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About

Areas of research:

- Knowledge discovery

  • Supervised learning   
  • Multiple predictive models
  • Applied knowledge discovery

- Intelligent transportation systems

  • Planning and operations of public transports

Interest
Topics
Details

Details

  • Name

    João Mendes Moreira
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st January 2011
002
Publications

2019

Impact of Genealogical Features in Transthyretin Familial Amyloid Polyneuropathy Age of Onset Prediction

Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;

Publication
Practical Applications of Computational Biology and Bioinformatics, 12th International Conference, PACBB 2018, Toledo, Spain, 20-22 May, 2018.

Abstract

2019

Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders

Authors
Gomes, D; Mendes Moreira, J; Sousa, I; Silva, J;

Publication
Sensors

Abstract

2019

Automatic Switching Between Video and Audio According to User’s Context

Authors
Ferreira, PJS; Cardoso, JMP; Moreira, JM;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Smartphones are increasingly present in human’s life. For example, for entertainment many people use their smartphones to watch videos or listen to music. Many users, however, stream or play videos with the intention to only listen to the audio track. This way, some battery energy, which is critical to most users, is unnecessarily consumed thus and switching between video and audio can increase the time of use of the smartphone between battery recharges. In this paper, we present a first approach that, based on the user context, can automatically switch between video and audio. A supervised learning approach is used along with the classifiers K-Nearest Neighbors, Hoeffding Trees and Naive Bayes, individually and combined to create an ensemble classifier. We investigate the accuracy for recognizing the context of the user and the overhead that this system can have on the smartphone energy consumption. We evaluate our approach with several usage scenarios and an average accuracy of 88.40% was obtained for the ensemble classifier. However, the actual overhead of the system on the smartphone energy consumption highlights the need for researching further optimizations and techniques. © 2019, Springer Nature Switzerland AG.

2019

An Efficient Scheme for Prototyping kNN in the Context of Real-Time Human Activity Recognition

Authors
Ferreira, PJS; Magalhães, RMC; Garcia, KD; Cardoso, JMP; Moreira, JM;

Publication
Intelligent Data Engineering and Automated Learning - IDEAL 2019 - 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part I

Abstract

2019

Energy Efficient Smartphone-Based Users Activity Classification

Authors
Magalhães, RMC; Cardoso, JMP; Moreira, JM;

Publication
Progress in Artificial Intelligence, 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3-6, 2019, Proceedings, Part II.

Abstract
Nowadays most people carry a smartphone with built-in sensors (e.g., accelerometers, gyroscopes) capable of providing useful data for Human Activity Recognition (HAR). Machine learning classification methods have been intensively researched and developed for HAR systems, each with different accuracy and performance levels. However, acquiring sensor data and executing machine learning classifiers require computational power and consume energy. As such, a number of factors, such as inadequate preprocessing, can have a negative impact on the overall HAR performance, even on high-end handheld devices. While high accuracy can be extremely important in some applications, the device’s battery life can be highly critical to the end-user. This paper is focused on the k-nearest neighbors’ algorithm (kNN), one of the most used algorithms in HAR systems, and research and develop energy-efficient implementations for mobile devices. We focus on a kNN implementation based on Locality-Sensitive Hashing (LSH) with a significant positive impact on the device’s battery life, fully integrated into a mobile HAR Android application able to classify human activities in real-time. The proposed kNN implementation was able to achieve execution time reductions of 50% over other versions of kNN with average accuracy of 96.55% when considering 8 human activities. © 2019, Springer Nature Switzerland AG.

Supervised
thesis

2018

Efeitos da Integração Óptima de Sistemas de Armazenamento de Energia na Operação da Rede de Transmissão Portuguesa

Author
André Filipe Porelo da Silva

Institution
UP-FEUP

2018

Safety features for unmanned maritime vehicles

Author
André Manuel Matos Leite

Institution
UP-FEUP

2018

Antenna Design for Underwater Applications

Author
Oluyomi Aboderin

Institution
INESCTEC

2017

2016_N76 - Tecnologias e modelos de suporte a analytics sobre séries temporais

Author
Paulo Manuel da Silva Faria

Institution
UP-FEUP

2017

Comparing Bus Travel Time Prediction Using AVL and Smart Card Data

Author
Fernando Aparecido dos Santos Silva

Institution
UP-FEUP