International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Artificial intelligence (AI) and machine learning techniques have revolutionized various fields, including 3D modelling of anatomical structures. One such area of research involves the use of AI and machine learning algorithms for approximating spherical harmonics functions in the realm of anatomical structure modelling.
Spherical harmonics functions are mathematical tools that describe functions on the surface of a sphere. In 3D modelling of anatomical structures, these functions are employed to represent complex surface details with accurate precision. However, calculating values of these functions for complex anatomical structures is time-consuming and prone to errors. This is where AI and machine learning come into play.
Using AI and machine learning algorithms, we have developed models that can automatically learn the inherent patterns and complexities of anatomical structures from vast amounts of training data. These models can then approximate the spherical harmonics functions that accurately represent the surface details of these structures. This automation significantly reduces the time and effort required in the 3D
modelling process.

The new platforms coming out on the social media space are faced from the beginning with a fierce contest. The competition for user attention is hard and the main players (Facebook, TikTok, Instagram, Reddit) have a huge advantage. To overcome these problems many of the newly lunched platform are making use of gamification strategy to keep the user engaged. The gamification strategy usually provides the user with a form of digital reputation but on some platform, it can be even a form of digital currency which can be exchanged for benefits or real products. To validate the user actions there is not always an automatic process, and the platforms are relaying on the community to cross-validate the authenticity and the value of the respective actions. The validation of the user actions can be automated but not always. When the validation cannot be automated, and the task requires human intervention the process is in his most part outsourced to the community and only a small number of the validation will be done by platform operators. The validation done by the platform operators will give credits to the users whose actions were validated. After certain amount of credit is given to a user, he became trusted, and his action can validate the actions of other users. The trusted user will act almost as a platform operator by validating other users’ actions. In this paper we simulate and analyse how the trust can be propagated into different social networks configuration. We start by defining a mathematical framework for modelling the users and the actions. After we introduce two different algorithms for propagating the trust in the community and finally for each configuration, we simulate the propagation of the trust and discuss the results.