ISEP - DM – Engenharia de Inteligência Artificial
Permanent URI for this collection
Browse
Browsing ISEP - DM – Engenharia de Inteligência Artificial by Title
Now showing 1 - 10 of 106
Results Per Page
Sort Options
- Adaptação automática de algoritmos de otimização metaheurísticaPublication . Carvalho, João Marcelo Fernandes de; Pinto, Tiago Manuel Campelos FerreiraA maioria dos problemas do mundo real tem uma multiplicidade de possíveis soluções. Além disso, usualmente, são encontradas limitações de recursos e tempo na resolução de problemas reais complexos e, por isso, frequentemente, não é possível aplicar um método determinístico na resolução desses problemas. Por este motivo, as meta-heurísticas têm ganho uma relevância significativa sobre os métodos determinísticos na resolução de problemas de otimização com múltiplas combinações. Ainda que as abordagens meta-heurísticas sejam agnósticas ao problema, os resultados da otimização são fortemente influenciados pelos parâmetros que estas meta-heurísticos necessitam para a sua configuração. Por sua vez, as melhores parametrizações são fortemente influenciadas pela meta-heurística e pela função objetivo. Por este motivo, a cada novo desenvolvimento é necessária uma otimização dos parâmetros das metas heurísticas praticamente partindo do zero. Assim, e, atendendo ao aumento da complexidade das meta-heurísticas e dos problemas aos quais estassão normalmente aplicadas, tem-se vindo a observar um crescente interesse no problema da configuração ótima destes algoritmos. Neste projeto é apresentada uma nova abordagem de otimização automática dos parâmetros de algoritmos meta-heurísticos. Esta abordagem não consiste numa pré-seleção estática de um único conjunto de parâmetros que será utilizado ao longo da pesquisa, como é a abordagem comum, mas sim na criação de um processo dinâmico, em que a parametrização é alterada ao longo da otimização. Esta solução consiste na divisão do processo de otimização em três etapas, forçando, numa primeira etapa um nível alto de exploração do espaço de procura, seguida de uma exploração intermédia e, na última etapa, privilegiando a pesquisa local focada nos pontos de maior potencial. De forma a permitir uma solução eficiente e eficaz, foram desenvolvidos dois módulos um Módulo de Treino e um Módulo de Otimização. No Módulo de Treino, o processo de fine-tuning é automatizado e, consequentemente, o processo de integração de uma nova meta-heurística ou uma nova função objetivo é facilitado. No Módulo de Otimização é usado um sistema multiagente para a otimização de uma dada função seguindo a abordagem de pesquisa proposta. Com base nos resultados obtidos através da aplicação de otimização por enxame de partículas e algoritmos genéticos a várias funções benchmark e a um problema real na área dos sistemas de energia, o Módulo de Treino permitiu automatizar o processo de fine-tuning e, consequentemente, facilitar o processo de introdução no sistema de uma nova meta-heurística ou de uma nova função relativa a um novo problema a resolver. Utilizando a abordagem de otimização proposta através do Módulo de Otimização, obtém-se uma maior generalização e os resultados são melhorados sem comprometer o tempo máximo para a otimização.
- Adversarial agent for synthetic data generation for phishing detectionPublication . CARDOSO, FRANCISCO FONSECA FERREIRA; Pereira, Isabel Cecília Correia da Silva Praça Gomes; Maia, Eva Catarina GomesPhishing attacks continue to be a significant security challenge, causing financial and reputational damage to organizations and individuals, with emails being the primary way for these attacks. While modern defenses continue to rely on phishing detection systems, their effectiveness is being challenged by the evolution of these attacks. Attackers are moving from generic emails to highly personalised and context-specific messages, which conventional models struggle to detect. The performance of these systems is mostly limited by the scarcity of specialised, domain-specific training data needed to recognise such threats. This thesis tries to address this gap by introducing CANDACE, a modular framework designed to generate context-aware synthetic email messages to train and improve these detection systems. The main innovation of CANDACE comes from its dual Knowledge Graph (KG) architecture, which gives the generation process a contextual foundation. The first KG maps external, real-world information about an organization, while the second models its internal structure, such as employees and projects. A Small Language Model (SLM) then uses the information of these KGs, with other important components, such as URL, to generate an email message that is contextually relevant to the domain of the organization. The contributions of this work include the complete design, end-to-end implementation, and validation of the CANDACE pipeline. A case study in the Public Administration sector presents the framework’s ability to produce convincing, context-aware synthetic messages. The findings confirm that contextual grounding is essential for creating better and more focused training data. This research shows the need to move beyond generic emails datasets, to build more resilient detection systems capable of detecting the more sophisticated and personalised phishing attacks.
