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About

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
001
Publications

2019

Generalizing Knowledge in Decentralized Rule-Based Models

Authors
Strecht, P; Mendes-Moreira, J; Soares, C;

Publication
Metasomatic Textures in Granites - Springer Mineralogy

Abstract

2018

Enhancing traffic model of big cities: Network skeleton & reciprocity

Authors
Bhanu, M; Chandra, J; Mendes Moreira, J;

Publication
10th International Conference on Communication Systems & Networks, COMSNETS 2018, Bengaluru, India, January 3-7, 2018

Abstract

2018

Updating a robust optimization model for improving bus schedules

Authors
Baghoussi, Y; Mendes Moreira, J; Emmerich, MTM;

Publication
10th International Conference on Communication Systems & Networks, COMSNETS 2018, Bengaluru, India, January 3-7, 2018

Abstract

2018

Agribusiness Intelligence: Grape Production Forecast Using Data Mining Techniques

Authors
de Oliveira, RC; Moreira, JM; Ferreira, CA;

Publication
Trends and Advances in Information Systems and Technologies - Volume 3 [WorldCIST'18, Naples, Italy, March 27-29, 2018].

Abstract
The agribusiness volatility is related to the uncertainty of the environment, rising demand, falling prices and new technologies. However, generation of agriculture data has increased over past years and can be used for a growing number of applications of data mining techniques in agriculture. The multidisciplinary approach of integrating computer science with agriculture will support the necessary decisions to be taken in order to mitigate risks and maximize profits. The present study analyzes different methods of regression applied in the study case of grapes production forecast. The selected methods were multivariate linear regression, regression trees, lasso and random forest. Their performance were compared against the predictions obtained by the company through the mean squared error and the coefficient of variation. The four regression methods used obtained better predictive results than the method used by the company with statistical significance < 0.5%. © Springer International Publishing AG, part of Springer Nature 2018.

2018

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

Authors
Pedroto, M; Jorge, A; Moreira, JM; Coelho, T;

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

Abstract

Supervised
thesis

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

2017

Novas perspetivas do Business Intelligence: criação de novos indicadores de avaliação

Author
Tânia Patrícia Serra Veloso

Institution
UP-FEP

2017

Leveraging Metalearning for Bagging Classifiers

Author
Fábio Hernâni dos Santos Costa Pinto

Institution
UP-FEUP

2017

Modelling the Age of Onset of TTR-FAP Patients using Familiar Clinical Data

Author
Maria José Gomes Pedroto

Institution
UP-FEUP