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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

INESC TEC tool allows a personalised digital marketing

PushNews - Automated Cross-Channel Orchestration for Content Distribution is the name of the project promoted by the Laboratory of Artificial Intelligence and Decision Support (LIAAD) and by the Centre for Enterprise Systems Engineering (CESE) of INESC TEC that allows digital communication between content producers and consumers to be more effective and efficient.

27th February 2019

Computer Science

INESC TEC researcher wins an IBM award on Quantum Computing

Miguel Ramalho, collaborator of INESC TEC’s Laboratory of Artificial Intelligence and Decision Support (LIAAD) and a student of the Integrated Master in Informatics and Computing Engineering (MIEIC) of the Faculty of Engineering of the University of Porto (FEUP), won the “IBM Q Teach Me Quantum Challenge”, thus receiving a $7,000 prize (around EUR 6,000).

21st February 2019

Computer Science

Best Paper Award in Deep Learning goes to INESC TEC

The work entitled “Uso de técnicas de Saliency para Selección de Características” (in English: “Use of Saliency techniques for Features Selection”), written by Brais Cancela and João Gama, collaborators of INESC TEC’s Laboratory of Artificial Intelligence and Decision Support (LIAAD), alongside Verónica Bolón-Canedo and Amparo Alonso-Betanzos, was awarded the best paper award at the I Workshop in Deep Learning (DeepL 2018).

19th December 2018

Computer Science

INESC TEC helps Metro do Porto controlling failures

The occurrence of failures in public transport vehicles during their regular functioning is the cause of several losses, especially when they cause the interruption of the journey. In order to tackle this problem, INESC TEC proposes project FailStopper - Early failure detection of public transport vehicles in operational context, in partnership with Metro do Porto (MP), and has the participation of the researchers Rita P. Ribeiro and João Gama from the Laboratory of Artificial Intelligence and Decision Support (LIAAD).

29th November 2018

Computer Science

INESC TEC participates in Iberian Mathematical Meeting

A group of researchers from INESC TEC’s Laboratory of Artificial Intelligence and Decision Support (LIAAD) represented the institution in the 7th Iberian Mathematical Meeting (7IMM) that took place in Évora between 12 and 14 October.

19th November 2018

Interest Topics
033

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

PANACea

Perfis para Anomalias Consumo

2016-2019

BI4UP2

Business Intelligence (BI) Tool

2016-2017

Dynamics2

Dynamics, optimization and modelling

2016-2019

MarineEye

MarinEye - A prototype for multitrophic oceanic monitoring

2015-2017

FOUREYES

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

2015-2019

NanoStima-RL5

NanoSTIMA - Advanced Methodologies for Computer-Aided Detection and Diagnosis

2015-2019

iMAN

iMAN - Intelligence for advanced Manufacturing systems

2015-2019

NanoStima-RL3

NanoSTIMA - Health data infrastructure

2015-2019

NanoStima-RL4

NanoSTIMA - Health Data Analysis & Decision

2015-2019

SMILES

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

2015-2019

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

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

2019

Impact of genealogical features in transthyretin familial amyloid polyneuropathy age of onset prediction

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

Publication
Advances in Intelligent Systems and Computing

Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient’s attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features. © Springer Nature Switzerland AG 2019.

2019

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Authors
Júnior, AN; Silva, E; Gomes, AM; Soares, C; Oliveira, JF;

Publication
Expert Syst. Appl.

Abstract

2019

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Authors
Neuenfeldt Junior, A; Silva, E; Gomes, M; Soares, C; Oliveira, JF;

Publication
Expert Systems with Applications

Abstract
In this paper, we explore the use of reference values (predictors) for the optimal objective function value of hard combinatorial optimization problems, instead of bounds, obtained by data mining techniques, and that may be used to assess the quality of heuristic solutions for the problem. With this purpose, we resort to the rectangular two-dimensional strip-packing problem (2D-SPP), which can be found in many industrial contexts. Mostly this problem is solved by heuristic methods, which provide good solutions. However, heuristic approaches do not guarantee optimality, and lower bounds are generally used to give information on the solution quality, in particular, the area lower bound. But this bound has a severe accuracy problem. Therefore, we propose a data mining-based framework capable of assessing the quality of heuristic solutions for the 2D-SPP. A regression model was fitted by comparing the strip height solutions obtained with the bottom-left-fill heuristic and 19 predictors provided by problem characteristics. Random forest was selected as the data mining technique with the best level of generalisation for the problem, and 30,000 problem instances were generated to represent different 2D-SPP variations found in real-world applications. Height predictions for new problem instances can be found in the regression model fitted. In the computational experimentation, we demonstrate that the data mining-based framework proposed is consistent, opening the doors for its application to finding predictions for other combinatorial optimisation problems, in particular, other cutting and packing problems. However, how to use a reference value instead of a bound, has still a large room for discussion and innovative ideas. Some directions for the use of reference values as a stopping criterion in search algorithms are also provided. © 2018 Elsevier Ltd

2019

Generalizing Knowledge in Decentralized Rule-Based Models

Authors
Strecht, P; Mendes-Moreira, J; Soares, C;

Publication
Metasomatic Textures in Granites - Springer Mineralogy

Abstract

2019

Self Hyper-parameter Tuning for Stream Recommendation Algorithms

Authors
Veloso, B; Gama, J; Malheiro, B; Vinagre, J;

Publication
Metasomatic Textures in Granites - Springer Mineralogy

Abstract

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

2Concluded PhD theses

2016

329Turnover (k€)

2016

116EU Programmes (k€)

2016