This study explores the integration of innovative technologies, such as video games and artificial intelligence (AI), in education, focusing on the use of Deep Reinforcement Learning (DRL) to create engaging educational tools. The project “Deep Neural Tower” presents a video game designed to teach fundamental AI concepts to high school students through interactive gameplay. Developed using the Unity engine and ML-Agents toolkit, the game incorporates AI agents that learn and adapt through interaction with the environment. Students encounter challenges illustrating key AI concepts, such as agent-environment interaction, reward systems, and neural networks, which promote active learning and knowledge retention. Tested with 111 high school students, the game showed positive results in usability and engagement, enhancing understanding of complex AI concepts. This paper discusses the pedagogical benefits of using AI in education and concludes that combining advanced AI techniques with interactive gameplay has immense potential to transform STEM education.

Smith–Magenis syndrome (SMS) is a rare, underdiagnosed condition due to limited public awareness of genetic testing and a lengthy diagnostic process. Voice analysis can be a noninvasive tool for monitoring and detecting SMS. In this paper, the cepstral peak prominence and mel-frequency cepstral coefficients are used as disease monitoring and detection metrics. In addition, an efficient neural network, incorporating synthetic data processes, was used to detect SMS in a cohort of individuals with the disease. Three study cases were conducted with a set of 19 SMS patients and 292 controls. The three study cases employed various oversampling and undersampling techniques, including SMOTE, random oversampling, NearMiss, random undersampling, and 16 additional methods, resulting in balanced accuracies ranging from 69% to 92%. This is the first study using a neural network model to focus on a rare genetic syndrome using phonation analysis data. By using synthetic data (oversampling and undersampling) and a CNN, it was possible to detect SMS with high levels of accuracy. Voice analysis and deep learning techniques have proven to be a useful and noninvasive method. This is a finding that may help in the complex identification of this syndrome as well as other rare diseases.

Homomorphic encryption has seen limited application in real-world settings due to its high computational costs, which often impede practical use in latency-sensitive scenarios. Addressing this challenge, this research work presents an efficient, partially homomorphic encryption framework for privacy-preserving ID creation tailored to biometric applications, such as border control or access to secure restricted facilities. The proposed solution leverages the additive homomorphic properties of Paillier and Elliptic Curve ElGamal encryption schemes to encapsulate biometric data within a secure cryptographic layer, enabling rapid verification while minimizing computation and storage demands. Paillier encryption ensures robust security with maximal accuracy, while Elliptic Curve ElGamal optimizes for minimal ciphertext sizes, both meeting rigorous ISO biometric security standards. Experimental results demonstrate that the framework achieves high accuracy with reduced memory and bandwidth requirements, with encrypted IDs as compact as 4 KiB, making it suitable for scalable deployment. This research work represents a key advancement in homomorphic encryption applications, balancing privacy and efficiency without the usual overhead, and making it feasible for real-time biometric processing. In summary, this framework offers a pioneering solution in secure biometric verification, setting a new standard for privacy-preserving applications, positioning it as a promising model for future secure identification systems.