درخت حوزه‌های تخصصی

In the theory of traffic flows the main characteristics are: intensity, speed, and density.  They make it possible to use hydrodynamic models. In connection with the development of modern highways and road networks, traffic flows behavior is becoming more and more complex and diverse. In particular, the B.Kerner studies have shown that the laminar solution of hydrodynamic models is poorly correlated with experimental data. Our research team is developing tools for intelligent monitoring of traffic flows on fragments of the road network with different geometries.  The paper presents a project of a client-server system, which allows obtaining, in real-time, information regarding the basic characteristics of traffic flows at the junction of any configuration using mobile devices. The automation of obtaining characteristics is based on the application of image recognition algorithms (virtual detection method).

Digital technologies are actively implemented in all areas of tourism. The article identifies the main areas of balancing interests in the field of tourism on the basis of optimization of digital marketing tools. Analytical research has shown that a set of sales channels in the Internet environment and a set of tools are formed, depending on the marketing strategy of the travel company. The main tactics and tools of digital marketing in tourism are highlighted. These are: the company's website, digital marketing channels - online channels for promoting and attracting customers: SEO, online advertising, email marketing, sales funnel, content marketing, teaser advertising, SMM, etc. The study proposes the application of portfolio investment theory to optimize investment in digital marketing tools to achieve a specific goal. The article uses a method of building a mathematical model that will optimally distribute financial investments in digital marketing. Thus balancing of interests in the field of tourism on the basis of digital marketing tools is offered on the basis of KPI indicators, according to the allocated purposes.

This study aims to identify the role of information and communication technology in improving electronic payment methods. The descriptive-analytical approach has been applied. We have learned about the concepts of ICT and electronic payment methods and the impact of ICT on these methods. On the applied side, an El Oued was distributed to a staff sample in the Directorate of the Algeria Poste unite of the State of El-Oued and several its postal institutions. The El Oued included two main axes: the first focuses on the reality of ICT in the organization. We discussed the electronic payment methods and the control procedures on their system on the second axis. Also, the challenges and risks which they face, the study revealed a statistically significant relationship between the variable of ICT and the means of electronic payment through the influence of some dimensions in the first axis on the dimensions of the second axis.

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.

Globalization is one of the important elements of external environment of the enterprises, that is why it is defined to be the main object of this study. The main signs of globalization in international trade and the qualitative / quantitative changes caused are studied, in particular, attention is paid to the mutual exchange of resources to solve international problems, including environmental ones. The transformation of competitive / monopoly relations under the influence of globalization in the areas of levels of cooperation, the ratio of competition / rivalry, the dependence of competition on innovation, models of competitive behavior are analyzed. The impact of globalization on economic processes of influences redistribution in the markets between residents and nonresidents is investigated, the factors that cause changes in investment activity of nonresidents are identified, namely the direct relationship between changes in the index of economic globalization and the number of foreign enterprises in Ukraine. Based on the research, the identified exogenous factors are complemented by such factors as protection of intellectual property rights and prevention of abuse of intellectual property rights, and endogenous - as a relationship of decisive influence on businesses in specific markets, management awareness of the impact of their transactions on the market and others. It is concluded that globalization does not take into account the national characteristics of individual economies, the resource potential of development, but involves the use of existing benefits, respectively, those who want to become part of it must acquire certain exclusivity, disseminate relevant information, introduce innovation in enterprise management systems, show interest in cooperation with the relevant participant for the development of digitalization, social and economic transformations.

The study is aimed at assessing the strategic importance of a business processes for achieving sustainable competitive advantage, therefore, in this article the theoretical approach for measuring the strategic contribution of a business process to an enterprise’s business system is presented.  For evaluating of a business process strategic importance the study proposes the system of economic and managerial indicators, which includes the process’ contribution to the added value, its compliance to critical success factors, and its organizational involvement. Сombining these three indicators into one integral allows it to be used in different types of matrix analysis to make decisions on improving of a company’s business system.

For the last three decades, research on Information Technology Business Value (ITBV) has highlighted how organizations can create greater value from IT investments in different industries, including the healthcare sector. There has been some research investigating the business value of Health Information Systems (HIS). However, the current body of knowledge regarding the challenges in front of proper business value realization of these systems is limited. This study investigates the challenges of HIS business value creation using a combination of the Fuzzy Analytic Hierarchy Process (FAHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL). First, this study reviews the literature to identify the main challenges in the field. The outcome of this step revealed three main categories and 22 challenges, including technological (ten challenges), organizational (eight challenges), and environmental (four challenges). Then, this study first uses FAHP to evaluate the weighting for each criterion and then adopts the fuzzy DEMATEL method to establish contextual relationships among those criteria. We find out that technological challenges are the most crucial dimension. Moreover, we observed that “infrastructure costs” has the highest priority. Under the technological dimension, “maintenance costs” and “systems compatibility” challenges are the most critical since they are in the cause area and directly influence the HIS outcomes. Under the organizational dimension, “Change in strategic objectives” is the most important challenge. Moreover, “inter-departmental coordination”, “training costs”, “proficiency”, and “users’ knowledge” are affected by each other as well as influenced by the net causes. The outcome of this study shows that to handle HIS business value creation challenges, healthcare executives should start with infrastructure costs, maintenance costs, and systems compatibility challenges.

K nearest neighbor algorithm is one of the most frequently used techniques in data mining for its integrity and performance. Though the KNN algorithm is highly effective in many cases, it has some essential deficiencies, which affects the classification accuracy of the algorithm. First, the effectiveness of the algorithm is affected by redundant and irrelevant features. Furthermore, this algorithm does not consider the differences between samples, which led the algorithm to have inaccurate predictions. In this paper, we proposed a novel scheme for improving the accuracy of the KNN classification algorithm based on the new weighting technique and stepwise feature selection. First, we used a stepwise feature selection method to eliminate irrelevant features and select highly correlated features with the class category. Then a new weighting method was proposed to give authority value to each sample in train dataset based on neighbor categories and Euclidean distances. This weighting approach gives a higher preference to samples that have neighbors with close Euclidean distance while they are in the same category, which can effectively increase the classification accuracy of the algorithm. We evaluated the accuracy rate of the proposed method and analyzed it with the traditional KNN algorithm and some similar works with the use of five real-world UCI datasets. The experiment results determined that the proposed scheme (denoted by WAD-KNN) performed better than the traditional KNN algorithm and considered approaches with the improvement of approximately 10% accuracy.