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Presentation

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
Computer Science and Engineering

Less common language varieties also have a place in the era of AI, as demonstrated by two INESC TEC papers presented at a top conference

It's hard to think of current technologies or innovations that do not resort to Language Models (LM) or Natural Language Processing (NLP). Their presence in various society domains - some with significant relevance, like the legal or healthcare sectors - raise issues (and concerns) that often end up focusing on the same question: are LM-based technologies reaching all communities? Recently, two scientific papers featuring INESC TEC - both accepted at AAAI, an A* conference - sought to address some of the challenges in this new era, which directly influence the Portuguese language.

28th February 2025

Computer Science and Engineering

Tell me what you're looking for and I'll tell you what you need. INESC TEC-Amazon collaboration optimises search engine results for special dates

The seasonality of search queries in search engines could be a factor for online businesses to consider if they seek to improve the ranking of their results. A new demo-paper featuring INESC TEC explored the creation of a database to present the Occasion-aware Recommender solution.

26th February 2025

Computer Science and Engineering

INESC TEC developed natural language processing resources for the Portuguese language

The main goal of the PTicola project was to expand and build new Natural Language Processing (NLP) capabilities for the Portuguese language. The results of this project - which include, for example, an English/European Portuguese translator and a PT-BR/PT-PT language variety identifier - address the gap in NLP resources available for PT-PT compared to PT-BR.

14th February 2025

Artificial Intelligence

The largest machine learning conference in Europe will take place in Porto and is now accepting papers

It is called ECML PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases -, and it is the largest European conference in machine learning. The event, promoted by INESC TEC, will take place between September 15 and 19, 2025, in Porto; the submission of papers is open until March.

09th January 2025

Artificial Intelligence

"Where do We Come From? Where are We Going?": that's how João Gama - one of the most-cited scientists in the world - said “goodbye” to his teaching activity

35 years separate the beginning and the end of the teaching career of João Gama, one of the most-cited scientists in the world. The INESC TEC researcher, who presented his Last Lecture on November 25, said “goodbye” to the classrooms of the Faculty of Economics of the University of Porto (FEP). The motto? "Where do We Come From? Where are we going?” – the culmination of a recognised academic career, particularly in the fields of Artificial Intelligence (AI) and Machine Learning.

28th November 2024

093

Featured Projects

CitiLink

CitiLink - Enhancing municipal transparency and citizen engagement through AI: from unstructured to structured data

2025-2026

OBSERVA

Optical Signals and Acoustic Surveillance Observatory

2025-2026

EnSafe

Enhancing Environmental Protection: Anomaly Detection in Waste Transportation using Network Science

2025-2026

TSP2Net

Time Series Privacy-Preserving: New Approaches via Complex Networks

2025-2026

NuClim

Nuclear observations to improve Climate research and GHG emission estimates

2024-2028

HALM

Humanitarian Accounting Logistics with Machine learning

2024-2024

AI4REALNET

AI for REAL-world NETwork operation

2023-2027

AIBOOST

Artificial intelligence for better opportunities and scientific progress towards trustworthy and human-centric digital environment

2023-2027

AzDIH

Azores Digital Innovation Hub on Tourism and Sustainability

2023-2025

PAPVI2

Previsão Avançada de Preços de Venda de Imóveis

2023-2025

PFAI4_4eD

Programa de Formação Avançada Industria 4 - 4a edição

2023-2023

StorySense

Reaching the Semantic Layers of Stories in Text

2023-2026

ATTRACT_DIH

Digital Innovation Hub for Artificial Intelligence and High-Performance Computing

2022-2025

Produtech_R3

Agenda Mobilizadora da Fileira das Tecnologias de Produção para a Reindustrialização

2022-2025

EMERITUS

Environmental crimes’ intelligence and investigation protocol based on multiple data sources

2022-2025

FAIST

Fábrica Ágil Inteligente Sustentável e Tecnológica

2022-2025

ADANET

Internet das Coisas Assistida por Drones

2022-2025

PFAI4_3ed

Programa de Formação Avançada Industria 4 - 3a edição

2022-2022

FORM_I40

Formação Indústria 4.0

2022-2022

DAnon

Supervised Deanonymization of Dark Web Traffic for Cybercrime Investigation

2022-2023

THEIA

Automated Perception Driving

2022-2023

City Analyser

An agnostic platform to analyse massive mobility patterns

2021-2023

HfPT

Health from Portugal

2021-2025

AgWearCare

Wearables para Monitorização das Condições de Trabalho no Agroflorestal

2021-2023

SADCoPQ

Sistema de Apoio à Decisão no Controlo Preditivo da Qualidade na Indústria Metalomecânica da Precisão

