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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Branco, Beatriz C. Barbosa, Ramiro S. Porto Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP/IPP Porto Portugal

ISBN: (纸本)9783031810824

The automotive industry is one of the biggest industries nowadays, representing almost 80% of the projects in Capgemini eng.neering Portugal, where this work was developed. In this paper, a new approach for automated tests in this sector is presented, based on the application of Deep Learning (DL) algorithms. Experimental results show that the new solution reduces the frameworks needed for testing, as well as the human interaction, improving the efficiency in HMI testing for the company. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Automated tests Automotive industry Deep learning

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Retrieval-Augmented Generation Based Assistant: A Smart Home Case Study 23rd

Retrieval-Augmented Generation Based Assistant: A Smart Home...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Ribeiro, Bruno Gomes, Luis Vale, Zita GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031734991

Smart homes are starting to become more present in our daily lives. Smart homes are useful for enabling energy management services and reducing monetary costs of the consumers. Despite the increasing research in these aspects of the smart home, few actually have in mind the interactivity of the system they propose. Therefore, this paper, an extension of a previous work, proposes a new human-machine interface based on multi-agent systems and natural language models with retrieval-augmented generation techniques for smart homes to facilitate the consumers adaptation to this type of systems. The assistant enables the user to control the house and provides him with the information he needs, even when a problem within the system occurs. Through the experiment the assistant showed to be flexible, able to provide answers to questions outside of the smart home system domain and it increases the scalability of the system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart homes

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Benchmarking Large Language Models for Multi-agent Systems: A Comparative Analysis of AutoGen, CrewAI, and TaskWeaver 22nd

Benchmarking Large Language Models for Multi-agent Systems: ...

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22nd International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Barbarroxa, Rafael Gomes, Luis Vale, Zita GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory Polytechnic of Porto R. Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031704147

This paper presents the benchmarking of three multi-agent systems powered by large language models. The paper presents a comparative analysis of AutoGen, CrewAI, and TaskWeaver. Nowadays, large language models have emerged as powerful tools able to assist users in various areas. The integration of large language models into multi-agent systems increases their potential for collaborative problem-solving. This study focuses on a case study involving a machine learning code generation task which is used to evaluate the framework’s performance. To assess the performance of the solutions, it is requested to create energy forecasting models using the same dataset as the base. After producing the code, a new dataset is used to test the model performance using the root mean square error. The three solutions were able to provide results using multiple large language models. The best result was achieved by TaskWeaver using GPT-3.5, with an error of 25.04. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi agent systems

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A Smart Contract-Based Peer-to-Peer Energy Trading System 6th

A Smart Contract-Based Peer-to-Peer Energy Trading System

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6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Abbasi, Mahmoud Faria, Pedro Gomes, Luis Prieto, Javier Vale, Zita BISITE Research Group University of Salamanca Salamanca37007 Spain GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal

ISBN: (纸本)9783031819278

The transition toward sustainable and decentralized energy systems has prompted the emergence of Peer-to-Peer (P2P) energy trading systems, challeng.ng traditional centralized models. This paper proposes a smart contract-based P2P energy trading system to foster a resilient and sustainable energy ecosystem. By integrating blockchain technology with P2P energy trading, we introduce trust, security, and transparency into energy transactions. Our solution employs an auction-based mechanism and blockchain oracles to address standardization challeng.s and enhance the accuracy of energy-related data. Additionally, an advanced identity management system ensures secure access to the trading system. Through smart contracts, energy producers and consumers eng.ge directly in transparent and decentralized transactions. Despite challeng.s such as regulatory uncertainties and scalability concerns, our solution offers a promising alternative for building a more inclusive and sustainable energy future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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An adaptive language model-based intelligent medication assistant for the decision support of antidepressant prescriptions

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Computers in Biology and Medicine 2025年 190卷 110065-110065页

作者： Silva, Regina Gomes, Luis Centro Hospitalar Universitário de Santo António Unidade Local de Saúde de Santo António Porto Portugal GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI – Intelligent Systems Associate Laboratory ISEP Polytechnic of Porto Porto Portugal

