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Artificial Intelligence and Decision Support

At LIAAD, we work on the very strategic area of Data Science, which has an increasing interest worldwide and is critical to all areas of human activity. The huge amounts of collected data (Big Data) and the ubiquity of devices with sensors and/or processing power offer opportunities and challenges to scientists and engineers. Moreover, the demand for complex models for objective decision support is spreading in business, health, science, e-government and e-learning, which encourages us to invest in different approaches to modelling.

Our overall strategy is to take advantage of the data flood and diversification, and to invest in research lines that will help reduce the gap between collected and useful data, while offering diverse modelling solutions.

At LIAAD, our fundamental scientific principals are machine learning, statistics, optimisation and mathematics.

Latest News

"Conta-me Histórias” and Unexmin projects presented in the European Researchers' Night

Two projects developed by INESC TEC teams were presented in the European Researchers' Night, a initiative of the European Commission that takes place simultaneously in more than 30 countries and 300 cities throughout Europe.

16th November 2018

INESC TEC presented new technologies for Agriculture and Forest in Santarém

Five technological solutions, two used in the forest and three used in the agriculture, representing a total investment of around EUR 7.4 million, were exhibited in Agroglobal 2018, the largest agricultural fair in Portugal that took place in Valada do Ribatejo (Santarém) between 5 and 7 September.

01st October 2018

Computer Science

INESC TEC's researcher edits book in the Springer-verlag publisher group

Alberto Adrego Pinto, researcher of INESC TEC’s Artificial Intelligence and Decision Support Laboratory (LIAAD) and Full Professor at the Faculty of Sciences of the University of Porto, has just edited the third volume of the series “Modeling, Dynamics, Optimization and Bioeconomics”, alongside with David Zilberman from the UC Berkeley, California, USA.

16th August 2018

Computer Science

New book on data analysis with INESC TEC’s co- authorship

João Moreira, researcher from INESC TEC’s Laboratory of Artificial Intelligence and Decision Support (LIAAD) was the co-author of the book «A General Introduction to Data Analytics» of the publisher Wiley, in collaboration with André Carvalho and Tomás Horvath.

14th July 2018

Computer Science

INESC TEC's researcher joins the Springer-Verlag publisher group

Alberto Adrego Pinto, researcher of INESC TEC’s Artificial Intelligence and Decision Support Laboratory (LIAAD) and full professor at the Faculty of Sciences of the University of Porto, joins the publisher group of the exclusive series Springer Monographs in Mathematics (SMM) of the prestigious publisher Springer.

13th July 2018

Interest Topics
035

Featured Projects

FAST-manufacturing

Flexible And sustainable manufacturing

2018-2021

EAIA2018

11th Advanced School on Data Science for Big Data

2018-2018

Coop_India

Técnicas de análise de redes sociais para planeamento urbano

2018-2019

FLOWTEE

Desenvolvimento de um programa que monitorize automaticamente os níveis de bem-estar (ou felicidade) dos funcionários, a partir de dados disponíveis online

2018-2019

PERS_TOMI

Prestação de Serviços para desenvolvimento de um algoritmo de recomendação PERS como serviço PERSaaS , PERSoff, PERStune e PERSboard

2017-2019

PERSONA

PERSONALIZAÇÃO E GESTÃO DE INFORMAÇÃO BASEADA EM DADOS CLIENTE

2017-2019

RECAP

Research on European Children and Adults born Preterm

2017-2021

SmartFarming-1

Ferramenta avançada para operacionalização da agricultura de precisão

2016-2018

PANACea

Perfis para Anomalias Consumo

2016-2018

BI4UP2

Business Intelligence (BI) Tool

2016-2017

Dynamics2

Dynamics, optimization and modelling

2016-2019

CORAL-TOOLS-1

CORAL – Sustainable Ocean Exploitation: Tools and Sensors

2016-2018

MarineEye

MarinEye - A prototype for multitrophic oceanic monitoring

2015-2017

FOUREYES

TEC4Growth - RL FourEyes - Intelligence, Interaction, Immersion and Innovation for media industries

2015-2018

NanoStima-RL5

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2018

iMAN

iMAN - Intelligence for advanced Manufacturing systems

2015-2018

NanoStima-RL3

NanoSTIMA - Health data infrastructure

2015-2018

NanoStima-RL4

NanoSTIMA - Health Data Analysis & Decision

2015-2018

SMILES

SMILES - Smart, Mobile, Intelligent and Large scale Sensing and analytics

2015-2018

FOTOCATGRAF

Graphene-based semiconductor photocatalysis for a safe and sustainable water supply: an advanced technology for emerging pollutants removal