2021-2023

SIGIPRO

Sistema inteligente de gestão de processos habilitados espacialmente

2021-2023

DigitalBudget_VE

Aplicação computacional para orçamentação automática de postos de carregamento de VE

2021-2021

XPM

eXplainable Predictive Maintenance

2021-2024

SSPM

Student Success Prediction Model

2021-2022

OnlineAIOps

Online Artificial Intelligence for IT Operations

2021-2023

AI_Sov

AI Sovereignty

2021-2021

PORT XXI

Space Enabled Sustainable Port Services

2020-2022

Training4DS

Formação Avançada em Data Science - Altice Labs

2020-2020

PFAI4.0

Programa de Formação Avançada Industria 4.0

2020-2021

HumanE-AI-Net

HumanE AI Network

2020-2024

MetaFLow

A Meta Learning work-flow for a Low Code Platform

2020-2021

PAIQAFSR

Provision of advisory inputs and quality assurance of the final study report.

2020-2020

Continental FoF

Fábrica do Futuro da Continental Advanced Antenna

2020-2023

PAFML

Investigação e desenvolvimento para aplicação de Machine Learning a dados de pacientes com Paramiloidose

2020-2023

AIDA

Adaptive, Intelligent and Distributed Assurance Platform

2020-2023

SLSNA

Prestação de Serviços no ambito do projeto SKORR

2020-2021

MINE4HEALTH

Text mining e clinical decision-making

2020-2021

Text2Story

Extracting journalistic narratives from text and representing them in a narrative modeling language

2019-2023

T4CDTKC

Training 4 Cotec, Digital Transformation Knowledge Challenge - Elaboração de Programa de Formação “CONHECER E COMPREENDER O DESAFIO DAS TECNOLOGIAS DE TRANSFORMAÇÃO DIGITAL”

2019-2021

PROMESSA

PROject ManagEment intellingent aSSistAnt

2019-2023

NDTECH

NDtech 4.0 - Smart and Connected - Estudo e Caderno de Encargos

2019-2019

RISKSENS

Market Risk Sensitivities

2019-2020

RAMnet

Risk Assessment for Microfinance

2019-2021

HOUSEVALUE

Estimativa de Valor de Avaliação de Imóveis

2019-2019

MLABA

Machine Learn Based Adaptive Business Assurance

2019-2019

Humane_AI

Toward AI Systems That Augment and Empower Humans by Understanding Us, our Society and the World Around Us

2019-2020

Moveo

Prestação de serviços de investigação e desenvolvimento relativos ao sistema MOVEO

2019-2019

FIN-TECH

A FINancial supervision and TECHnology compliance training programme

2019-2021

FailStopper

Early failure detection of public transport vehicles in operational context

2018-2021

TerraAlva

Terr@Alva

2018-2019

MDG

Modelling, dynamics and games

2018-2022

NITROLIMIT

Life at the edge: define the boundaries of the nitrogen cycle in the extreme Antarctic environments

2018-2022

RUTE

Randtech Update and Test Environment

2018-2020

MaLPIS

Aprendizagem Automática para Deteção de Ataques e Identificação de Perfis Segurança na Internet

2018-2022

SKORR

Advancing the Frontier of Social Media Management Tools

2018-2021

FAST-manufacturing

Flexible And sustainable manufacturing

2018-2022

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

MDIGIREC

Context Recommendation in Digital Marketing

2017-2018

NEXT-NET

Next generation Technologies for networked Europe

2017-2019

RECAP

Research on European Children and Adults born Preterm

2017-2021

SmartFarming

Ferramenta avançada para operacionalização da agricultura de precisão

2016-2018

PANACea

Perfis para Anomalias Consumo

2016-2019

BI4UP2

Business Intelligence (BI) Tool

2016-2017

Dynamics2

Dynamics, optimization and modelling

2016-2019

CORAL-TOOLS

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

2015-2019

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

2025

Anomaly Detection in Pet Behavioural Data

Authors
Silva, I; Ribeiro, RP; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2023, PT II

Abstract
Pet owners are increasingly becoming conscious of their pet's necessities and are paying more attention to their overall wellness. The well-being of their pets is intricately linked to their own emotional and physical well-being. Some veterinary system solutions are emerging to provide proactive healthcare options for pets. One such solution offers the continuous monitoring of a pet's activity through accelerometer tracking devices. Based on data collected by this application, in this paper, we study different time aggregation and three unsupervised machine learning techniques to identify anomalies in pet behaviour data. Specifically, three algorithms, Isolation Forest, Local Outlier Factor, and K-Nearest Neighbour, with various thresholds to differentiate between normal and abnormal events. Results conducted on ten pets (five cats and five dogs) show that the most effective approach is to use daily data divided into periods. Moreover, the Local Outlier Factor is the best algorithm for detecting anomalies when prioritizing the identification of true positives. However, it also produces a high false positive ratio.