- AI-based synthesis of bacterial colony evolution imagesPublication . SILVA, MIGUEL ÂNGELO FERRAZ DA; Martinho, Diogo Emanuel Pereira; Marreiros, Maria Goreti CarvalhoThe growing demand for safety and efficiency in healthcare highlights the importance of optimising sterilisation procedures, where delays or errors can compromise patient outcomes. In this context, microbiological analysis of agar plates is a fundamental step, as it allows the identification of microbial growth that may compromise sterilisation quality. However, traditional inspection methods are time-consuming and rely heavily on manual observation, which limits their scalability in clinical environments. Meanwhile, Artificial Intelligence has demonstrated strong potential in image analysis and forecasting, offering opportunities to enhance microbiological analysis and support decision-making in healthcare workflows. This dissertation addresses the problem of detecting and predicting the growth of bacterial colonies on agar plates. Anticipating how colonies evolve is essential to evaluate contamination levels, yet this task remains challenging due to the natural variability of growth patterns, the occurrence of overlapping colonies, and the diversity of experimental conditions that affect microbial behaviour. To tackle this problem, an integrated application was developed and structured into three main modules. The first is a detection module that applies the YOLO object detection architecture to identify bacterial colonies from agar plate images. The second is a synthetic forecasting module based on convolutional autoencoders capable of predicting future colony states from early observations. The third is a contamination analysis module that translates predictions into interpretable indicators such as colony count, average size, growth rate, and coverage. Together, these modules form a complete pipeline designed to combine visual fidelity with biological relevance. The results show that the system can detect colonies with high accuracy, achieving a Precision of 99.1%, a Recall of 91.7%, and an F1 score of 95.3%. In addition, the forecasting module generated realistic predictions of colony growth, and the contamination analysis provided meaningful metrics across different experimental conditions. The exploration of different temporal intervals revealed complementary trade-offs between predictive detail and biological plausibility, reinforcing the flexibility of the proposed methodology. The main conclusion of this dissertation is that Artificial Intelligence can be effectively applied to predict microbial growth in laboratory settings. By integrating detection, forecasting, and contamination analysis within a single framework, this work establishes a technological foundation that supports the transition to more intelligent sterilisation workflows and contributes to the broader vision of safe, efficient, and smart healthcare environments.
- Ai-driven emotion recognition for mental health diagnoses: Assessing mental health through emotional state evaluationPublication . PRETO, PEDRO MIGUEL PERES; Conceição, Luís Manuel Silva; Figueiredo, Ana Maria Neves Almeida BaptistaMental health conditions remain a concerning challenge across the globe, requiring timely and reliable approaches to correctly make accurate diagnoses and effective interventions. Traditional assessment methods often rely on subjective self-reports and clinical interviews, which may not always capture the full spectrum of an individual’s emotional state. In this context, computational techniques for emotion analysis provide a complementary perspective by identifying patterns in facial expressions, speech, and language. This dissertation evaluates the potential of multimodal emotional state analysis and its contribution to mental health assessment, through the development of a computational application. A systematic review was conducted to evaluate existing methodologies and highlight their strengths, limitations, and applicability in clinical contexts. Building on this review, the present work explores an integration of visual, vocal, textual patterns, assessing the contribution of their combined capacity to improve the consistency and depth of emotional interpretation. An analysis centered on methodological design was conducted by applying techniques such as preprocessing, fine-tuning, and data augmentation on the datasets to enhance the model’s capacity. Ethical and security considerations were also incorporated to strengthen system robustness and ensure responsible deployment in the market. The proposed solution consists of an artificial intelligence based multimodal system that integrates the analysis of emotions present in facial expressions, voice, and text patterns to provide a comprehensive assessment of the user’s emotional state. The application’s modular architecture enables real-time processing and the generation of clinical reports. The experimental validation of the system revealed promising results across several DSM-5 domains, the clinical reference manual that defines diagnostic criteria for mental disorders cases. High F1-scores were recorded in domains such as Anger (0.84) and Personality Functioning (0.87), while more subtle domains, such as Dissociation (0.43) and Repetitive Behaviors (0.52), revealed more modest performance. The overall analysis resulted in an observed agreement level of 71.9% and a Cohen’s Kappa of 0.42, indicating moderate agreement with the DSM-5. The findings underline the promise of computational emotion analysis as a supplementary tool for mental health professionals, while also emphasizing the importance of critical evaluation of its limitations and careful integration into clinical practice.
