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About

Rui S. Moreira, Moimenta da Beira, 1969; graduate (Systems and Computers) and MSc (Telecommunications) both in Electrical and Computers Engineering from Faculdade Engenharia Universidade Porto (FEUP), Portugal, respectively in 1992 and 1995. PhD in Computer Science from Faculty of Applied Sciences, Lancaster University, UK, 2003. Currently he is a lecturer at Universidade Fernando Pessoa (UFP) and also a researcher at Instituto de Engenharia de Sistemas e Computadores do Porto (INESC Porto) since 1996. His main research interests include middleware and software architectures for dynamically adaptable distributed and ubiquitous systems such as distributed Digital Libraries and Learning Systems. Emails: rmoreira@ufp.pt, rjm@inescporto.pt.

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Publications

2019

Absenteeism Prediction in Call Center Using Machine Learning Algorithms

Authors
de Oliveira, EL; Torres, JM; Moreira, RS; de Lima, RAF;

Publication
Advances in Intelligent Systems and Computing

Abstract
Absenteeism is a major problem faced particularly by companies with a large number of employees. Therefore, the existence of absenteeism prediction tools is essential for such companies depending on intensive human-resources. This paper focuses on using machine learning technologies for predicting the absences of employees from work. More precisely, a few prediction models were tuned and tested with 241 features extracted from a population of 13.805 employees. This target population was sampled from the help desk work force of a major Brazilian phone company. The features were extracted from the profile of the help desk agents and then filtered by processes of correlation and feature selection. The selected features were then used to compare absenteeism prediction given by different classification algorithm (cf. Random Forest, Multilayer Perceptron, Support Vector Machine, Naive Bayes, XGBoost and Long Short Term Memory). The parameterization of these ML models was also studied to reach the classifier best suited for the prediction problem. Such parameterizations were tuned through the use of evolutionary algorithms, from which considerable precision was reached, the best being 72% (XGBoost) and 71% (Random Forest). © 2019, Springer Nature Switzerland AG.

2019

Automatic forest fire detection based on a machine learning and image analysis pipeline

Authors
Alves, J; Soares, C; Torres, JM; Sobral, P; Moreira, RS;

Publication
Advances in Intelligent Systems and Computing

Abstract
Forest fires can have devastating consequences if not detected and fought before they spread. This paper presents an automatic fire detection system designed to identify forest fires, preferably, in their early stages. The system pipeline processes images of the forest environment and is able to detect the presence of smoke or flames. Additionally, the system is able to produce an estimation of the area under ignition so that its size can be evaluated. In the process of classification of a fire image, one Deep Convolutional Neural Network was used to extract, from the images, the descriptors which are then applied to a Logistic Regression classifier. At a later stage of the pipeline, image analysis and processing techniques at color level were applied to assess the area under ignition. In order to better understand the influence of specific image features in the classification task, the organized dataset, composed by 882 images, was associated with relevant image metadata (eg presence of flames, smoke, fog, clouds, human elements). In the tests, the system obtained a classification accuracy of 94.1% in 695 images of daytime scenarios and 94.8% in 187 images of nighttime scenarios. It presents good accuracy in estimating the flame area when compared with other approaches in the literature, substantially reducing the number of false positives and nearly keeping the same false negatives stats. © Springer Nature Switzerland AG 2019.

2019

Improving ambient assisted living through artificial intelligence

Authors
Miguez, A; Soares, C; Torres, JM; Sobral, P; Moreira, RS;

Publication
Advances in Intelligent Systems and Computing

Abstract
The longevity of the population is the result of important scientific breakthroughs in recent years. However, living longer with quality, also brings new challenges to governments, and to the society as a whole. One of the most significant consequences will be the increasing pressure on the healthcare services. Ambient Assisted Living (AAL) systems can greatly improve healthcare scalability and reach while keeping the user in their home environment. The work presented in this paper specifies, implements, and validates a smart environment system that aggregates Automation and Artificial Intelligence (AI). The specification includes a reference architecture, composed by three modules, whose tasks are to automate and standardize the collection of data, to relate and give meaning to that data and to learn from it. The system is able to identify daily living activities with different levels of complexity using a temporal logic. It enables a real time response to emergency situations and also a long term analysis of the user daily routine useful to induce healthier lifestyles. The implementation addresses the applications and techniques used in the development of a functional prototype. To demonstrate the system operation three use cases with increasing levels of complexity are proposed and validated. A discussion on related projects is also included, specifically on automation applications, Knowledge Representation (KR) and Machine Learning (ML). © Springer Nature Switzerland AG 2019.

2019

Técnicas de Aprendizado de Máquina Aplicadas na Previsão de Produtividade de Operadores de Centros de Teleatendimento

Authors
OLIVEIRA, E; Manuel Torres, J; Silva Moreira, R; França Lima, R;

Publication
Anais do 14º Simpósio Brasileiro de Automação Inteligente

Abstract

2018

Color algorithm for flame exposure (CAFE) [Color Algorithm for Flame Exposure (CAFE)]

Authors
Alves, J; Soares, C; Torres, J; Sobral, P; Moreira, RS;

Publication
Iberian Conference on Information Systems and Technologies, CISTI

Abstract
Panoramic or aerial images can be acquired with some easiness and cover vast tracts of territory to be used in fire detection. The analysis of these images, in particular based on color and threshold indices, can be very interesting computationally when applied in real time systems and collected, for example, through drones or watchtowers. This paper presents a solution designated Color Algorithm for Flame Exposure (CAFE), which significantly improves an existing method (cf. Forest Fire Detection Index - FFDI) in flame detection, based on daylight images, in mixed Mediterranean landscape, containing vegetation, buildings, burning areas, land, etc. The CAFE approach, presented, adds a parameterizable transformation of the image into the Lab color space. This approach was tested in four distinct scenarios, significantly reducing false positives and maintaining an equivalent level of false negatives when compared to the FFDI approach. © 2018 AISTI.