ISEP - DM – Engenharia de Inteligência Artificial
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Browsing ISEP - DM – Engenharia de Inteligência Artificial by advisor "Figueiredo, Ana Maria Neves de Almeida Baptista"
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- Application of artificial intelligence to optimize project management within the PMO areaPublication . Vieira, João Fernando Ferreira; Figueiredo, Ana Maria Neves de Almeida BaptistaThe Project Management Office (PMO) functions as an organizational entity designed to standardize project procedures and leverage efficiencies through project repetition. Beyond standardization, PMOs promote learning from past projects, enabling the adoption of best practices to optimize project delivery in terms of schedule, budget, and quality throughout the project lifecycle. This study proposes an innovative Machine Learning (ML)-based tool that monitors ongoing projects to predict the likelihood of missing deadlines and estimates the percentage of potential delays. Additionally, the tool recommends the most suitable team members for new projects based on the project's area and category. It also includes a web-based alert system that notifies project managers when a project is at risk of failing to meet its deadline. To achieve these goals, various Machine Learning techniques and methodologies were employed. Historical project data was collected and analyzed to develop predictive models capable of forecasting potential delays. Supervised learning models were trained on this data to classify projects at risk of missing deadlines and to estimate the delay percentage. The recommendation system for team member assignments was built using data-driven algorithms that consider the expertise and past performance of team members in specific project areas. Furthermore, feature engineering techniques were applied to enhance the dataset, ensuring that the models could make accurate predictions. Hyperparameter tuning methods such as Grid Search and Random Search were used to optimize the models’ performance. A web application was developed to serve as an interface for project managers, providing real-time alerts on projects at risk and displaying visual indicators for easy monitoring. Finally, this study concludes that the implementation of such a tool in a PMO represents a significant innovation. It is expected to improve the efficiency and effectiveness of project management by enhancing decision-making processes and reducing the likelihood of project delays.
- Lifelong predictive maintenance for railway faultPublication . Risca, Diogo Ferreira; Figueiredo, Ana Maria Neves de Almeida BaptistaThe integration of advanced sensor technologies with machine learning algorithms has revolutionized fault diagnosis in railway systems, particularly at the wheel-track interface. Although numerous models have been proposed to detect irregularities such as wheel out-of-roundness, they often fall short in real-world applications due to the dynamic and nonstationary nature of railway operations. This thesis explores the challenges and opportunities of applying continual learning for predictive maintenance in railway systems, where the model’s ability to share knowledge between domains is critical to improving performance over time. By allowing the model to continuously learn and adapt as new data become available, continual learning overcomes the issue of catastrophic forgetting, which often plagues traditional models. The model retains past knowledge while improving predictive accuracy with each new learning episode, leveraging knowledge sharing mechanisms to adapt to evolving operational conditions, such as changes in speed, load, and track irregularities. Techniques such as experience replay and regularization-based strategies enhance model performance across multiple domains, making it particularly suitable for complex real-world environments. The methodology is validated through comprehensive simulations of train-track dynamic interactions, which capture realistic railway operating conditions. The proposed model demonstrates significant improvements in identifying wheel defects and other irregularities, establishing a reliable sequence for maintenance interventions. Future work will focus on field trials to assess the robustness of the approach in real-world railway environments, including challenges posed by track environments such as bridges and tunnels.
- Sistema de Recomendações Multi-Objectivo Multi-Model para Previsão de Ações e-CommercePublication . Costa, Nuno; Figueiredo, Ana Maria Neves de Almeida BaptistaThe thriving retail e-commerce sector, driven by the surge in digital transactions and con sumer engagement, emphasizes the imperative for enterprises to optimize revenue and prof itability. To achieve this, online stores are increasingly turning to advanced recommender systems. These systems strategically target multiple objectives, focusing on factors that boost user interaction and value extraction, such as increased item viewing and cart addi tions. By prioritizing a set of objectives, recommender systems aim to cater to immediate user preferences and cultivate a personalized user experience, fostering loyalty and continu ous engagement in the dynamic landscape of e-commerce. In response to this evolving landscape, e-commerce enterprise OTTO initiated a Kaggle competition, calling upon the global community of data scientists and machine learning enthusiasts to model and predict a set of events within their products. This collaborative effort not only propelled advancements in the field but also underscored the significance of community-driven initiatives in shaping the future of personalized online shopping experi ences. This project directly addresses the challenges posed by the OTTO Kaggle competition, aiming to evaluate the individual and collective performance of diverse recommendation models within e-commerce recommender systems. Utilizing the Design Science Research (DSR) methodology, the project underwent iterative design and development, aligning with specific goals derived from a comprehensive review of existing literature and state-of-the-art recommender systems as well as goals and requirements extrapolated from the mentioned competition. The implemented system integrates Gradient Boosting Decision Trees (GBDT), Dropouts meet Multiple Additive Regression Trees (DART), Gated Recurrent Unit for Recommender Systems (GRU4Rec), and Random Forest models into an ensemble framework. Evaluated using predefined metrics from the Kaggle competition, the system leverages user session data to predict user actions across various event types. While the performance analysis demonstrates the system’s competency, there is room for improvement to provide enhanced value in real-world e-commerce scenarios. The project highlights the continuous evolution of recommender systems, emphasizing the need for ongoing research and refinement.