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Details

  • Name

    Jorge Morais
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st January 2010
Publications

2022

Intelligent Monitoring and Management Platform for the Prevention of Olive Pests and Diseases, Including IoT with Sensing, Georeferencing and Image Acquisition Capabilities Through Computer Vision

Authors
Alves A.; Jorge Morais A.; Filipe V.; Alberto Pereira J.;

Publication
Lecture Notes in Networks and Systems

Abstract
Climate change affects global temperature and precipitation patterns. These effects, in turn, influence the intensity and, in some cases, the frequency of extreme environmental events, such as forest fires, hurricanes, heat waves, floods, droughts, and storms. In general, these events can be particularly conducive to the appearance of plant pests and diseases. The availability of models and a data collection system is crucial to manage pests and diseases in sustainable agricultural ecosystems. Agricultural ecosystems are known to be complex, multivariable, and unpredictable. It is important to anticipate crop pests and diseases in order to improve its control in a more ecological and economical way (e.g., precision in the use of pesticides). The development of an intelligent monitoring and management platform for the prevention of pests and diseases in olive groves at Trás-os- Montes region will be very beneficial. This platform must: a) integrate data from multiple data sources such as sensory data (e.g., temperature), biological observations (e.g., insect counts), georeferenced data (e.g., altitude) or digital images (e.g., plant images); b) systematize these data into a regional repository; c) provide relevant forecasts for pest and diseases. Convolutional Neural Networks (CNNs) can be a valuable tool for the identification and classification of images acquired by Internet of Things (IoT).

2022

Multi-Agent-Based Recommender Systems: A Literature Review

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

Publication
Proceedings of Sixth International Congress on Information and Communication Technology - ICICT 2021, London, UK, Volume 1

Abstract

2022

Adaptive Recommendation in Online Environments

Authors
de Azambuja R.X.; Morais A.J.; Filipe V.;

Publication
Lecture Notes in Networks and Systems

Abstract
Recommender systems form a class of Artificial Intelligence systems that aim to recommend relevant items to the users. Due to their utility, it has gained attention in several applications domains and is high demanded for research. In order to obtain successful models in the recommendation problem in non-prohibitive computational time, different heuristics, architectures and information filtering techniques are studied with different datasets. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the sequential recommender systems development. This research focuses on models for managing sequential recommendation supported by session-based recommendation. This paper presents the characterization in the specific theme and the state-of-the-art towards study object of the thesis: the adaptive recommendation to mitigate the information overload in online environments.

2022

An Ontology for Fire Building Evacuation

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

Publication
Proceedings of Sixth International Congress on Information and Communication Technology - ICICT 2021, London, Volume 3

Abstract

2019

A multi-agent system for recommending fire evacuation routes in buildings, based on context and IoT

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

Publication
Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection - International Workshops of PAAMS 2019, Ávila, Spain, June 26-28, 2019, Proceedings

Abstract
The herein proposed research project brings together the area of the multi-agent recommender systems and the IoT and aims to study the extent to which a context-based multi-agent recommender system can contribute to improving efficiency in the evacuation of buildings under a fire emergency, recommending the most adequate and efficient evacuation routes in real time. © Springer Nature Switzerland AG 2019.