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Details

  • Name

    Jorge Morais
  • Role

    External Research Collaborator
  • Since

    01st January 2010
Publications

2024

Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models

Authors
Alves, A; Pereira, J; Khanal, S; Morais, AJ; Filipe, V;

Publication
Optimization, Learning Algorithms and Applications

Abstract

2023

X-Wines: A Wine Dataset for Recommender Systems and Machine Learning

Authors
de Azambuja, RX; Morais, AJ; Filipe, V;

Publication
BIG DATA AND COGNITIVE COMPUTING

Abstract
In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1-5 ratings carried out over a period of 10 years (2012-2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented.

2023

An Ontological Model for Fire Evacuation Route Recommendation in Buildings

Authors
Neto J.; Jorge Morais A.; Gonçalves R.; Coelho A.L.;

Publication
Lecture Notes in Networks and Systems

Abstract
The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protégé tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.

2023

Geometric and Physical Building Representation and Occupant’s Movement Models for Fire Building Evacuation Simulation

Authors
Neto J.; Morais A.J.; Gonçalves R.; Coelho A.L.;

Publication
Lecture Notes in Networks and Systems

Abstract
Building evacuation simulation allows for a better assessment of fire safety conditions in existing buildings, which is why it is of interest to develop an easy-to-use Web platform that helps fire safety technicians in this assessment. To achieve this goal, the geometric and physical representation of the building and installed fire safety devices are necessary, as well as the modelling of occupant movement. Although these are widely studied areas, in this paper, we present two new model approaches, either for the physical and geometric representation of a building or for the occupant’s movement simulation, during a building evacuation process. To test both models, we develop a multi-agent Web simulator platform. The tests carried out show the suitability of the model approaches herein presented.

2023

Guiding Evacuees to Improve Fire Building Evacuation Efficiency: Hazard and Congestion Models to Support Decision Making by a Context-Aware Recommender System

Authors
Neto, J; Morais, AJ; Gonçalves, R; Coelho, AL;

Publication
BUILDINGS

Abstract
Fires in large buildings can have tragic consequences, including the loss of human lives. Despite the advancements in building construction and fire safety technologies, the unpredictable nature of fires, particularly in large buildings, remains an enormous challenge. Acknowledging the paramount importance of prioritising human safety, the academic community has been focusing consistently on enhancing the efficiency of building evacuation. While previous studies have integrated evacuation simulation models, aiding in aspects such as the design of evacuation routes and emergency signalling, modelling human behaviour during a fire emergency remains challenging due to cognitive complexities. Moreover, behavioural differences from country to country add another layer of complexity, hindering the creation of a universal behaviour model. Instead of centring on modelling the occupant behaviour, this paper proposes an innovative approach aimed at enhancing the occupants' behaviour predictability by providing real-time information to the occupants regarding the most suitable evacuation routes. The proposed models use a building's environmental conditions to generate contextual information, aiding in developing solutions to make the occupants' behaviour more predictable by providing them with real-time information on the most appropriate and efficient evacuation routes at each moment, guiding the occupants to safety during a fire emergency. The models were incorporated into a context-aware recommender system for testing purposes. The simulation results indicate that such a system, coupled with hazard and congestion models, positively influences the occupants' behaviour, fostering faster adaptation to the environmental conditions and ultimately enhancing the efficiency of building evacuations.