- AI-driven information retrieval system for candidate screeningPublication . Silva, Vasco Reid Ferreira da; Conceição, Luís Manuel da SilvaEfficient screening and evaluation in the recruitment process are tasks that demand substantial time and effort from Human Resources professionals. These processes often suffer from long waiting periods, inconsistent candidate evaluation, and the potential to overlook qualified candidates. In this context, leveraging state-of-the-art natural language processing architectures, specifically large language models (LLMs), holds significant promise. LLMs can generate evaluations using advanced prompt techniques to improve the accuracy and reliability of the output. This thesis researches the feasibility of employing 7 billion parameter LLMs in candidate screening to reduce response times, decrease workload, and improve evaluation consistency. The study involves a comparative analysis of various state-of-the-art large language models to identify those most suitable for this application. Additionally, it examines different prompt engineering techniques to optimize the performance of these models. A comprehensive analysis of the results is conducted to determine the most effective combinations of LLMs and prompt engineering techniques. This includes a two-way validation process, utilizing both the state-of-the-art GPT-4 model and manual human resources validation, to ensure the robustness and reliability of the findings. The outcomes of this thesis aim to enhance the quality of candidate screening by integrating LLMs into the process. Furthermore, this work aspires to provide valuable insights into the capabilities of 7 billion parameter large language models in the field of human resources and their application in real-world scenarios.
- Ajuste dinâmico de dificuldade em videojogos usando aprendizagem automáticaPublication . Felício, Jorge Emanuel Coelho Mendonça de Anciães; Faria, Luiz Felipe Rocha deIn the constantly evolving field of video games, traditional difficulty settings fail to accommodate the wide range of skill levels among players. The resulting mismatch between the player’s skill and the game’s challenge can make the game boring for skilled players or frustrating for less experienced ones, negatively affecting player engagement. Dynamic Difficulty Adjustment (DDA) seeks to resolve this issue by adapting the game’s difficulty in real time in response to the player’s performance. While advancements in artificial intelligence (AI), particularly machine learning (ML), have enabled more adaptive DDA systems, the full potential of certain advanced techniques or tools has yet to be explored. This thesis thus explores possible innovations in the integration of AI in DDA systems for video games. The research begins by reviewing the techniques used for DDA, focusing on methodologies such as player modeling, rule-based systems, and ML. Based on this research, potential areas for innovation were identified and the application of Deep Reinforcement Learning (DRL) in the Unity game development platform through the usage of the MLAgents toolkit was chosen as a promising approach for this research. Using this methodology, this research aims to implement a DDA system that adjusts a game’s difficulty based on the player’s skills, enhancing their engagement and maintaining a consistent challenge. This project has several critical phases of development, including the creation of a game prototype, data collection for model training, development and integration of the DDA system into the game prototype, and conducting an experiment comparing the prototype with DDA integrated with a version of the prototype that used traditional static difficulty scaling. The experiment conducted was done with 20 participants of varying skill levels and used a combination of collected gameplay metrics and a modified Game Experience Questionnaire (GEQ) survey to evaluate the DDA system’s effectiveness. The results showed that the DDA system demonstrated a statistically significant increase in the player engagement component and appropriately adjusted the difficulty to be harder for participants of higher skill. However, the system sometimes exhibited some issues with drastic adjustments in difficulty between levels, which led to a slightly lower Post-Game positive experience score compared to the static difficulty scaling system. Despite these fluctuations, the proposed system demonstrates the potential of the ML-Agents toolkit in implementing DDA with DRL in games made on the Unity platform. By identifying underexplored areas in the current literature and applying advanced techniques like DRL, this thesis aims to contribute to both academic research and game development regarding the approach to DDA in video games.
- ALMA: ALgorithm Modeling ApplicationPublication . Oliveira, Nuno André Lapa; Pereira, Isabel Cecília Correia da Silva Praça GomesAs of today, the most recent trend in information technology is the employment of large-scale data analytic methods powered by Artificial Intelligence (AI), influencing the priorities of businesses and research centers all over the world. However, due to both the lack of specialized talent and the need for greater compute, less established businesses struggle to adopt such endeavors, with major technological mega-corporations such as Microsoft, Facebook and Google taking the upper hand in this uneven playing field. Therefore, in an attempt to promote the democratization of AI and increase the efficiency of data scientists, this work proposes a novel no-code/low-code AI platform: the ALgorithm Modeling Application (ALMA). Moreover, as the state of the art of such platforms is still gradually maturing, current solutions often fail into encompassing security/safety aspects directly into their process. In that respect, the solution proposed in this thesis aims not only to achieve greater development and deployment efficiency while building machine learning applications but also to build upon others by addressing the inherent pitfalls of AI through a ”secure by design” philosophy.