Background: Depression, a pervasive mental disorder affecting millions worldwide, requires a holistic and personalized approach to treatment. Combining therapy and antidepressant medication is crucial, but selecting the right medication remains a complex task due to the variety of options available, each with unique, contraindications and drug interactions. While intelligent clinical decision support systems exist, there is a gap in medication-specific solutions. This paper proposes an innovative intelligent medication assistant, leveraging retrieval-augmented generation and large language models to provide personalized recommendations considering patient records and current health situation. Method: The proposed assistant tailors recommendations based on individual context by injecting patient-specific documents into large language models, enhancing efficiency and minimizing adverse effects. Our approach adapts to various query formats and dynamically incorporates relevant sources, empowering healthcare providers to make informed decisions. The analysis covers 40 clinical cases of patients requiring psychiatric monitoring. The patient's basic biography, clinical context, and ongoing situation were presented for each model, followed by questions about antidepressant pharmacological treatment. Results: The case study used 40 mental health patient clinical cases, and the results showed that the proposed assistant represents a significant advancement for the health provider, enabling the support, fast search, and consultation of medication information leaflets in almost real-time. A total of ten large language models with two configurations each were evaluated. Two commercial models and eight open local models were used. Conclusions: The commercial model gpt4o had a total score of 4.10/5.00, while the open model llama3 had a total score of 2.92/5.00. The results showed that commercial models present better results than open models, providing more accurate answers, cons

关键词： Recommender systems

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A multidisciplinary approach to prevent student anxiety: a Toolkit for educators

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Procedia Computer Science 2025年 256卷 852-860页

作者： Alberto Freitas Bruno Costa Diogo Martinho Fatima Pais Ivone Duarte Constantino Martins Goreti Marreiros Rute Almeida MEDCIDS Faculty of Medicine University of Porto Porto 4200-450 Portugal CINTESIS@RISE University of Porto Porto 4200-450 Portugal Centro Hospitalar Universitário São João 4200-319 Porto GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto Porto 4249-015 Portugal & LASI – Intelligent Systems Associate Laboratory Guimarães 4800-058 Portugal Sucessos Criativos Lda São João da Madeira 3700-121 Portugal

The project WAI aims to tackle the growth of mental health disorders among the younger population, namely the most prevalent anxiety disorders. It intends to enhance educational strategies to help schools, teachers, and other professionals dealing with student anxiety. With that purpose, a suite of resources with a focus on mental health promotion in the school environment was organized by collecting existing tools and creating new elements for completeness. Information on mental health, AI, and AI use on mental health, classroom strategies, and best practices, structured for interdisciplinary application and use by both teachers and students, were considered. These resources were integrated into a Toolkit with a recommendation feature, which allows easy location and access to the most adequate tool for the specific need or educational challeng.. To maximize accessibility, resources will be translated into several languages. The Toolkit will guide the implementation of a capacitation formative program for educators that will be implemented based on its resources. The development of the project involves participatory design with contributions from educators, mental health experts, and AI technologists to ensure a comprehensive approach. The Toolkit is intended to provide a systematic framework for educators to promote sustainable mental health promotion practices and, when appropriate, to use AI in ways that are ethically sound, while fostering digital responsibility.

关键词： Mental Health Promotion Anxiety Disorders Educational Strategies AI in Education Toolkit Teacher Capacitation

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A methodology for energy key performance indicators analysis

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Energy Informatics 2021年第1期4卷 1-15页

作者： Faria, Pedro Lezama, Fernando Vale, Zita Khorram, Mahsa GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal Polytechnic of Porto Porto Portugal

With the advent of the smart grid era, the electrical grid is becoming a complex network in which different technologies coexist to bring benefits to both customers and operators. This paper presents a methodology for analyzing Key Performance Indicators (KPIs), providing knowledge about the performance and efficiency of energy systems, focusing on the demand side. In the first stage of the methodology, the baseline KPIs are calculated. In the second stage, all KPIs are updated to be compared with the baseline ones. In fact, due to the dynamic nature of players in a smart grid, this methodology plays a crucial role in the performance assessment. Moreover, the proper definition and selection of KPIs is usually a challeng.ng task since KPIs can be applied to evaluate diverse areas within a smart grid. Such areas include building energy efficiency, home communications, and smart metering deployment, just to mention a few. In the proposed methodology, the information obtained from a KPI can be driven to distinct aspects such as efficiency, environment, reliability, power quality, safety, security, or cost reduction. Through a case study from a real implementation of an energy system, we show how to assess energy consumption efficiency, thus improving energy management. © 2021, The Author(s).

