"Scientific research" Almanac

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  TRAINING IN THE DOCTORAL PROGRAM "ACCOUNTING, CONTROL AND ANALYSIS OF BUSINESS (ACCOUNTING)" AT D. A. TSENOV ACADEMY OF ECONOMICS – PROBLEMS AND PROSPECTS

  Achieving an economic and social standard of society is related to the need for highly qualified scientists and specialists. Policies and activities in the field of education and science are a guarantee for such achievement. Higher schools face the problem of developing and continuously improving doctoral education, incl. education in the field of ... Achieving an economic and social standard of society is related to the need for highly qualified scientists and specialists. Policies and activities in the field of education and science are a guarantee for such achievement. Higher schools face the problem of developing and continuously improving doctoral education, incl. education in the field of accounting. The purpose of the research is to outline the existing problems and directions for the development of the doctoral program "Accounting, control and analysis of business activities (accounting)" at D. A. Tsenov Academy of Economics, based on theoretical and empirical study of training in this programme in the context of the European priorities. The main research thesis is that training at D. A. Tsenov Academy of Economics in a doctoral degree in the field of accounting requires integrated management of all aspects of its regulation, organization and practical implementation, which will contribute to increase the competitiveness of the doctoral programme and its position as a leader in the educational market in the country and abroad. By examining the regulatory and strategic documents in the country and within the EU in the field of education at Doctoral level of tertiary education and by outlining the characteristics of the doctoral programme under study, as well as by applying a survey method to study the motivation and satisfaction of those interested in the programme, the state and prospects for its development and opportunities for increasing its competitiveness have been outlined.

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  INFORMATION SECURITY OF HOSPITALS IN BULGARIA

  The relevance of this study is determined by the increasing requirements of and threats to the information security of healthcare facilities. The aim of the study is to investigate the state of information security of hospitals in Bulgaria and to define the concept of the model for information security. The following tasks are solved: studying the ... The relevance of this study is determined by the increasing requirements of and threats to the information security of healthcare facilities. The aim of the study is to investigate the state of information security of hospitals in Bulgaria and to define the concept of the model for information security. The following tasks are solved: studying the state of information security of hospitals in Bulgaria and opportunities for improvements; highlighting the good practices; analysing the capability of new technologies. On this basis, a concept for an information security model is developed, taking into account the features of Bulgarian hospitals. The research thesis, which is defended, is that threats and information security requirements have been steadily increasing. This requires precautionary measures and a high level of security and protection of data and services, as well as business continuity and is particular important to hospitals that collect, store and process personal data and health data of their patients. The information security level can be increased by applying an information security model. The studies have analysed the state of information security of medical establishments in Bulgaria and defined a concept for an information security model based on: good practices; following the recommendations for technical implementation required by the GDPR; accounting for the principle of protection levels.

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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.