Over the last few years, the phenomenon of fake news has become an important issue, especially during the worldwide COVID-19 pandemic, and also a serious risk for the public health. Due to the huge amount of information that is produced by the social media such as Facebook and Twitter it is becoming difficult to check the produced contents manually. This study proposes an automatic fake news detection system that supports or disproves the dubious claims while returning a set of documents from verified sources. The system is composed of multiple modules and it makes use of different techniques from machine learning, deep learning and natural language processing. Such techniques are used for the selection of relevant documents, to find among those, the ones that are similar to the tested claim and their stances. The proposed system will be used to check medical news and, in particular, the trustworthiness of posts related to the COVID-19 pandemic, vaccine and cure.

This paper introduces a new approach to emotion classification utilising deep learning models, specifically the Vision Transformer (ViT) model, in the analysis of electroencephalogram (EEG) signals. A dual-feature extraction approach was implemented in our study, utilising Power Spectral Density and Differential Entropy, to analyse the SEED IV dataset. This methodology resulted in the detailed classification of four distinct emotional states. The ViT model, which was originally designed for image processing, has been successfully applied to EEG signal analysis. It demonstrated remarkable performance by attaining a test accuracy of 99.02% with little variance. Notably, it outperformed conventional models like GRUs, LSTMs, and CNNs in this context. The findings of our study indicate that the ViT model has a high level of effectiveness in accurately identifying complex patterns present in EEG data. Specifically, the precision and recall rates achieved by the model surpass 98%, while the F1 score is estimated to be about 98.9%. The results of this study not only demonstrate the efficacy of transformer-based models in analysing cognitive states, but also indicate their considerable potential in improving systems for sympathetic human-computer interaction.

Eye-tracking systems have emerged as valuable tools in various research fields, including psychology, medicine, marketing, car safety, and advertising. However, the high costs of the necessary specialized hardware prevent the widespread adoption of these systems. Appearance-based gaze estimation techniques offer a cost-effective alternative that can rely solely on RGB cameras, albeit with reduced accuracy. Therefore, the aim of our work was to present a real-time eye-tracking system with low-end hardware that leverages appearance-based techniques while overcoming their drawbacks to make reliable gaze data accessible to more users. Our system employs fast and light machine learning algorithms from an external library called MediaPipe to identify 3D facial landmarks. Additionally, it uses a series of widely recognized computer vision techniques, like morphological transformations, to effectively track eye movements. The precision and accuracy of the developed system in recognizing saccades and fixations when the eye movements are mainly horizontal were tested through a quantitative comparison with the EyeLink 1000 Plus, a professional eye tracker. Based on the encouraging registered results, we think that it is possible to adopt the presented system as a tool to quickly retrieve reliable gaze information.

Data concerning overweight and obesity in children and adolescent populations are alarming and represent one of the most serious public health problems of our time. Moreover, it is demonstrated that the school environment may play an important role in health promotion with regard to nutritional aspects. This article reports the results of a study conducted in the Apulia region (Southern Italy), aimed at providing an integrated surveillance of the behaviors related to nutrition habits in students and the hygienic and nutritional conditions of the school’s canteens attended by enrolled students. To this purpose, a sample of 501 students attending primary school (third class—children approximately eight years old) replied to a validated questionnaire, and official controls (OC), of both food and nutritional safety, were performed in 22 primary schools. A team of healthcare professionals carried out the study, and the implementation of all the prescribed improvement actions were subsequently verified through follow-up OC. The results of our study show a critical situation in the student sample, with 41.3% of children having a weight excess (overweight or obesity). With regard to the children’s behaviors, only 59.8% of children ate at least one fruit or had a fruit juice for breakfast, and 10.8% did not have breakfast at all. Overall, 40.1% of the total children played outdoors the afternoon before the survey and 45% reported going to school on foot or by bicycle. During the afternoon, 83.5% of the sample watched television or used video games/tablets/mobile phones, while 42.3% played sports. The schools had an internal canteen with on-site preparation of meals in 36.4%, the remaining 63.6% received meals from external food establishments. With regard to OC, for the hygienic–sanitary section, eleven prescriptions were issued, in the great part related to the structure and organization of the canteen. For the nutritional section, nine corrective actions were prescribed, mainly related to official documents and management. The follow-up OC showed that all prescriptions were subsequently addressed. Eating at school was less frequent among obese and overweight students compared with those with normal weight. Although this evidence needs to be further confirmed, it highlights the potential role that the school canteens may play in health promotion and prevention of nutritional disorders. On the other hand, in order to fulfill its health promotion task, the school canteens have to comply with official regulations and guidelines; therefore, OC during the management of the food service at school are needed.

Despite the growing interest in physiological stress monitoring, an objective measure of stress is currently lacking, especially in clinical and rehabilitation contexts. With the emerging integration of artificial intelligence (AI) in data analytics, heart rate variability (HRV) has gained attention as an effective biomarker; however, the literature remains fragmented across disciplines, stress types, and methodological approaches. This scoping review aims to investigate how AI techniques are applied to HRV analysis for stress detection and prediction in adult populations. Although this review does not focus on a specific subtype of stress, its primary objective is to explore the current methodological state of the art as reported in the literature, without restrictions on stress typology. Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, and Google Scholar for studies published between 2005 and 2025, using MeSH terms including “HRV”, “Rehabilitation”, “SCI” (for Spinal Cord Injury), “Stress”, “Sympathetic”, “Parasympathetic”, “Non-linear”, “Gamification”, “AI” and “Machine Learning”. Inclusion criteria targeted adult human populations and studies employing HRV features as input for AI and machine learning techniques for psychophysical stress assessment. Of the 566 records identified, 15 studies met the eligibility criteria. The reviewed studies exhibit substantial heterogeneity in terms of settings, populations, sensors, and algorithms with most employing supervised methods (e.g., random forest, support vector machine), alongside several applications of deep learning and explainable AI. Only one study focused specifically on physiological stress, none focused on SCI populations, and rehabilitation-related research was scarce, thereby underscoring important gaps in the current literature. Overall, HR variability analysis, especially when combined with artificial intelligence techniques, represents a promising approach for stress assessment; however, the field is methodologically fragmented and clinically underdeveloped in critical areas, underscoring the need for a multidisciplinary methodological framework.