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Semantic segmentation of medical images presents an enormous potential for diagnosis and surgery. However, achieving precise results involves designing and training complex Deep Learning (DL) models specifically for this task, which is only available to some. SAM is a model developed by Meta capable of segmenting objects present in virtually any type of image. This paper showcases SAM’s robustness and exceptional performance in medical image segmentation, even in the absence of direct training on these image types (lung Computed Tomographies (CTs) and chest X-rays, in particular). Additionally, it achieves this impressive outcome while requiring minimal user intervention. Although the dataset used to train SAM does not contain a single sample of both medical image types, processing a popular dataset comprised of $\mathrm {20 }$ volumes with a total of $\mathrm {3520 }$ slices using the ViT-L version of the model yields an average Jaccard index of $\mathrm {91.45 \%}$ and an average Dice score of $\mathrm {94.95 \%}$ . The same version of the model achieves a $\mathrm {93.19 \%}$ Dice score and a $\mathrm {87.45 \%}$ Jaccard index when segmenting a frequently-used chest X-ray dataset. The values obtained are above the $\mathrm {70 \%}$ mark recommended in the literature, and close to state-of-the art models developed specifically for medical segmentation. These results are achieved without user interaction by providing the model with positive prompts based on the masks of the dataset used and a negative prompt located in the center of bounding box that contains the masks.

Technology innovation for the sixth generation (6G) era should focus on improving quality of life by addressing societal needs, advancing human experience with the fusion of digital and physical worlds, and achieving a sustainable well-being. The 6G networks are expected to bring higher capacity and coverage combined with dependable real-time properties but must additionally be able to control the balance between quality of service (QoS) and quality of experience (QoE). QoS is the ability to offer reliable performance to connect people and things. QoE is a way to measure the quality of a service as perceived by the end-user, enabling a more customer-centric network design approach for end-user services. To provide excellent QoS, accurate estimation of QoE and corrective measures to adapt QoS to maintain acceptable QoE from the end-user perspective will be needed. In this paper, we propose a linear weighted QoE model in terms of mean opinion score (MOS) and the ability to adapt QoS. We further validate our model with a QoE impairment model and analyze the results with respect to the latency and bit rate metrics, which are critical for our target use case of Virtual Reality (VR) gaming. We also propose to use the QoE model to enable adaptive QoS in the 6G radio access network (RAN), including dynamic learning of QoS metrics.

High levels of school dropout are a major burden on the educational and professional development of a country’s inhabitants. A country’s prosperity depends, among other factors, on its ability to produce higher education graduates capable of moving a country forward. To alleviate the dropout problem, more and more institutions are turning to the possibilities that artificial intelligence can provide to predict dropout as early as possible. The difficulty of accessing personal data and privacy issues that it entails force the institutions to rely on the Academic Data of their students to create accurate and reliable predictive systems. This work focuses on creating the best possible predictive model based solely on academic data, and accordingly, its capacity to infer knowledge must be maximised. Thus, Feature Engineering and Instance Engineering techniques such as dealing with redundancy, significance of the features, correlation, cardinality features, missing values, creation or elimination of features, data fusion, removal of unuseful instances, binning, resampling, normalisation, or encoding are applied in detail before the construction of well-known models such as Gradient Boosting, Random Forest, and Support Vector Machine along with an Ensemble of them at different stages: prior to enrolment, at the end of the first semester, at the end of the second semester, at the end of the third semester, and at the end of the fourth semester. Through the construction of these predictive models that serve as inputs to a decision support system, the application of effective dropout prevention policies can be applied.

Higher Education plays a principal role in the changing and complex world of today, and there has been rapid growth in the scientific literature dedicated to predicting students' academic success or risk of dropout thanks to advances in Data Mining techniques. Degrees such as Computer Science in particular are in ever greater demand. Although the number of students has increased, the number graduating is still not enough to provide society with as many as it requires. This study contributes to reversing this situation by introducing an approach that not only predicts the dropout risk or students' performance but takes action to help both students and educational institutions. The focus is on maximizing graduation rates by constructing a Recommender System to assist students with their selection of subjects. In particular, the challenge is addressed of constructing reliable Recommender Systems on the basis of data which are both sparse and few in quantity, imbalanced, and anonymized, and which might have been stored under imperfect conditions. This approach is successfully applied to create a Recommender System using a real-world dataset from a public Spanish university containing performance data of a Computer Science degree course, demonstrating its successful application in real environments. The construction of a support system based on that approach is described, its results are evaluated, and its implications for students' academic achievement, and for institutions' graduation rates are discussed. Through the construction of this decision support system for students, we intend to increase the graduation rates and lower the dropout rate.

The application of natural language to improve students' interaction with information systems is demonstrated to be beneficial. In particular, advances in cognitive computing enable a new way of interaction that accelerates insight from existing information sources, thereby contributing to the process of learning. This work aims at researching the application of cognitive computing in blended learning environments. We propose a modular cognitive agent architecture for pedagogical question answering, featuring social dialogue (small talk), improved for a specific knowledge domain. This system has been implemented as a personal agent to assist students in learning Data Science and Machine Learning techniques. Its implementation includes the training of machine learning models and natural language understanding algorithms in a human-like interface. The effectiveness of the system has been validated through an experiment.