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Sobre

Áreas de investigação:

- Descoberta de conhecimento

  • Aprendizagem supervisionada   
  • Modelos múltiplos preditivos
  • Descoberta de conhecimento aplicada

- Sistemas inteligentes de transportes

  • Planeamento e operações de transportes públicos

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    João Mendes Moreira
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2011
007
Publicações

2021

Embedding Traffic Network Characteristics Using Tensor for Improved Traffic Prediction

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

Publicação
IEEE Transactions on Intelligent Transportation Systems

Abstract

2021

An Ensemble of Autonomous Auto-Encoders for Human Activity Recognition

Autores
Garcia, KD; de Sa, CR; Poel, M; Carvalho, T; Mendes Moreira, J; Cardoso, JMP; de Carvalho, ACPLF; Kok, JN;

Publicação
Neurocomputing

Abstract

2021

An analysis of Monte Carlo simulations for forecasting software projects

Autores
Miranda, P; Faria, JP; Correia, FF; Fares, A; Graça, R; Moreira, JM;

Publicação
SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, Republic of Korea, March 22-26, 2021

Abstract
Forecasts of the effort or delivery date can play an important role in managing software projects, but the estimates provided by development teams are often inaccurate and time-consuming to produce. This is not surprising given the uncertainty that underlies this activity. This work studies the use of Monte Carlo simulations for generating forecasts based on project historical data. We have designed and run experiments comparing these forecasts against what happened in practice and to estimates provided by developers, when available. Comparisons were made based on the mean magnitude of relative error (MMRE). We did also analyze how the forecasting accuracy varies with the amount of work to be forecasted and the amount of historical data used. To minimize the requirements on input data, delivery date forecasts for a set of user stories were computed based on takt time of past stories (time elapsed between the completion of consecutive stories); effort forecasts were computed based on full-time equivalent (FTE) hours allocated to the implementation of past stories. The MMRE of delivery date forecasting was 32% in a set of 10 runs (for different projects) of Monte Carlo simulation based on takt time. The MMRE of effort forecasting was 20% in a set of 5 runs of Monte Carlo simulation based on FTE allocation, much smaller than the MMRE of 134% of developers' estimates. A better forecasting accuracy was obtained when the number of historical data points was 20 or higher. These results suggest that Monte Carlo simulations may be used in practice for delivery date and effort forecasting in agile projects, after a few initial sprints. © 2021 ACM.

2021

Benchmark of Encoders of Nominal Features for Regression

Autores
Seca, D; Moreira, JM;

Publicação
Trends and Applications in Information Systems and Technologies - Volume 1, WorldCIST 2021, Terceira Island, Azores, Portugal, 30 March - 2 April, 2021.

Abstract
Mixed-type data is common in the real world. However, supervised learning algorithms such as support vector machines or neural networks can only process numerical features. One may choose to drop qualitative features, at the expense of possible loss of information. A better alternative is to encode them as new numerical features. Under the constraints of time, budget, and computational resources, we were motivated to search for a general-purpose encoder but found the existing benchmarks to be limited. We review these limitations and present an alternative. Our benchmark tests 16 encoding methods, on 15 regression datasets, using 7 distinct predictive models. The top general-purpose encoders were found to be Catboost, LeaveOneOut, and Target. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2021

Embedding Traffic Network Characteristics Using Tensor for Improved Traffic Prediction

Autores
Bhanu, M; Moreira, JM; Chandra, J;

Publicação
IEEE Trans. Intell. Transp. Syst.

Abstract

Teses
supervisionadas

2020

Time-To-Event Prediction

Autor
Maria José Gomes Pedroto

Instituição
UP-FEUP

2020

Multiboosting for regression

Autor
Nuno Miguel Rainho Valente

Instituição
UP-FEUP

2020

Next Generation Machine Learning Based Real Time Fraud Detection

Autor
Rui Filipe Laranjeira da Costa

Instituição
UP-FEUP

2020

Automatic definition and classification of points of interest of vehicles through floating car data

Autor
Tiago Alexandre de Sousa Dias da Silva

Instituição
UP-FEUP

2020

Techniques to deal with imbalanced data in multi-class problems: A review of existing methods

Autor
Vitor Miguel Saraiva Esteves

Instituição
UP-FEUP