Abstract
Nowadays, decision making is one of the most important and influential aspects of everyday life, and the application of metaheuristics and heuristics facilitates the process. Thus, this paper presents a performance analysis of the combination of constructive heuristics used to generate initial solutions for metaheuristics applied to scheduling problems. Namely, Nawaz, Enscore, and Ham Heuristic (NEH), Palmer Heuristic and Campbell, Dudek, and Smith Heuristic (CDS) with Cuckoo Search, Firefly Algorithm and Simulated Annealing. The aim is to compare the performance of these combinations to analyse the efficiency, effectiveness and robustness of each. All combinations were analysed in an in-depth computational study and then subjected to a statistical study to support an accurate analysis of the results. The results of the analysis show that the Firefly Algorithm associated with NEH, despite having a high runtime, performs better than the other combinations. However, the best effectiveness-efficiency ratio corresponds to SA-Palmer and SA-CDS.

Abstract
The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing's unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace.

Abstract
The adoption of residential photovoltaic (PV) systems to mitigate the effects of climate change has been incentivized in recent years by government policies. Due to the impacts of these systems on the energy mix and the electrical grid, it is essential to understand how these technologies will expand in urban areas. To fulfill that need, this article presents an innovative method for modeling the diffusion of residential PV systems in urban environments that employs spatial analysis and urban characteristics to identify residences at the subarea level with the potential for installing PV systems, along with temporal analysis to project the adoption growth of these systems over time. This approach integrates urban characteristics such as population density, socioeconomic data, public environmental awareness, rooftop space availability, and population interest in new technologies. Results for the diffusion of PV systems in a Brazilian city are compared with real adoption data. The results are presented in thematic maps showing the spatiotemporal distribution of potential adopters of PV systems. This information is essential for creating efficient decarbonization plans because, while many households can afford these systems, interest in new technologies and knowledge of the benefits of clean energy are also necessary for their adoption.

Abstract
Currently, energy consumption in residential buildings is increasingly high. To meet demand, renewable energies are increasingly being used to produce more energy in a sustainable way, which has led to an increase in the load on the distribution network. Thus, with the exponential growth of dependence on technologies, studies on consumption patterns are increasingly common in order to try to understand the needs of the population and, in this way, make a more rational and efficient use of energy. This article aims to find consumption patterns in residential devices, considering specific houses. This work proposes the use of the Apriori algorithm, which allows the creation of several association rules among devices. The results, considering several scenarios in a house with 9 appliances, show that, despite the Apriori algorithm's difficulty in finding associations in household appliances with little time of use, several interesting association rules can be identified, providing relevant insights for future consumption flexibility models applications.

Abstract
This work presents a specialized tabu search algorithm applied to the problem of electric power distribution systems primary feeders' reconfiguration. The specialization is related to two fundamental aspects of the tabu search algorithm. The first proposal eliminates the concept of a list of prohibited attributes and the aspiration criterion, but also avoids the possibility of revisiting a candidate solution so that cycling is avoided by maintaining a tabu list with all previously visited solutions. The second proposal is the possibility of restarting the search from the incumbent solution while avoiding paths that can be formed by revisiting candidate solutions. A new strategy based on Prim's algorithm generates a high-quality initial solution for the problem. Tests are conducted using the 33-, 84-, 118-, 136-, and 415-node test systems. The results demonstrate the effectiveness of the proposal for solving the reconfiguration problem since the best-known solution for each system is achieved within highly efficient execution times.

Abstract
Artificial intelligence encapsulates a black box of undiscovered knowledge, propelling the exploration of Explainable Artificial Intelligence (XAI) in generative data synthesis and deep learning. Focused on unveiling these black box areas, pointed into interpretability and validation in synthetic data generation, shedding light on the intricacies of generative processes. XAI techniques illuminate decision-making in complex algorithms, enhancing transparency and fostering a comprehensive understanding of non-linear relationships. Addressing the complexity of explaining deep learning models, this paper proposes an XAI solution for deep synthetic data generation explanation. The model integrates a clustering approach to identify similar training instances, reducing interpretation time for large datasets. Explanations, available in various formats, are tailored to diverse user profiles through integration with language models, generating texts with different technical detail levels. This research contributes to ethically deploying AI, bridging the gap between advanced model complexities and human interpretability in the dynamic landscape of artificial intelligence.