2025

Data Science for Fighting Environmental Crime

Authors
Barbosa, M; Ribeiro, C; Gomes, F; Ribeiro, RP; Gama, J;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2023, PT II

Abstract
The rise of environmental crimes has become a major concern globally as they cause significant damage to ecosystems, public health and result in economic losses. The availability of vast sensor data provides an opportunity to analyze environmental data proactively. This helps to detect irregularities and uncover potential criminal activities. This paper highlights the critical role played by machine learning (ML) and remote sensing technologies in the continuously evolving scenarios of environmental crime. By examining some case studies on detecting illegal fishing, illegal oil spills, illegal landfills, and illegal logging, we delve into the practical implementation of data-driven approaches for environmental crime detection. Our goal with this study is to provide an overview of the existing research in this area and foster the use of ML and data science techniques to enhance environmental crime detection.

2025

Parametric models for distributional data

Authors
Brito, P; Silva, APD;

Publication
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION

Abstract
We present parametric probabilistic models for numerical distributional variables. The proposed models are based on the representation of each distribution by a location measure and inter-quantile ranges, for given quantiles, thereby characterizing the underlying empirical distributions in a flexible way. Multivariate Normal distributions are assumed for the whole set of indicators, considering alternative structures of the variance-covariance matrix. For all cases, maximum likelihood estimators of the corresponding parameters are derived. This modelling allows for hypothesis testing and multivariate parametric analysis. The proposed framework is applied to Analysis of Variance and parametric Discriminant Analysis of distributional data. A simulation study examines the performance of the proposed models in classification problems under different data conditions. Applications to Internet traffic data and Portuguese official data illustrate the relevance of the proposed approach.

2025

Forecasting with Deep Learning: Beyond Average of Average of Average Performance

Authors
Cerqueira, V; Roque, L; Soares, C;

Publication
DISCOVERY SCIENCE, DS 2024, PT I

Abstract
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising performance into a single score, using metrics such as SMAPE. We hypothesize that averaging performance over all samples dilutes relevant information about the relative performance of models. Particularly, conditions in which this relative performance is different than the overall accuracy. We address this limitation by proposing a novel framework for evaluating univariate time series forecasting models from multiple perspectives, such as one-step ahead forecasting versus multi-step ahead forecasting. We show the advantages of this framework by comparing a state-of-the-art deep learning approach with classical forecasting techniques. While classical methods (e.g. ARIMA) are long-standing approaches to forecasting, deep neural networks (e.g. NHITS) have recently shown state-of-the-art forecasting performance in benchmark datasets. We conducted extensive experiments that show NHITS generally performs best, but its superiority varies with forecasting conditions. For instance, concerning the forecasting horizon, NHITS only outperforms classical approaches for multi-step ahead forecasting. Another relevant insight is that, when dealing with anomalies, NHITS is outperformed by methods such as Theta. These findings highlight the importance of evaluating forecasts from multiple dimensions.

2025

PrivateCTGAN: Adapting GAN for Privacy-Aware Tabular Data Sharing

Authors
Lopes, F; Soares, C; Cortez, P;

Publication
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2023, PT II

Abstract
This research addresses the challenge of generating synthetic data that resembles real-world data while preserving privacy. With privacy laws protecting sensitive information such as healthcare data, accessing sufficient training data becomes difficult, resulting in an increased difficulty in training Machine Learning models and in overall worst models. Recently, there has been an increased interest in the usage of Generative Adversarial Networks (GAN) to generate synthetic data since they enable researchers to generate more data to train their models. GANs, however, may not be suitable for privacy-sensitive data since they have no concern for the privacy of the generated data. We propose modifying the known Conditional Tabular GAN (CTGAN) model by incorporating a privacy-aware loss function, thus resulting in the Private CTGAN (PCTGAN) method. Several experiments were carried out using 10 public domain classification datasets and comparing PCTGAN with CTGAN and the state-of-the-art privacy-preserving model, the Differential Privacy CTGAN (DP-CTGAN). The results demonstrated that PCTGAN enables users to fine-tune the privacy fidelity trade-off by leveraging parameters, as well as that if desired, a higher level of privacy.

Facts & Figures

72Researchers

2016

3Book Chapters

2020

29Senior Researchers

2016