XXIV Congresso da Associação Portuguesa de Investigação Operacional (IO2025)

Session

Session 3.4 - Artificial Intelligence and OR

Not scheduled

Room 4

37. Diferenciação de Espécies Bacterianas com Inteligência Artificial a partir de Compostos Orgânicos Voláteis

António Prata Cardoso (inesctec.pt)

Apresentação regular

As infeções associadas aos cuidados de saúde representam uma ameaça significativa à saúde pública, agravada pela sua resistência antimicrobiana. Métodos tradicionais de deteção apresentam várias limitações, incluindo baixa sensibilidade e custos elevados. Uma alternativa promissora é a deteção de compostos orgânicos voláteis (VOCs) emitidos por bactérias, que atuam como uma “impressão digital”...

112. Forecasting UK Socioeconomic Indicators under Political Uncertainty: Classical vs Deep Learning Approaches

Filipe R. Ramos (CEAUL - Universidade de Lisboa | CETRAD, FCST - Universidade Europeia)

Apresentação regular

Abstract
The growing uncertainty of the international economic and political landscape, accentuated by phenomena such as Brexit and the pandemic, highlights the importance of robust and interpretable decision-support tools. In this environment, operational research (OR) is crucial for designing sustainable and innovative solutions for both society and businesses. This study contributes to...

105. How integration of generative AI and optimization methods can accelerate product design and manufacturing process development?

Antonio Forno (Departamento de Engenharia Mecânica, Universidade de Coimbra, Coimbra, Portugal)

Apresentação regular

Current methods in product development and industrialization are rigid, resource-intensive, and reliant on expert intuition. Integrating Artificial Intelligence (AI), particularly Generative AI (GenAI), with combinatorial optimization presents significant potential to address these challenges. Such integration can enable data-driven, automated decision-making across the product lifecycle—from...

57. Reframing Signal Drift: An AI-Based Approach for Functionalized Graphene Sensors

Luís Guimarães

Apresentação regular

Graphene field-effect transistors (GFETs) are powerful tools for detecting a variety of biological and chemical substances, including ions, glucose, DNA, and proteins, due to their exceptional sensitivity to electric fields. Although non-functionalized GFETs have performed well, especially when combined with Artificial Intelligence (AI), this study centers on the use of functionalized GFETs....