Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical solutions. The BullyBuster project pioneers a multi-disciplinary approach, integrating artificial intelligence (AI) techniques with psychological models to comprehensively understand and combat these issues. In particular, employing AI in the project allows the automatic identification of potentially harmful content by analyzing linguistic patterns and behaviors in various data sources, including photos and videos. This timely detection enables alerts to relevant authorities or moderators, allowing for rapid interventions and potential harm mitigation. This paper, a culmination of previous research and advancements, details the potential for significantly enhancing cyberbullying detection and prevention by focusing on the system’s design and the novel application of AI classifiers within an integrated framework. Our primary aim is to evaluate the feasibility and applicability of such a framework in a real-world application context. The proposed approach is shown to tackle the pervasive issue of cyberbullying effectively.

The security of modern smartphones is related to the combination of Continuous Authentication approaches, Touch events, and Human Activities. The approaches of Continuous Authentication, Touch Events, and Human Activities are silent to the user but are a great source of data for Machine Learning Algorithms. This work aims to develop a method for continuous authentication while the user is sitting and scrolling documents on the smartphone. Touch Events and Smartphone Sensor Features (from the well-known H-MOG Dataset) were used with the addition, for each sensor, of the feature called Signal Vector Magnitude. Several Machine Learning Models have been considered with different experiment setups, 1-class, and 2-class, for evaluation. The results show that the 1-class SVM achieves an accuracy of 98.9% and an F1-score of 99.4%, considering the selected features and the feature Signal Vector Magnitude very significant.

The smartphone is an excellent source of data; it is possible to extrapolate smartphone sensor values and, through Machine Learning approaches, perform anomaly detection analysis characterized by human behavior. This work exploits Human Activity Recognition (HAR) models and techniques to identify human activity performed while filling out a questionnaire via a smartphone application, which aims to classify users as Bullying, Cyberbullying, Victims of Bullying, and Victims of Cyberbullying. The purpose of the work is to discuss a new smartphone methodology that combines the final label elicited from the cyberbullying/bullying questionnaire (Bully, Cyberbully, Bullying Victim, and Cyberbullying Victim) and the human activity performed (Human Activity Recognition) while the individual fills out the questionnaire. The paper starts with a state-of-the-art analysis of HAR to arrive at the design of a model that could recognize everyday life actions and discriminate them from actions resulting from alleged bullying activities. Five activities were considered for recognition: Walking, Jumping, Sitting, Running and Falling. The best HAR activity identification model then is applied to the Dataset derived from the “Smartphone Questionnaire Application” experiment to perform the analysis previously described.

This study establishes a correlation between computer science and psychology, specifically focusing on the incorporation of smartphone sensors and users' personality index. A limited number of state-of-the-art approaches have considered these factors, while no existing dataset currently encompasses this correlation. In this study, an Android application was developed to implement a questionnaire on bullying and cyberbullying, using smartphone sensors to predict Personal Index. Sensor data are collected in the “UNIBA HAR Dataset” and were analyzed using AI algorithms to find a correlation between the categorization class of the questionnaire (Personality Index) and the prediction of ML behavioral models. The results indicate that the Bayesian Bridge with "Bullying bully vs. Victimization bullying" and "Total bullying vs. Total victimization" performs better on average 0.94 accuracy, and the LSTM with the last categorization performs 0.89 accuracy. These results are crucial for future development in the same research area. Graphical abstract

This study presents Care-MOVE, a smartphone-based application designed for continuous, passive, and unobtrusive monitoring of mobility, environmental exposure, and cognitive status in older adults within a telemedicine framework. The system integrates movement-related data collected through smartphone sensors (GPS, activity recognition, and caloric expenditure estimation) with contextual air quality information and standardized neuropsychological assessments, resulting in a comprehensive multimodal dataset (Care-MOVE Dataset). An exploratory proof-of-concept study was conducted on a subsample of 53 participants aged over 65, each monitored continuously for five days, contributing on average more than 30,000 longitudinal records. To investigate whether daily motor behavior can serve as a digital biomarker of cognitive functioning, several Machine Learning and Deep Learning models were evaluated using a Leave-One-User-Out (LOUO) cross-validation strategy. The comparative analysis included traditional classifiers (Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines) as well as temporal deep learning architectures (1D CNN, LSTM, GRU, and Transformer). Among all of the evaluated approaches, the Support Vector Machine with RBF kernel achieved the best performance, reaching an accuracy of 98.1%, a balanced accuracy of 0.988, and an F1-score of 0.981, demonstrating robust generalization across unseen subjects. For this reason, the study was designed and presented as an exploratory proof-of-concept rather than a definitive clinical validation. This integrated approach not only enables the collection of detailed and contextualized data but also opens new perspectives for proactive digital healthcare, focused on risk prevention, improving quality of life, and promoting autonomy in elderly patients.