- An intelligent hybrid recommender system improved with Association RulesPublication . Moreira, João Filipe Coelho; Santos, Joaquim Filipe Peixoto dosWith the popularization of the Internet and the maturation of associated technologies, the digital environment has evolved into a global marketplace facilitating the exchange of goods and services, commonly referred to as e-commerce. This market has experienced substantial growth due to the expansion of product catalogues and the rising demand for effective recommender systems that enhance user experience and boost the competitiveness of companies. This dissertation examines the current landscape of e-commerce recommender systems, analysing the techniques currently in use, their limitations, and evaluation methods. It also proposes a hybrid approach that integrates recommendation techniques with association rules derived from historical purchase data, assigning weights to balance the influence of each technique. The primary goal is to provide users with personalised and effective recommendations, leveraging the combination of established recommendation methods with association rules, to mitigate existing limitations. The effectiveness of the components in this hybrid approach is evaluated using standard metrics, supplemented by feedback from test users, which aids in adjusting the weights and analysing the relevance of the recommendations. The findings of this approach contribute to increased user satisfaction on e-commerce platforms, although the creation of meaningful association rules requires substantial amounts of data.
- Análise do Movimento dos Atletas em Eventos FutebolísticosPublication . Campos, João Manuel Costa; Martins, António Constantino LopesO mercado do futebol está em alta, com jogadores e treinadores sendo cada vez mais valorizados. Para garantir um desempenho superior, é crucial fazer escolhas criteriosas na contratação. Além disso, há uma demanda crescente por dados nesse setor, e métricas avançadas, como "expected goals", estão a tornar-se populares na análise de jogos de futebol. Essas métricas, originalmente usadas por mercados de apostas, agora são adotadas por comentadores e treinadores renomados. Isso indica que a análise de dados é essencial para melhorar o desempenho de todos os envolvidos no futebol. Diante desse cenário, surge a necessidade de desenvolver uma solução que consiga explorar sequências e padrões de jogo através de análises avançadas e consiga extrair padrões de jogo a partir de imagens de sequências. A metodologia utilizada neste projeto de pesquisa é a Design Science Research. Inicialmente, foi realizada uma revisão bibliográfica sobre os tipos de dados existentes no contexto do futebol, as métricas avançadas atualmente em alta no mundo analítico desportivo e soluções existentes no ramo. Foram identificadas e descritas algumas das características e limitações mais comuns dos serviços atuais do mercado. Este trabalho pretende apresentar uma proposta que inove no cálculo da métrica de xG, consiga identificar diversas estatísticas calculadas a partir de dados de eventos e consiga estabelecer uma relação entre esses dados, as sequências das equipas e o estilo de jogo da equipa. O sistema Verance App utiliza dados do tipo de fluxo de eventos para calcular estatísticas para todas as equipas que atuaram nas principais 6 ligas durante a presente temporada (2022/23) e apresentar estatísticas de todas as sequências e ações destas mesmas equipas. Para além disto, apresenta também a funcionalidade de apresentação das 3 equipas mais semelhantes em análise. A Verance App não foi utilizada por nenhuma equipa real para fornecer informação de melhoria dos resultados desportivos, mas foi avaliada tendo em conta os seus 3 componentes principais, o modelo xG, o modelo xT e a componente de extração dos padrões das sequências. A análise confirma que a solução projetada, na maioria das circunstâncias, apresenta resultados superiores aos dos serviços atuais do mercado.
- Animal route prediction using artificial intelligencePublication . Azevedo, Catarina Peniche Brandão; Ramos, Carlos Fernando da SilvaThe conservation of wildlife is becoming increasingly critical, especially for endangered species, which face threats from habitat destruction and human interference. This dissertation explores the application of artificial intelligence to predict animal migration routes, an important aspect in species conservation. By using historical GPS tracking data, this study seeks to improve the understanding of the movement patterns of migratory animals. This work starts by addressing several research questions that culminate in the main question, ’How can artificial intelligence be used in predicting animal migration routes?’. These questions focus on the primary techniques and algorithms applied in these cases, the main tracking mechanisms used to gather animal movement information, and the societal implications of the use of AI in this context. Following the systematic review, the development of a feedforward neural network model design for animal route prediction was done. The choice of this model reflects the need for a computationally efficient solution capable of handling the complex data derived from the GPS tracking of African elephants. The model’s performance was improved with hyperparameter tuning, and metrics such as mean squared error (MSE) and R-squared were utilised, demonstrating promising predictive accuracy. By combining AI techniques with wildlife conservation efforts, this work aims to contribute towards mitigating the adverse impacts of human intrusion on migration corridors and enhance efforts to protect endangered species.