The integrated collection of personal health data represents a relevant research topic, which is enhanced further by the development of next-generation mobile networks that can be used in order to transport the acquired medical data. The gathering of personal health data has become recently feasible using relevant wearable personal devices. Nevertheless, these devices do not possess sufficient computational power, and do not offer proper local data storage capabilities. This paper presents an integrated personal health metrics data management system, which considers a virtualized symmetric 5G data transportation system. The personal health data are acquired using a client application component, which is normally deployed on the user’s mobile device, regardless it is a smartphone, smartwatch, or another kind of personal mobile device. The collected data are securely transported to the cloud data processing components, using a virtualized 5G infrastructure and homomorphically encrypted data packages. The system has been comprehensively assessed through the consideration of a real-world use case, which is presented.

This paper focuses on key areas in which artificial intelligence has affected the chess world, including cheat detection methods, which are especially necessary recently, as there has been an unexpected rise in the popularity of online chess. Many major chess events that were to take place in 2020 have been canceled, but the global popularity of chess has in fact grown in recent months due to easier conversion of the game from offline to online formats compared with other games. Still, though a game of chess can be easily played online, there are some concerns about the increased chances of cheating. Artificial intelligence can address these concerns.

UNSTRUCTURED The solutions proposed so far to the problem of recognising a chess diagram from an image containing a standard chess set are not usable in a real scenario, because the constant mistakes in recognising different chess pieces, such as pawns versus bishops, for example, make the whole recognition process useless. There is a great need to combine the computing power of chess engines with a physical chessboard, especially during individual chess training. Therefore, this article proposes a method for identifying chess positions on a two-dimensional chess set, which allows obtaining close to perfect accuracy during the classification of chess pieces. The generation of a custom data set using the specific Magne 2D chess set, which was designed in 2019 and is the first of its kind in the world, allowed the proposed model to have the aforementioned accuracy. Furthermore, the recognition process is supported by a cross-platform mobile application, which includes the necessary artificial intelligence features to recognize a chess diagram.

UNSTRUCTURED This paper focuses on key areas in which artificial intelligence has affected the chess world, including cheat detection methods, which are especially necessary recently, as there has been an unexpected rise in the popularity of online chess. Many major chess events that were to take place in 2020 have been canceled, but the global popularity of chess has in fact grown in recent months due to easier conversion of the game from offline to online formats compared with other games. Still, though a game of chess can be easily played online, there are some concerns about the increased chances of cheating. Artificial intelligence can address these concerns.