aims to better understand how CPS and DT are correlated, particularly exploring their similarities and differences, their positioning within the Industry 4.0 paradigm, and their convergence to develop Industry 4.0 solutions. Some research challenges to develop Industry 4.0 solutions by integrating these concepts are also discussed.

2024

Next Location Prediction with Time-Evolving Markov Models over Data Streams

Autores
Andrade, T; Gama, J;

Publicação
Progress in Artificial Intelligence - 23rd EPIA Conference on Artificial Intelligence, EPIA 2024, Viana do Castelo, Portugal, September 3-6, 2024, Proceedings, Part III

Abstract
Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the pervasiveness of geolocation devices and the amount of data they generate. This paper presents an approach for personal location prediction using a probabilistic model and data mining techniques over mobility data streams. We extract the individuals’ locations from relevant events in a data stream to build and maintain a Markov Chain over the important places. We evaluate the method over 3 real-world datasets. The results show the usefulness of the proposal in comparison with other well-known approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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