درخت حوزه‌های تخصصی

The object of the research is the economic dynamic of a depressed low-productive economy, as exemplified by the modern Ukrainian economy. With the purpose to analyze the main causes and consequences of such an economy, the characteristic tendencies of its functioning, activation of the processes of reformation and growth, there was developed the dynamic economic model with significant share of consumer import and export of raw materials, with the use of IT technology. The model also takes into account the socio-economic clustering of a society. It is formalized in the space of economic variables, which describes respectively the savings (liquid capital) of the main social groups involved in the economy, the prices of consumer products in the domestic and foreign markets, the price of exported raw materials for the exchange rate.The model is intended for qualitative analysis of the processes occurring in the economy described by it, and for conducting computational experiments on the basis of information technologies in simulation mode with its parameters, which allows to identify the most characteristic features of the object of the research. The results of experimental researches with the model allowed to establish the main periods of economic dynamics represented by the dynamic variables model, its specificity and social consequences for the society, which is characterized by a bimodal distribution of savings. There was investigation the dependence of the mode solution from it parameters which means in practice the dependence of economic dynamics from the change of individual conditions and factors of economic development.The obtained results are important for informational and analytical support of decision making processes in order to bring the being studied economy out of a low-productive state.

Purpose: The purpose of the research was to understand the cross media electronic marketing process. In fact, with the emergence of virtual organizations, social network media marketing has become a basic need for the possibility of movement and continued developmentMethod: this research conducted using Grounded theory analysis. Snowball or targeted sampling is utilized to gather the required data. The qualitative part of this study was conducted based on the viewpoints of 24 university professors, marketing activists, experts, elites, managers, and employees of insurance companies. Collected interviews were analyzed in MAXQDA software. To achieve this goal, the collected interviews are evaluated using different coding methods as the main process. The interviews were entered into the MAXQDA software. Coding in data theory methodology is a process of conceptual abstraction by assigning general concepts (codes) to individual occurrences in the data.Findings: Based on the analysis, 104 primary codes, 18 core categories have been identified in 6 categories of the foundation's data. With the emergence of electronic commerce, organizations are changing their business. The proposed model shows that the digital era has changed the expectations of customers from communication with brands. Before social networks, mass media communication channels such as television and radio to brand managers. It allowed them to communicate with consumersConclusion: In the present research, based on the participants' viewpoints, the categories of management factors challenge, financial potential, communication potential, customer characteristics and welfare and comfort, finally, marketing techniques have been identified and it has been related to another broader category called causal conditions. Information technology and targeted marketing were identified as background conditions. Environmental factors and psychological factors of the clients were identified as intervening conditions. Research strategies include business improvement, knowledge and education, and finally innovation and creativity. Outcomes include customer loyalty, sustainability in customer relationships with the product and social media, beneficial word-of-mouth and viral advertising, customer satisfaction, and purchase intent.

Agriculture is one of the essential sources of occupation and revenue in India. Conferring to existing statistics, most agriculturalists are facing severe losses due to poor farming yield. Farming activities are challenged by various environmental factors that affect agricultural productivity to a greater extent. The present farming situation is above the average of the process involves more biochemical bases for managing the diseases and other destructing facts. The foremost problems they are facing in day-to-day farming tasks are crop or plant diseases affecting productivity. Also, the growth of weeds along with field crops has been another challenge.  The technology has developed to rectify the problems using some machine learning algorithms like Random Forest algorithms, Decision trees, Naïve Bayes, KNN, K-Means clustering, Support vector machines. The result has been evaluated and observed through the performance evaluation metrics using confusion matrix, accuracy, precision, Sensitivity, specificity with the observations, research, and studies. The statistics have expressed the overall accuracy of 98% by achieving the detection of diseases in plants and by removing the weeds that ruin the growth of plants.

