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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
006
Publicações

2020

A Study on Hyperparameter Configuration for Human Activity Recognition

Autores
Crarcia, KD; Carvalho, T; Mendes Moreira, J; Cardoso, JMP; de Carvalho, ACPLF;

Publicação
14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) - Seville, Spain, May 13-15, 2019, Proceedings

Abstract
Human Activity Recognition is a machine learning task for the classification of human physical activities. Applications for that task have been extensively researched in recent literature, specially due to the benefits of improving quality of life. Since wearable technologies and smartphones have become more ubiquitous, a large amount of information about a person’s life has become available. However, since each person has a unique way of performing physical activities, a Human Activity Recognition system needs to be adapted to the characteristics of a person in order to maintain or improve accuracy. Additionally, when smartphones devices are used to collect data, it is necessary to manage its limited resources, so the system can efficiently work for long periods of time. In this paper, we present a semi-supervised ensemble algorithm and an extensive study of the influence of hyperparameter configuration in classification accuracy. We also investigate how the classification accuracy is affected by the person and the activities performed. Experimental results show that it is possible to maintain classification accuracy by adjusting hyperparameters, like window size and window overlap, depending on the person and activity performed. These results motivate the development of a system able to automatically adapt hyperparameter settings for the activity performed by each person. © 2020, Springer Nature Switzerland AG.

2020

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

2020

Reconciling Predictions in the Regression Setting: An Application to Bus Travel Time Prediction

Autores
Moreira, JM; Baratchi, M;

Publicação
Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings

Abstract
In different application areas, the prediction of values that are hierarchically related is required. As an example, consider predicting the revenue per month and per year of a company where the prediction of the year should be equal to the sum of the predictions of the months of that year. The idea of reconciliation of prediction on grouped time-series has been previously proposed to provide optimal forecasts based on such data. This method in effect, models the time-series collectively rather than providing a separate model for time-series at each level. While originally, the idea of reconciliation is applicable on data of time-series nature, it is not clear if such an approach can also be applicable to regression settings where multi-attribute data is available. In this paper, we address such a problem by proposing Reconciliation for Regression (R4R), a two-step approach for prediction and reconciliation. In order to evaluate this method, we test its applicability in the context of Travel Time Prediction (TTP) of bus trips where two levels of values need to be calculated: (i) travel times of the links between consecutive bus-stops; and (ii) total trip travel time. The results show that R4R can improve the overall results in terms of both link TTP performance and reconciliation between the sum of the link TTPs and the total trip travel time. We compare the results acquired when using group-based reconciliation methods and show that the proposed reconciliation approach in a regression setting can provide better results in some cases. This method can be generalized to other domains as well. © 2020, The Author(s).

2019

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

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

Publicação
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

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

Publicação
Sensors

Abstract

Teses
supervisionadas

2019

Throughput Forecasting for Crowdsourced Text-Enrichment

Autor
Inês Isabel Correia Gomes

Instituição
UP-FEUP

2019

Sistemas de Recomendação para percursos culturais

Autor
Francisca Reis Rodrigues

Instituição
UP-FEUP

2019

From Binary to Multi-Class Divisions: improvements on Hierarchical Divisive Human Activity Recognition

Autor
Tomás Vieira Caldas

Instituição
UP-FEUP

2019

Data mining tool for Sports analytics

Autor
José Carlos Milheiro Soares Coutinho

Instituição
UP-FEUP

2019

Time-To-Event Prediction

Autor
Maria José Gomes Pedroto

Instituição
UP-FEUP