"Scientific research" Almanac

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  INTERESTS AND BEHAVIOUR – MANAGERIAL ASPECTS

  Managing employees’ behaviour is a challenge that every leader has to face and is closely related to managing their interests. Changing human behaviour is a gradual process because behavioural change is the most difficult and slowest to achieve, and the outcome is expected in the long run. The main objective of this research is to study the ... Managing employees’ behaviour is a challenge that every leader has to face and is closely related to managing their interests. Changing human behaviour is a gradual process because behavioural change is the most difficult and slowest to achieve, and the outcome is expected in the long run. The main objective of this research is to study the theoretical and empirical aspects of the categories of “interests” and “behaviour” in terms of their functional and rational integration in the management process. The research thesis is based on the assumption that interests and behaviour are interrelated and interactive in the management process. To prove the thesis, the authors have set the following tasks: • to review and analyse the relationship between “interests” and “behaviour” in the field of management; • to present theoretical grounds for the impact of interests on behaviour; • to conduct an empirical survey for verification of the thesis; • to summarize and systematize the scientific and empirical results and outline the contributing points. The conducted research confirms the research thesis, outlining the scientific and applied results of the research as follows: a generalized, systematized and analysed conceptual apparatus, which enriches the scientific theory in the field of management and administration; opportunities for influence and interaction between interests and behaviour are presented; techniques and mechanisms ensuring the sustainable management of the organization are explored. Recommendations and proposals for balanced and sustainable development of internal organizational relations are presented.

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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.