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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
008
Publications

2021

Embedding Traffic Network Characteristics Using Tensor for Improved Traffic Prediction

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

Publication
IEEE Transactions on Intelligent Transportation Systems

Abstract

2021

Novelty Detection in Physical Activity

Authors
Leite, B; Abdalrahman, A; Castro, J; Frade, J; Moreira, J; Soares, C;

Publication
Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, Volume 2, Online Streaming, February 4-6, 2021.

Abstract

2021

An Ensemble of Autonomous Auto-Encoders for Human Activity Recognition

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

Publication
Neurocomputing

Abstract

2021

An analysis of Monte Carlo simulations for forecasting software projects

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

Publication
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

Authors
Seca, D; Moreira, JM;

Publication
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.

Supervised
thesis

2021

Delivery Forecasting in Project Management

Author
Henrique Miguel Bastos Goncalves

Institution
UP-FEUP

2021

Implementing Hadoop distributed file system (hdfs) Cluster for BI Solution

Author
Jorge Afonso Barandas Queirós

Institution
UP-FEUP

2021

Process discovery in project management: process and sequence mining approaches

Author
José Alejandro Briones Romero

Institution
UP-FEUP

2021

Transportation Mode Detection for Real Mobile Crowdsourced Datasets

Author
Akilu Rilwan Muhammad

Institution
UP-FEUP

2021

Suggesting Human Resources for Project Tasks

Author
Ana Sá e Sousa Carneiro da Silva

Institution
UP-FEUP