Abstract
Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.

Abstract
Nowadays, there is the paradox of technology: although smartphones have revolutionized our way of living, bringing convenience and connectivity, they have also introduced new challenges, notably distracted driving. This paper addresses the issue of visual distraction, one of the main contributors to traffic accidents, through the development of an innovative system that combines the application of convolutional neural networks and the functionality of mobile devices. The adopted methodology focused on the collection of a broad set of images to train an artificial intelligence model capable of classifying a qualitative variable with two distinct categories: attention and distraction of a driver. In particular, the study concentrated on creating a mobile application that uses a smartphone's camera to monitor the driver and issue auditory alerts if it detects prolonged distraction. The achieved results highlighted the efficacy of the model, especially after its optimization for the TensorFlow Lite format, suitable for implementation on mobile devices due to its efficiency in terms of speed and resource consumption.

Abstract
This article proposes methods for maximising the detection rates of thermal fiducial markers using thermography. By exploring the combination of image processing techniques with the use of an affordable thermographic camera, the aim is to mitigate the negative effects of thermography and improve accurate marker identification in a variety of mounting and distance conditions. The research identified a diversity of processing techniques capable of improving thermal marker recognition, offering the potential to surpass previous results. The results highlight the possibility of using low-cost thermographic cameras for this purpose, which could democratise and reduce the costs of recognition processes. This methodology validates the proposed approach, providing a robust basis for future improvements in thermal marker detection and promoting the feasibility of practical, low-cost applications in an assortment of fields.

Abstract
The Ambient Assisted Living (AAL) systems are human-centered and designed to prioritize the needs of elderly individuals, providing them with assistance in case of emergencies or unexpected situations. These systems involve caregivers or selected individuals who can be alerted and provide the necessary help when needed. To ensure effective assistance, it is crucial for caregivers to understand the reasons behind alarm triggers and the nature of the danger. This is where an explainability module comes into play. In this paper, we introduce an explainability module that offers visual explanations for the fall detection module. Our framework involves generating anchor boxes using the K-means algorithm to optimize object detection and using YOLOv8 for image inference. Additionally, we employ two well-known XAI (Explainable Artificial Intelligence) algorithms, LIME (Local Interpretable Model) and Grad-CAM (Gradient-weighted Class Activation Mapping), to provide visual explanations.

Abstract
Olive trees play a crucial role in the global agricultural landscape, serving as a primary source of olive oil production. However, olive trees are susceptible to several diseases, which can significantly impact yield and quality. This study addresses the challenge of improving the diagnosis of diseases in olive trees, specifically focusing on aculus olearius and Olive Peacock Spot diseases. Using a novel hybrid approach that combines deep learning and machine learning methodologies, the authors aimed to optimize disease classification accuracy by analyzing images of olive leaves. The presented methodology integrates Local Binary Patterns (LBP) and an adapted ResNet50 model for feature extraction, followed by classification through optimized machine learning models, including Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrated that the hybrid model achieved a groundbreaking accuracy of 99.11%, outperforming existing models. This advancement underscores the potential of integrated technological approaches in agricultural disease management and sets a new benchmark for the early and accurate detection of foliar diseases.

Abstract
Retail transactions represent sales of consumer goods, or final goods, by consumer companies. This sector faces security challenges due to the hustle and bustle of sales, affecting employees' workload. In this context, it is essential to estimate the number of customers who will appear in the store daily so that companies can dynamically adjust employee schedules, aligning workforce capacity with expected demand. This can be achieved by forecasting transactions using past observations and forecasting algorithms. This study aims to compare the ARIMA time series algorithm with several Machine Learning algorithms to predict the number of daily transactions in different store patterns, considering data variability. The study identifies four typical store patterns based on these criteria using daily transaction data between 2019 and 2023 from all retail stores of the leading company in Portugal. Due to data variability and the results obtained, the algorithm that presents the most minor errors in predicting daily transactions is selected for each store. This study's ultimate goal is to fill the gap in forecasting daily customer transactions and present a suitable forecasting model to mitigate risks associated with transactions in retail stores.