关键词： Smart power grids

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A systematic review of explainability in computational intelligence for optimization

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Computer Science Review 2025年 57卷

作者： José Almeida João Soares Fernando Lezama Steffen Limmer Tobias Rodemann Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory ISEP Polytechnic of Porto Porto Portugal HRI-EU – Honda Research Institute Europe Offenbach Germany

This systematic review explores the need for explainability in computational intelligence methods for optimization, such as metaheuristic optimizers, including evolutionary algorithms and swarm intelligence. The work focuses on four aspects: (1) the contribution of Explainable AI (XAI) methods to interpreting metaheuristic performance; (2) the influence of problem features on search behavior and explainability; (3) the role of mathematical theory in providing transparent explanations; and (4) the potential of metaheuristics to enhance the explainability of AI models, such as machine learning (ML). XAI methods such as SHAP, LIME, and visualization techniques provide valuable insights into metaheuristic performance, while landscape analysis and quality diversity approaches reveal algorithm performance across different problem landscapes. The review also explores how metaheuristic algorithms can enhance the interpretability of ML models, turning black-box models into more transparent systems. The work moves on to proposing ”Explainergy,” a novel concept for integrating explainability into metaheuristic algorithms within the energy domain, enhancing the transparency and usability of optimization models. This review is a foundation for future research combining explainability with evolutionary computation and metaheuristic optimization to address real-world challeng.s in diverse fields, including energy systems.

关键词： Artificial intelligence Black box optimization Computational intelligence Explainability Explainergy Metaheuristics Systematic review

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Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods 20

Defining the Optimal Number of Demand Response Programs and ...

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20th International Conference on intelligent System Application to Power Systems, ISAP 2019

作者： Silva, Catia Faria, Pedro Vale, Zita GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781728131924

Nowadays, the data can be considered an asset when properly managed. An entity with the right tool to analyse the amount of data existent and withdraw crucial information will have the power to obliterate the competition. In the Energy sector, with Smart Grid introduction, small resources have more influence in the market through Demand Response and bidirectional communication. However, none of the actual business models is prepared to deal with the uncertainty related to these resources. The authors, in order to find a solution for this complex problem, proposed a methodology which the goal is to minimize operation costs and give fair compensation for resources who participate in the management of local markets. With this fair payment, it is expected continuous participation. Through clustering methods, remuneration groups are created. In the present paper, a study about the optimal number of clusters is performed. The information gives the Aggregator control in results of the following phases, understanding the impact in the remuneration of the resources. © 2019 IEEE.

关键词： Smart power grids

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Optimizing Energy Costs in Energy Communities: A Monthly Tariff Clustering Approach 22

Optimizing Energy Costs in Energy Communities: A Monthly Tar...

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22nd International Conference on intelligent Systems Applications to Power Systems, ISAP 2024

作者： Lezama, Fernando Bairrao, Diego Doria, Filipe Vale, Zita LASI - Polytechnic of Porto GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Porto Portugal

ISBN: (纸本)9798331531751

In collaborative energy communities, optimizing energy costs is a critical aspect of sustainable management. This article explores the potential benefits of applying clustering algorithms to vary retail tariffs monthly, aiming to reduce energy bills for the community as a whole. The article compares a traditional approach of applying the same tariff to all community members throughout the year with a novel approach of dynamically changing tariffs based on monthly clustering results. A case study is conducted, wherein energy bill costs per month are analyzed under different tariff scenarios utilizing k-means clustering. Results indicate that the proposed approach yields promising reductions in energy costs, up to 8.76% (1170.18 EUR) improvement compared to the traditional method. The study contributes valuable insights into the practical application of clustering in energy community management and highlights the potential for significant cost savings through dynamic tariff adjustments. © 2024 IEEE.

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