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- Big Data-based recommendation systemsPublication . Barata, Luís; Mata, Diogo; Nunes, Jorge; Rodrigues, LucasIntroduction: With the exponential growth of data, digital platforms increasingly rely on Big Data technologies to personalize user experiences and improve the accuracy of item recommendations. Recommendation systems play a critical role in e-commerce, entertainment, and social media by analyzing user interactions, behaviors, and preferences. However, the complexity of large-scale data processing and the diversity of filtering techniques pose significant challenges to achieving high performance and scalability. Objectives: This study aims to analyze how Big Data technologies are applied in modern recommendation systems, emphasizing their role in enhancing personalization and system performance. The work seeks to identify the main data collection strategies, algorithms, and tools adopted in recent research, as well as to assess how these systems address challenges related to scalability and real-time processing. Methods: A systematic literature review was conducted using the IEEE Xplore database, focusing on articles published between 2015 and 2025. The search targeted studies combining Big Data and recommendation systems within e-commerce contexts. Out of 49 retrieved publications, 10 met the inclusion criteria, and 5 were ultimately selected after applying exclusion filters. Each selected paper was analyzed regarding its objectives, employed algorithms, data sources, and achieved outcomes. Results: The reviewed studies demonstrate a wide variety of approaches and technologies, including Hadoop and Spark frameworks for large-scale data processing, deep learning models such as DeepLearning4j for real-time prediction, and classical data mining algorithms like K-Means, Apriori, and FP-Growth. Hybrid methods combining collaborative and content-based filtering were shown to overcome limitations such as the cold start and data sparsity problems. Scalability was addressed through distributed processing and optimization techniques like network pruning and parallel computation. These systems achieved higher recommendation precision and responsiveness across different e-commerce and media applications. Conclusions: The analysis confirms that Big Data–driven recommendation systems are essential for enhancing user engagement and conversion in digital platforms. By integrating data mining, machine learning, and distributed processing technologies, these systems deliver efficient, adaptive, and context-aware recommendations. Future developments should continue exploring hybrid and deep learning–based approaches to further improve scalability, personalization, and computational performance in increasingly complex digital ecosystems.
- Integrating sentiment analysis into agile feedback loops for continuous improvementPublication . Marçal, Diogo; Metrôlho, J.C.M.M; Ribeiro, Fernando Reinaldo; Gasparetto , Alessandro; O’Shaughnessy, DouglasThe pursuit of continuous improvement is a defining feature of agile software development, yet its success depends on the systematic collection and interpretation of team members’ feedback. Conventional mechanisms, such as retrospectives and surveys, provide valuable insights but are often constrained by their episodic nature and susceptibility to subjective interpretation. This study examines the potential of Artificial Intelligence (AI), and in particular sentiment analysis, to complement feedback-driven practices and strengthen continuous improvement in agile contexts. Two literature reviews were conducted: one on applications of AI across software engineering domains and another focusing specifically on sentiment analysis in agile environments. Based on these insights, a prototype tool was developed to integrate sentiment analysis into task management workflows, enabling the structured collection and analysis of developers’ perceptions of task descriptions. Semi-structured interviews with experienced project managers confirmed the relevance of this approach, highlighting its capacity to improve task clarity and foster more transparent and inclusive feedback processes. Participants emphasized the value of the proposed approach in generating rapid, automated insights, while also identifying potential limitations related to response fatigue and the reliability of AI-generated outcomes. The findings suggest that incorporating sentiment analysis into agile practices is both feasible and advantageous, providing a pathway to align technical objectives with developer experiences while enhancing motivation, collaboration, and operational efficiency.
- EuroAGE+: um projeto para melhorar a qualidade de vida dos idosos, através da utilização de tecnologias inovadoras e da cooperação transfronteiriçaPublication . Marques, Ermelinda; Pires, Nuno; Vaz, Cláudia; Brito, Ana Raquel Costa; Serra, Nuno; Hornero Sánchez, Roberto; Gómez-Raja, Jonathan; Rocha, Rui; Gonçalves, Paulo; Ortega Morán, Juan Francisco; Vila-Chã, Carolina; Rodríguez Domínguez, María Trinidad; Núñez Trujillo, PedroO principal desafio que enfrenta este projeto é o envelhecimento da população, uma questão desafiante para as políticas dos países, Espanha e Portugal, e que se estende com maior incidência nas regiões que cooperam no projeto (EuroACE: Extremadura, Centro de Portugal e Alentejo) e que têm características comuns: reduzida densidade populacional e um acentuado envelhecimento. Este projeto enfrenta assim desafios territoriais comuns no espaço transfronteiriço de Espanha-Portugal. Objetivos: o principal objetivo do Projeto é melhorar a qualidade de vida das pessoas idosas, promovendo a autonomia através da utilização de tecnologias inovadoras e da cooperação transfronteiriça. Métodos: A promoção da prática de atividade física, estimulação cognitiva e socioemocional, bem como a utilização das Tecnologias de Informação e Comunicação como a robótica, os jogos sérios e as interfaces cérebro computador, serão as principais ferramentas desenvolvidas durante o projeto. Com o desenvolvimento e implementação destas soluções e aplicação dos conhecimentos técnicos, pretende-se que esta cooperação transfronteiriça traga benefícios na resolução do problema demográfico do envelhecimento progressivo da população. Resultados: O projeto terá impacto positivo na região EuroACE, prevendo-se o envolvimento de cerca de 200 profissionais na implementação das soluções desenvolvidas junto de mais de 1000 potenciais idosos. Através dos parceiros beneficiários do projeto, estima-se o impacto dos resultados no tecido empresarial em mais de 50 instituições e empresas. Conclusão: A Rede Euro-AGE+ será fundamental para promover a investigação e a inovação no domínio da saúde e do envelhecimento saudável, melhorando a qualidade de vida da população e prolongando a esperança média de vida saudável.
