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Detalhes

Detalhes

  • Nome

    Luís Torgo
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2008
007
Publicações

2020

Analysis and Detection of Unreliable Users in Twitter: Two Case Studies

Autores
Guimaraes, N; Figueira, A; Torgo, L;

Publicação
Communications in Computer and Information Science - Knowledge Discovery, Knowledge Engineering and Knowledge Management

Abstract

2020

Wise Sliding Window Segmentation: A Classification-Aided Approach for Trajectory Segmentation

Autores
Etemad, M; Etemad, Z; Soares, A; Bogorny, V; Matwin, S; Torgo, L;

Publicação
Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Ottawa, ON, Canada, May 13-15, 2020, Proceedings

Abstract

2020

Knowledge-based Reliability Metrics for Social Media Accounts

Autores
Guimarães, N; Figueira, A; Torgo, L;

Publicação
Proceedings of the 16th International Conference on Web Information Systems and Technologies

Abstract

2020

Understanding the Response of Nitrifying Communities to Disturbance in the McMurdo Dry Valleys, Antarctica

Autores
Monteiro, M; Baptista, MS; Seneca, J; Torgo, L; Lee, CK; Cary, SC; Magalhaes, C;

Publicação
MICROORGANISMS

Abstract
Polar ecosystems are generally limited in nitrogen (N) nutrients, and the patchy availability of N is partly determined by biological pathways, such as nitrification, which are carried out by distinctive prokaryotic functional groups. The activity and diversity of microorganisms are generally strongly influenced by environmental conditions. However, we know little of the attributes that control the distribution and activity of specific microbial functional groups, such as nitrifiers, in extreme cold environments and how they may respond to change. To ascertain relationships between soil geochemistry and the ecology of nitrifying microbial communities, we carried out a laboratory-based manipulative experiment to test the selective effect of key geochemical variables on the activity and abundance of ammonia-oxidizing communities in soils from the McMurdo Dry Valleys of Antarctica. We hypothesized that nitrifying communities, adapted to different environmental conditions within the Dry Valleys, will have distinct responses when submitted to similar geochemical disturbances. In order to test this hypothesis, soils from two geographically distant and geochemically divergent locations, Miers and Beacon Valleys, were incubated over 2 months under increased conductivity, ammonia concentration, copper concentration, and organic matter content. Amplicon sequencing of the 16S rRNA gene and transcripts allowed comparison of the response of ammonia-oxidizing Archaea (AOA) and ammonia-oxidizing Bacteria (AOB) to each treatment over time. This approach was combined with measurements of (NH4+)-N-15 oxidation rates using N-15 isotopic additions. Our results showed a higher potential for nitrification in Miers Valley, where environmental conditions are milder relative to Beacon Valley. AOA exhibited better adaptability to geochemical changes compared to AOB, particularly to the increase in copper and conductivity. AOA were also the only nitrifying group found in Beacon Valley soils. This laboratorial manipulative experiment provided new knowledge on how nitrifying groups respond to changes on key geochemical variables of Antarctic desert soils, and we believe these results offer new insights on the dynamics of N cycling in these ecosystems.

2019

Evaluation Procedures for Forecasting with Spatio-Temporal Data

Autores
Oliveira, M; Torgo, L; Costa, VS;

Publicação
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I

Abstract

Teses
supervisionadas

2019

Ensembles for Time Series Forecasting

Autor
Vítor Manuel Araújo Cerqueira

Instituição
UP-FEUP

2019

Predictive Analytics for Dependent Data

Autor
Mariana Rafaela Figueiredo Ferreira de Oliveira

Instituição
UP-FCUP

2019

Analyzing and Developing Indicators for Building an Automatic Detector of Unreliable Information in Social Media

Autor
Nuno Ricardo Pinheiro da Silva Guimarães

Instituição
UP-FCUP

2017

Prediction and Ranking of Highly Popular Web Content

Autor
Nuno Miguel Pereira Moniz

Instituição
IES_Outra

2017

Utility-based Predictive analytics

Autor
Paula Alexandra de Oliveira Branco

Instituição
UP-FCUP