درخت حوزه‌های تخصصی

The purpose of this study is theoretical and methodological substantiation, deepening of the conceptual provisions and development of the scientific and practical recommendations on the formation of mechanisms for crime prevention in the border regions of the southern of Ukraine. The relevance of this study is due to the need to solve the problem crime by introducing precautionary measures in the border regions of the southern of Ukraine (Bondarenko et al., 2021).The study proved the direct dependence of the level and intensity of crime on the proximity of areas to areas of significant social and criminological tension, to areas of instability and armed conflict, which contributes to the militarization of the population and increasing psychological preparedness for conflict and its antisocial methods (Iatsyshyn et al., 2020). It is established that in the border regions of the south of Ukraine the growth of criminal crime for the studied period significantly exceeds the data for Ukraine. The threats to irredentism and separatism, international terrorism, organized crime of a transnational global scale are real for the southern regions of Ukraine: economic smuggling, smuggling of weapons, ammunition and means of terror, drug trafficking, unregulated migration, trafficking in human beings and their organs. Priorities for Ukraine have been set task of crime prevention: introduction of the newest technologies of protection of a law and order, attraction of scientific and industrial potential of regions, establishment of coordination and interaction between subjects of maintenance of frontier safety at all levels (frontier, national, interstate and international) and cross-border interaction with the international organizations of fight against organized crime. Based on international experience, crime prevention measures in the border regions of southern Ukraine are proposed in the following areas: general social precautionary measures; special criminological precautions;international cooperation in the development of cross-border cooperation;introduction of automated security systems and crime prevention; informational and educational activities of formationlegal culture among the population and the public

Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called Long Short-Term Memory (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.

The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally.