Recent advances in neuroimaging techniques, such as diffusion tensor imaging (DTI), represent a crucial resource for structural brain analysis and allow the identification of alterations related to severe neurodegenerative disorders, such as Alzheimer’s disease (AD). At the same time, machine-learning-based computational tools for early diagnosis and decision support systems are adopted to uncover hidden patterns in data for phenotype stratification and to identify pathological scenarios. In this landscape, ensemble learning approaches, conceived to simulate human behavior in making decisions, are suitable methods in healthcare prediction tasks, generally improving classification performances. In this work, we propose a novel technique for the automatic discrimination between healthy controls and AD patients, using DTI measures as predicting features and a soft-voting ensemble approach for the classification. We show that this approach, efficiently combining single classifiers trained on specific groups of features, is able to improve classification performances with respect to the comprehensive approach of the concatenation of global features (with an increase of up to $9\%$ on average) and the use of individual groups of features (with a notable enhancement in sensitivity of up to $11\%$). Ultimately, the feature selection phase in similar classification tasks can take advantage of this kind of strategy, allowing one to exploit the information content of data and at the same time reducing the dimensionality of the feature space, and in turn the computational effort.

Safety and security issues for Critical Infrastructures are growing as attackers adopt drones as an attack vector flying in sensitive airspaces, such as airports, military bases, city centers, and crowded places. Despite the use of UAVs for logistics, shipping recreation activities, and commercial applications, their usage poses severe concerns to operators due to the violations and the invasions of the restricted airspaces. A cost-effective and real-time framework is needed to detect the presence of drones in such cases. In this contribution, we propose an efficient radio frequency-based detection framework called URANUS. We leverage real-time data provided by the Radio Frequency/Direction Finding system, and radars in order to detect, classify and identify drones (multi-copter and fixed-wings) invading no-drone zones. We adopt a Multilayer Perceptron neural network to identify and classify UAVs in real-time, with 90% accuracy. For the tracking task, we use a Random Forest model to predict the position of a drone with an MSE ≈ 0.29, MAE ≈ 0.04, and R2 ≈ 0.93. Furthermore, coordinate regression is performed using Universal Transverse Mercator coordinates to ensure high accuracy. Our analysis shows that URANUS is an ideal framework for identifying, classifying, and tracking UAVs that most Critical Infrastructure operators can adopt.

Nowadays, modern technology is widespread in sports; therefore, finding an excellent approach to extracting knowledge from data is necessary. Machine Learning (ML) algorithms can be beneficial in biomechanical data management because they can handle a large amount of data. A fencing lunge represents an exciting scenario since it necessitates neuromuscular coordination, strength, and proper execution to succeed in a competition. However, to investigate and analyze a sports movement, it is necessary to understand its nature and goal and to identify the factors that affect its performance. The present work aims to define the best model to screen élite and novice fencers to develop further a tool to support athletes’ and trainers’ activity. We conducted a cross-sectional study in a fencing club to collect anthropometric and biomechanical data from élite and novice fencers. Wearable sensors were used to collect biomechanical data, including a wireless inertial system and four surface electromyographic (sEMG) probes. Four different ML algorithms were trained for each dataset, and the most accurate was further trained with hyperparameter tuning. The best Machine Learning algorithm was Multilayer Perceptron (MLP), which had $96.0$% accuracy and 90% precision, recall, and F1-score when predicting class novice (0); and 93% precision, recall, and F1-score when predicting class élite (1). Interestingly, the MLP model has a slightly higher capacity to recognize élite fencers than novices; this is important to determine which training planning and execution are the best to achieve good performances.

The realm of music composition, augmented by technological advancements such as computers and related equipment, has undergone significant evolution since the 1970s. In the field algorithmic composition, however, the incorporation of artificial intelligence (AI) in sound generation and combination has been limited. Existing approaches predominantly emphasize sound synthesis techniques, with no music composition systems currently employing Nicolas Slonimsky’s theoretical framework. This article introduceNeuralPMG, a computer-assisted polyphonic music generation framework based on a Leap Motion (LM) device, machine learning (ML) algorithms, and brain-computer interface (BCI). ML algorithms are employed to classify user’s mental states into two categories: focused and relaxed. Interaction with the LM device allows users to define a melodic pattern, which is elaborated in conjunction with the user’s mental state as detected by the BCI to generate polyphonic music.NeuralPMGwas evaluated through a user study that involved 19 students of Electronic Music Laboratory at a music conservatory, all of whom are active in the music composition field. The study encompassed a comprehensive analysis of participant interaction with NeuralPMG. The compositions they created during the study were also evaluated by two domain experts who addressed their aesthetics, innovativeness, elaboration level, practical applicability, and emotional impact. The findings indicate that NeuralPMG represents a promising tool, offering a simplified and expedited approach to music composition, and thus represents a valuable contribution to the field of algorithmic music composition.

Background: Diet and social determinants influence the state of human health. In older adults, the presence of social, physical and psychological barriers increases the probability of deprivation. This study investigated the relationship between social deprivation and eating habits in non-institutionalized older adults from Southern Italy, and identified foods and dietary habits associated with social deprivation.Methods: We recruited 1,002 subjects, mean age 74 years, from the large population based Salus in Apulia Study. In this cross-sectional study, eating habits and the level of deprivation were assessed with FFQ and DiPCare-Q, respectively.Results: Deprived subjects (n = 441) included slightly more females, who were slightly older and with a lower level of education. They consumed less fish (23 vs. 26 g), fruiting vegetables (87 vs. 102 g), nuts (6 vs. 9 g) and less “ready to eat” dishes (29 vs. 33 g). A Random Forest (RF) model was used to identify a dietary pattern associated with social deprivation. This pattern included an increased consumption of low-fat dairy products and white meat, and a decreased consumption of wine, leafy vegetables, seafood/shellfish, processed meat, red meat, dairy products, and eggs.Conclusion: The present study showed that social factors also define diet and eating habits. Subjects with higher levels of deprivation consume cheaper and more readily available food.