"Scientific research" Almanac

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  EFFECTS OF GOVERNMENT BUDGET BALANCE ON ECONOMIC GROWTH

  The necessity of adequate use of public budget as a fiscal instrument for economic regulation is even more tangible in conditions of slow economic growth rate. The debate on whether the public budget can maintain a balance between economic stability and social equity has a long history. Another question is whether budget deficit or budget surplus ... The necessity of adequate use of public budget as a fiscal instrument for economic regulation is even more tangible in conditions of slow economic growth rate. The debate on whether the public budget can maintain a balance between economic stability and social equity has a long history. Another question is whether budget deficit or budget surplus would trigger higher economic growth. The main objective of the empiric analysis is to evaluate the budget deficit/ surplus impact on real economic growth rate by using econometric analysis and comparative analysis between Bulgaria and the other Member States of the European Union as well as the Balkan States (Cyprus, The Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, Slovenia, Romania, Croatia, Greece, Serbia, Turkey and North Macedonia). The specific task is to justify the need to balance the public budget and reduce the budget deficit in order to achieve sustainable real economic growth. The thesis of the research is that balancing public budget and reducing distributive and redistributive government activities contribute to higher real economic growth rate in the analyzed Bulgarian and European economies. The main results of the research prove that reduction of budget deficit and balanced government budget have a positive effect on realized economic growth rate in the analyzed European countries over the period 2007 – 2018.

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  AGILE PROJECT MANAGEMENT IN PUBLIC SECTOR ORGANIZATIONS

  One of the latest approaches to improve the operations of business organizations is the Agile Project Management. It emerged in the software industry in 2001, but gradually entered other, non-software industries. However, the transfer of the approach to the public sector requires a specific transformation of the Agile methodology, insofar as the ... One of the latest approaches to improve the operations of business organizations is the Agile Project Management. It emerged in the software industry in 2001, but gradually entered other, non-software industries. However, the transfer of the approach to the public sector requires a specific transformation of the Agile methodology, insofar as the two sectors are radically different. The purpose of this study is to present a methodology for agile project management in the public sector in Bulgaria and to define the conditions and barriers for its effective implementation in public sector organizations. The tasks of the research are the following: 1) scientific literature research and critical analysis related to the object of the study; 2) developing a methodology for agile project management in the public sector; 3) approbation of the methodology and definition of the conditions for its implementation. The thesis of the research states that agile project management methodology can be adapted and integrated into public sector practice, although further efforts are needed to create organizational sensitivity to implement this approach. As a result of the study, a methodology for agile project management in the public sector in Bulgaria has been developed, which includes basic principles, specific roles and preconditions. Three stages of the methodology are formed, each with steps and iterations that are visualized through a process map. The conditions and barriers to the implementation of the methodology in public sector organizations are outlined.

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  DEEP NEURAL NETWORKS FOR DETECTION OF CREDIT CARD FRAUD

  This publication presents research on the possibility of using Deep Neural Net-works (DNNs) for detection of credit card fraud. The research is based on an open da-taset and aims to determine how the varying number of hidden layers and other archi-tectural parameters (width of each layer, activation functions) affects training and predictive ... This publication presents research on the possibility of using Deep Neural Net-works (DNNs) for detection of credit card fraud. The research is based on an open da-taset and aims to determine how the varying number of hidden layers and other archi-tectural parameters (width of each layer, activation functions) affects training and predictive potential of the network. It also aims to determine how to use generative-adversarial approach efficiently in order to overcome the problem with the imbalance between legitimate and fraudulent transactions, which is crucial characteristic of the datasets related to detection of credit card fraud. The publication presents the results of various experiments, conducted to achieve the stated goals. A number of experiments have been carried out combining network training of various depth of the network and with different parameters of the learning process, for example, activation function and dropout values. The experiments focus on using generative-adversarial approach as a method for oversampling, in par-ticular. The results indicate that deep neural networks with moderate hidden layers, combined with methods for oversampling are performing much better in identifying fraudulent transactions than the classical neural networks, consisting of only one hid-den layer. The results also indicate that using generative-adversarial approach as a method for oversampling is comparable in efficiency to other widely used techniques.