to download project abstract/base paper of detection


This paper deals with those measures against overvoltage taken when protecting buildings occupied by people or animals. Such over voltages can appear in the case of ground fault if the grounding system of the building is located within a potential funnel of a transmission tower’s grounding system. This paper presents an entire concept regarding protection against any potential rise within the building. Computations of electrical potential on the grounding system having been performed using data obtained by measurements. This includes equipotential bonding, overvoltage protection of the building, and a transformer substation with metal–oxide varistors and gas-discharge arresters installed on the low-voltage line. A simulation model for the entire overvoltage protection based on known mathematical models has been merged within this paper. Adequate overvoltage protection elements were selected for protection against potential rises in the grounding systems of buildings on the basis of computation results. Index Terms—Gas-discharge arrester (GDA), grounding electrodes, grounding system, metal-oxide varistor, overvoltage protection.

Today, in the age of technology, and while we spend most of our time online, we receive hundreds of information from random sources. We are digital citizens so we have the duty of fighting fake news spreading and controlling our life. In January 2018, the European Commission established a high-level group of experts to advise on policy initiatives to fight fake news and disinformation spread online. The outcome of this group was a report (March 2018) designed “to review best practices in the light of fundamental principles, and suitable responses stemming from such principles”. Among the recommendations of the group was to “invest in research and innovation actions to improve technologies for online media services”.

In this paper, we present our preliminary experiments on applying machine learning techniques for fake news detection. In particular, we studied and developed methods and tools for detecting fake news, also, proposing a methodology for that purpose and implementing an algorithm which allows reporting, respectively detecting fake news articles.

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