Abstract
Problem: Portable ECG/sEMG acquisition systems for telemedicine often lack application flexibility (e.g., limited configurability, signal validation) and efficient wireless data handling. Methodology: A modular biosignal acquisition system with up to 8 channels, 24-bit resolution and configurable sampling (1-4 kHz) is proposed, featuring per-channel gain/source adjustments, internal MUX-based reference drive, and visual electrode integrity monitoring; Bluetooth (R) transmits data via a bit-wise packet structure (83.92% smaller than JSON, 7.28 times faster decoding with linear complexity based on input size). Results: maximum 6.7 mu V-rms input-referred noise; harmonic signal correlations >99.99%, worst-case THD of -53.03 dBc, and pulse wave correlation >99.68% in frequency-domain with maximum NMSE% of 6e-6%; and 22.3-hour operation (3.3 Ah battery @ 150 mA). Conclusion: The system enables high-fidelity, power-efficient acquisition with validated signal integrity and adaptable multi-channel acquisition, addressing gaps in portable biosensing.

Abstract
In the field of gaming artificial intelligence, selecting the appropriate machine learning approach is essential for improving decision-making and automation. This paper examines the effectiveness of deep reinforcement learning (DRL) within interactive gaming environments, focusing on complex decision-making tasks. Utilizing the Unity engine, we conducted experiments to evaluate DRL methodologies in simulating realistic and adaptive agent behavior. A vehicle driving game is implemented, in which the goal is to reach a certain target within a small number of steps, while respecting the boundaries of the roads. Our study compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) in terms of learning efficiency, decision-making accuracy, and adaptability. The results demonstrate that PPO successfully learns to reach the target, achieving higher and more stable cumulative rewards. Conversely, SAC struggles to reach the target, displaying significant variability and lower performance. These findings highlight the effectiveness of PPO in this context and indicate the need for further development, adaptation, and tuning of SAC. This research contributes to developing innovative approaches in how ML can improve how player agents adapt and react to their environments, thereby enhancing realism and dynamics in gaming experiences. Additionally, this work emphasizes the utility of using games to evolve such models, preparing them for real-world applications, namely in the field of vehicles' autonomous driving and optimal route calculation.

Abstract
Forest fires in Portugal are a recurring tragedy, especially during the summer, leaving a devastating trail affecting the environment and local communities. In addition to the loss of vast forest areas, these disasters harm wildlife, pollute the air, and compromise soil and water quality, contributing to environmental degradation and increasing the risk of soil erosion and landslides. Furthermore, fires have significant economic impacts, affecting communities that depend on the forest for subsistence, tourism, and agricultural activities. To address this issue, an innovativeWeb Service has been developed that uses artificial intelligence algorithms to calculate real-time fire risk. This service integrates up-todate weather data with historical fire patterns, providing an accurate and timely assessment of fire potential in specific areas. The machine learning model behind the service was trained with historical fire data from mainland Portugal between 2017 and 2023, allowing for a more accurate and predictive analysis of fire risk. The Web Service facilitates proactive emergency prevention and decision-making response by integrating realtime weather information with historical fire data. Authorities can use the information provided by the service to implement preventive policies to help elderly people.

Abstract
This paper presents an interactive game designed to improve users' experience related to driving behaviour, as well as to provide decision support in this context. This paper explores machine learning (ML) methods to enhance the decision-making and automation in a gaming environment. It examines various ML strategies, including supervised, unsupervised, and Reinforcement Learning (RL), emphasizing RL's effectiveness in interactive environments and its combination with Deep Learning, culminating in Deep Reinforcement Learning (DRL) for intricate decision-making processes. By leveraging these concepts, a practical application considering a gaming scenario is presented, which replicates vehicle behaviour simulations from real-world driving scenarios. Ultimately, the objective of this research is to contribute to the ML and artificial intelligence (AI) fields by introducing methods that could transform the way player agents adapt and interact with the environment and other agents decisions, leading to more authentic and fluid gaming experiences. Additionally, by considering recreational and serious games as case studies, this work aims to demonstrate the versatility of these methods, providing a rich, dynamic environment for testing the adaptability and responsiveness, while can also offer a context for applying these advancements to simulate and solve real-world problems in the complex and dynamic domain of mobility.

Abstract
Explainable Artificial Intelligence (XAI) aims to enhance the interpretability of Artificial Intelligence (AI) systems for humans. The goal is to ensure that algorithmic decisions and underlying data are understandable to non-technical stakeholders. Advanced Machine Learning (ML) models, such as deep neural networks, enable AI systems to process vast data and extract intricate patterns, akin to the human brain, but this complicates XAI development. Complex ML models require substantial data for training, exacerbating the challenge. Consequently, this paper proposes a novel approach to improve XAI for complex ML models, particularly those with large data needs. Using K-Means clustering, the paper proposes to identify relevant data instances to create similarity clusters. This filtering process focuses XAI on essential information, even with complex models, reducing the data set to find patterns and explanations, so that, using the same approach, only the best explanations are filtered efficiently. The paper proposes to implement and test this model with a case study on ML for PV generation forecasting in buildings. Results show that the proposed approach is able to generate explanations that are very similar to those generated when using the entire available data, in only a portion of the execution time, leveraging from the identification of a small number of representative data points. This approach, therefore, enhances the efficiency of XAI by achieving promising results with a smaller dataset. It also facilitates the development of more understandable and fastly provided solutions, which is essential for real-world XAI users such as electric mobility users that need PV forecasting explanations as support for their vehicles charging management.

Abstract
With the serious danger to nature and humanity that forest fires are, taken into consideration, this work aims to develop an artificial intelligence model capable of accurately predicting the forest fire risk in a certain region based on four different factors: temperature, wind speed, rain and humidity. Thus, three models were created using three different approaches: Artificial Neural Networks (ANN), Random Forest (RF), and K-Nearest Neighbor (KNN), and making use of an Algerian forest fire dataset. The ANN and RF both achieved high accuracy results of 97%, while the KNN achieved a slightly lower average of 91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.