2015-2018

SEA

SEA-Sistema de ensino autoadaptativo

2015-2015

MAESTRA

Learning from Massive, Incompletely annotated, and Structured Data

2014-2017

BI4UP

Business Intelligence (BI) Tool

2014-2014

SIBILA

Towards Smart Interacting Blocks that Improve Learned Advice

2013-2015

SmartManufacturing

Smart Manufacturing and Logistics

2013-2015

SmartGrids

Smart Grids

2013-2015

Dynamics

Dynamics and Applications

2012-2015

e-Policy

Engineering for the Policy-making Life Cycle (ePolicy)

2011-2014

SIMULESP

Expert system to support network operator on real time decision

2011-2015

CRN

Trust-aware Automatic E-Contract Negotiation in Agent-based Adaptive Normative Environments

2010-2013

KDUS

Knowledge Discovery from Ubiquitous Data Streams

2010-2013

Palco3.0

Intelligent Web system to support the management of a social network on music

2008-2011

Argos-1

Wind power forecasting system

2008-2012

MOREWAQ

Monitoring and Forecasting of Water Quality Parameters

2008-2011

ORANKI

Resource-bounded outlier detection

2008-2011

Team
Publications

LIAAD Publications

View all Publications

2018

Mr. Silva and patient zero: A medical social network and data visualization information system

Authors
Gonçalves, PCT; Moura, AS; Cordeiro, MNDS; Campos, P;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Detection of Patient Zero is an increasing concern in a world where fast international transports makes pandemia a Public Health issue and a social fear, in cases such as Ebola or H5N1. The development of a medical social network and data visualization information system, which would work as an interface between the patient medical data and geographical and/or social connections, could be an interesting solution, as it would allow to quickly evaluate not only individuals at risk but also the prospective geographical areas for imminent contagion. In this work we propose an ideal model, and contrast it with the status quo of present medical social networks, within the context of medical data visualization. From recent publications, it is clear that our model converges with the identified aspects of prospective medical networks, though data protection is a key concern and implementation would have to seriously consider it. © Springer Nature Switzerland AG 2018.

2018

Multi-label classification from high-speed data streams with adaptive model rules and random rules

Authors
Sousa, R; Gama, J;

Publication
Progress in Artificial Intelligence

Abstract

2018

Co-training study for online regression

Authors
Sousa, R; Gama, J;

Publication
Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018, Pau, France, April 09-13, 2018

Abstract
This paper describes the development of a Co-training (semi-supervised approach) method that uses multiple learners for single target regression on data streams. The experimental evaluation was focused on the comparison between a realistic supervised scenario (all unlabelled examples are discarded) and scenarios where unlabelled examples are used to improve the regression model. Results present fair evidences of error measure reduction by using the proposed Co-training method. However, the error reduction still is relatively small. © 2018 Authors.

2018

Preference rules for label ranking: Mining patterns in multi-target relations

Authors
de Sa, CR; Azevedo, P; Soares, C; Jorge, AM; Knobbe, A;

Publication
INFORMATION FUSION

Abstract
In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

2018

Using metalearning for parameter tuning in neural networks

Authors
Felix, C; Soares, C; Jorge, A; Ferreira, H;

Publication
Lecture Notes in Computational Vision and Biomechanics

Abstract
Neural networks have been applied as a machine learning tool in many different areas. Recently, they have gained increased attention with what is now called deep learning. Neural networks algorithms have several parameters that need to be tuned in order to maximize performance. The definition of these parameters can be a difficult, extensive and time consuming task, even for expert users. One approach that has been successfully used for algorithm and parameter selection is metalearning. Metalearning consists in using machine learning algorithm on (meta)data from machine learning experiments to map the characteristics of the data with the performance of the algorithms. In this paper we study how a metalearning approach can be used to obtain a good set of parameters to learn a neural network for a given new dataset. Our results indicate that with metalearning we can successfully learn classifiers from past learning tasks that are able to define appropriate parameters. © 2018, Springer International Publishing AG.

Supervised Theses

2017

APP Consumer Response: A.I. Modelling Towards Optimal Managerial Decisions in Mobile Marketing.

Author
Fabiane Valéria de Oliveira Bastos Valente

Institution
UP-FCUP

2017

Bridging Consumer Psychology with Augmented Reality: A Psychophysiological Approach.

Author
Mafalda Teles de Moura e Roxo Espírito Santo

Institution
UP-FEP

2017

R&D Dynamics with uncertainty in the production cost

Author
Joana Becker Paulo

Institution
UP-FCUP

2017

Applications to dynamical systems to immunology and to random exchange economies

Author
Yusuf Aliyu Ahmad

Institution
UP-FCUP

2017

Eating and drinking recognition for triggering smart reminders

Author
Diana Sousa Gomes

Institution
UP-FEUP

Facts & Figures

152National R&D Programmes (k€)

2016

0R&D Employees

2016

36Papers in indexed journals

2016