درخت حوزه‌های تخصصی

Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.

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