- Ontological framework for high-level task replanning for autonomous robotic systemsPublication . Bernardo, Rodrigo; Sousa, João M.C.; Gonçalves, PauloSeveral frameworks for robot control platforms have been developed in recent years. However, strategies that incorporate automatic replanning have to be explored, which is a requirement for Autonomous Robotic Systems (ARS) to be widely adopted. Ontologies can play an essential role by providing a structured representation of knowledge. This paper proposes a new framework capable of replanning high-level tasks in failure situations for ARSs. The framework utilizes an ontology-based reasoning engine to overcome constraints and execute tasks through Behavior Trees (BTs). The proposed framework was implemented and validated in a real experimental environment using an Autonomous Mobile Robot (AMR) sharing a plan with a human operator. The proposed framework uses semantic reasoning in the planning system, offering a promising solution to improve the adaptability and efficiency of ARSs.
- Internet of robotic things evolution, standards and data interoperability best practices for the next generation of artificial intelligence‐powered systemsPublication . Gyrard, Amelie; Freitas, Edison Pignaton de; Serrano, Martin; Li, Howard; Gonçalves, Paulo; Quintas, João; Vermesan, Ovidiu; Olivares‐Alarcos, Alberto; Kung, Antonio; Cavallo, FilippoThe Internet of Robotic Things (IoRT) represents the rise of a new paradigm enabling robots to serve not only as autonomous units but also as intelligent interconnected entities that can interact, collaborate, and share information through the edge, cloud and other data networks. IoRT is a technological progress and the fusion of Robotics with the Internet of Things (IoT), artificial intelligence (AI), and edge‐Computing, IoRT can benefit from the next‐generation spatial web, Web 4.0 (the intelligent immersive knowledge Web), by enhancing data processing, situational awareness, and integration with immersive technologies, software‐defined automation (SDA), and spatial computing technologies. Semantic Web and Web 4.0 technologies are becoming common in robotics projects for exchanging data and enabling data set interoperability. The main challenge is to upgrade how robotic things interact with each other and their environment in a more situation‐aware fashion, enabling IoRT situation‐aware capabilities. This paper reviews the definition of IoRT considering the latest developments in sensor technology and data management systems and uses a novel survey methodology to find, classify, and reuse robotic expertise and present it to the community and engineering experts. The survey is shared through the LOV4IoT‐Robotics ontology catalog, which is available online. This catalog demonstrates how best practices for data sharing and data set interoperability are also used to extract robotic knowledge semi‐automatically. A set of relevant semantic‐enabled projects designed by domain experts that focused on extracting robotic knowledge was included.
- AI-powered prompt engineering for education 4.0: Transforming digital resources into engaging learning experiencesPublication . Serra, Paulo; Oliveira, ÂngelaThe integration of Artificial Intelligence into educational environments is reshaping the way digital resources support teaching and learning, which reinforces the need to understand how prompting strategies can enhance engagement, autonomy, and personalisation. This study examines the pedagogical role of prompt engineering in the transformation of static digital materials into adaptive and interactive learning experiences aligned with the principles of Education 4.0. A systematic literature review was conducted between 2023 and 2025 following the PRISMA protocol, comprising a sample of 166 studies retrieved from the ACM Digital Library and Scopus databases. The search strategy employed the keywords “artificial intelligence” OR “intelligent tutoring systems” AND “e-learning” OR “digital education” AND “personalised learning” OR “academic performance” OR “student engagement” OR “motivation” OR “ethical issues” OR “student autonomy” OR “limitations of AI”. The analysis identified consistent improvements in academic performance, motivation, and student engagement, although persistent limitations remain related to technical integration, ethical risks, and limited pedagogical alignment. Building on these findings, the article proposes a structured prompt engineering methodology that integrates interdependent components including role definition, audience specification, feedback style, contextual framing, guided reasoning, operational rules, and output format. A practical illustration shows that embedding prompts into digital learning resources, exemplified through PDF-based exercises, enables AI agents to support personalised and adaptive study sessions. The study concludes that systematic prompt design can reposition educational resources as intelligent, transparent, and pedagogically rigorous systems for knowledge construction.
- A practical approach to industrial digitalization through data acquisition and systems integration for predictive maintenancePublication . Bocharov, Nikita; Torres, Pedro; Matos, JoãoThe digital transformation of industrial environments requires the ability to collect, process, and integrate data from production systems in real time. However, many manufacturing facilities operate with legacy equipment that is perfectly functional and operational but lacks native connectivity or standardized interfaces for data acquisition. This paper presentes an approach to enable industrial digitalization through the implementation of a network architecture at the Operational Technology (OT) level that facilitates the collection of structured data from legacy and modern machines. The proposed solution ensures integration between production systems and supervisory platforms, supporting real-time monitoring through SCADA systems and providing relevant information for predictive maintenance strategies. The proposal is based on the implementation of a standardized and secure communication infrastructure between the shop floor and higher-level Information Technology (IT) systems, aligning with the principles of Industry 4.0. The solution has been implemented in a real industrial scenario, is fully operational and the results demonstrate significant benefits of integrating heterogeneous industrial assets into a unified data ecosystem, improving process insight, operational efficiency and supporting maintenance decision-making.
- Recognition of food ingredients: Dataset analysisPublication . Louro, João; Fidalgo, Filipe; Oliveira, ÂngelaNowadays, food waste is seen as a complex problem with effects on the social, economic, and environmental domains. Even though this view is widely held, it is frequently believed that individual acts have little to no impact on the issue. But just like with recycling, there may be a significant impact if people start adopting more sustainable eating habits. We suggest using a cutting-edge convolutional neural network (CNN) model to identify food in light of these factors. To improve performance, this model makes use of several strategies, such as fine-tuning and transfer learning. Additionally, we suggest using the Selenium library to create a dataset by employing the web scraping technique. This strategy solves the problem that many open-source datasets have with the overrepresentation of foods from the Asian continent by enabling the addition of foods to the dataset in a customized way. First, using the PRISMA methodology, a thorough examination of recente research in this field will be carried out. We will talk about the shortcomings of the most widely used dataset (Food-101), which prevent the ResNet-50 model from performing well. Using this information, a smartphone app that can identify food and suggest recipes based on the ingredients it finds could be developed. This would prevent food waste that results from the lack of imagination and patience of most people. The food recognition model used was the ResNet-50 convolutional neural network, which achieved 90% accuracy for the validation set and roughly 97% accuracy in training.
- Gamification and emotional intelligence: Development of a digital application for childrenPublication . Nunes, Maria; Oliveira, Ângela; Fidalgo, FilipeIt is important to work on educating children’s emotional intelligence, namely the re-awareness and control of emotions, both their own and those around them so that they feel empathy for others, establish and maintain positive relationships and make conscious decisions. This document describes a proposal for a gamified solution, based on the development of a multimedia product, which aims to help children, parents and teachers in the education of emotional intelligence in children. The solution makes it possible to present everyday scenarios to children, allowing adults to find out how they feel and, based on this information, to work on feelings and social behaviour. The solution was based on research into studies available in scientific databases on children’s emotional intelligence, as well as research into exercises that can help work on this same issue. Once implemented, the solution was tested with children from a primary school, where it was possible to collect feedback from them and their teachers and make improvements. This study presentsthe design, development and evaluation of a gamified application for children focused on emotional intelligence. The methodology used is based on a systematic literature review following the PRISMA protocol and the development of an iterative multimediaproduct. The study sample included around 200 elementary school children, where it was possible to collect qualitative feedback to evaluate the effectiveness of the application. The results obtained made it possible to make improvements to the design of the application and to obtain feedback from the teachers, which was very positive, but transmitted by direct interview.
- Edge-computing smart irrigation controller using LoRaWAN and LSTM for predictive controlled deficit irrigationPublication . Baseca, Carlos Cambra; Dionísio, Rogério Pais; Ribeiro, Fernando Reinaldo; Metrôlho, J.C.M.M; Yu, GuandingEnhancing sustainability in agriculture has become a significant challenge today where in the current context of climate change, particularly in countries of the Mediterranean area, the amount of water available for irrigation is becoming increasingly limited. Automating irrigation processes using affordable sensors can help save irrigation water and produce almonds more sustainably. This work presents an IoT-enabled edge computing model for smart irrigation systems focused on precision agriculture. This model combines IoT sensors, hybrid machine learning algorithms, and edge computing to predict soil moisture and manage Controlled Deficit Irrigation (CDI) strategies in high density almond tree fields applying reductions of 35% ETc (crop evapotranspiration). By gathering and analyzing meteorological, humidity soil, and crop data, a soft ML (Machine Learning) model has been developed to enhance irrigation practices and identify crop anomalies in real-time without cloud computing. This methodology has the potential to transform agricultural practices by enabling precise and efficient water management, even in remote locations with lack of internet access. This study represents an initial step toward implementing ML algorithms for irrigation CDI strategies.