Flash floods are considered to be one the worst kind of hazard. They are characterized by their suddenness, rarity, small scale, heavy rain and peak discharge, unpredictable, fast and violent movement. It has severe effects on human society in the form life losses, damages to property, roads, communication and on natural settings. Advances in hydrology, meteorology, engineering, using of GIS and remote sensing still not able to increase real time forecast. Researchers from developed countries have stressed to more focus to improve very short time an effective early warning system with collaboration of local communities for flash flood risk supervision. The data were combined with the Geographical Information System to analyze the temporal and spatial distribution of flood events in Salah Al-Din Governorate and Ramadan Valley. The analysis of the spatial distribution of the floods proves that most of the occurrences are recorded in the southern part of the study area. Most of the flooded areas in the study area are mainly pre-classified areas within the areas threatened by the flood due to the low level of its surface and its proximity to the course of the main valley (Wadi Jarnav), which flows into the Tigris River. The proposed method estimates the localization of sites prone to flood, and it may be used for flood hazard assessment mapping and for flood risk management. It was therefore, suggested that government agencies and policy makers should adopt this powerful technique for reliable and well synthesized information which is a vital component of flood risk assessment and planning.

This study aims at investigating the factors that affect a user’s satisfaction of accounting information systems based on the quality and the perceived usefulness of such systems, and the quality of data involved. The paper analyses the given factors on data collected from Bank Sumut,  Indonesia. In addition, this research also aims to find out whether the influence of perceived ease of use can be utilized as a moderating variable or not. the research is quantitative in terms of methodology and the sample pool consists of 112 people. The data used is primary data, which has been collected through the distribution of a questionnaire. Data has been analyzed through the Multiple Linear Regression and moderator test has been done using the Eviews 9 and SPSS software. The results contained in this study show that the variables can influence the accounting information system (AIS) user satisfaction significantly and that the information system quality plays an important role in the process. In addition, the quality of information systems, the information quality and perceived usefulness simultaneously affect the AIS of user satisfaction.

The worldwide Covid-19 epidemic while affecting numerous places, has had a profound effect on virtual advertising and advertising and, globally, in the provinces and at the neighborhood level. except, this effect for the most element become positive, in contrast to what has seen in exclusive sectors which include economy, human sources, etc., whilst contamination reasons a variety of incapacity amongst clients and advertisers alike, in phrases of welfare, social work, inflation, business, and many others., the equal shifted conduct goes as a long way as running. , investing strength, getting into self-schooling, adopting new programs from the internet, expanding social and hygiene concerns, retaining distance strategies from complete regions, internet-based media willpower, get right of entry to online sources, etc., and this has greatly impacted the display and endorsed efforts. The moral movement has moved past the PC and digital international, which places open doors for advertisers and products to connect with clients more efficiently than ever before. With the arrival of expanded online media and the call for pc-generated content material, the evolved Media have given advertisers a part of the monetary freedom. At the equal time, this has, in turn, enabling advertisers to be extra proactive and to engage with the public at the same time as appearing excessive excellent demonstration programs. The purpose of this study is to explore, investigate, and recognize the effect of coronavirus on the digital market and businesses.

In recent privacy has emerged as one of the major concerns of deep learning, since it requires huge amount of personal data. Medical Image Analysis is one of the prominent areas where sensitive data are shared to a third party service provider. In this paper, a secure deep learning scheme called Metamorphosed Learning (MpLe) is proposed to protect the privacy of images in medical image analysis. An augmented convolutional layer and image morphing are two main components of MpLe scheme. Data providers morph the images without privacy information using image morphing component. The human unrecognizable image is then delivered to the service providers who then apply deep learning algorithms on morphed data using augmented convolution layer without any performance penalty. MpLe provides sturdy security and privacy with optimal computational overhead. The proposed scheme is experimented using VGG-16 network on CIFAR dataset. The performance of MpLe is compared with similar works such as GAZELLE and MiniONN and found that the MpLe attracts very less computational and data transmission overhead. MpLe is also analyzed for various adversarial attack and realized that the success rate is as low as . The efficiency of the proposed scheme is proved through experimental and performance analysis.

The purpose of this study is to study the indicators for evaluating the effectiveness of the implementation of investment processes on the securities market, taking into account the scientific foundations of information management systems and analysis of indicators of financial efficiency of the investment function of the securities market in Ukraine. The relevance of this study is due to the growing importance of management information systems in all sectors of the Ukrainian economy, in particular, the provision of solutions to the problems of activating investment processes in the securities market of Ukraine by analyzing and reassessing the effectiveness of investment processes at this level, taking into account the scientific basis of management information systems.  A set of indicators that best reflect the implementation of the investment function of the Ukrainian securities market is proposed. A matrix of characteristics of investment processes in the securities market is proposed. It is argued why domestic and foreign investors prefer local securities market indices when making investment decisions. Through the implementation of correlation-regression models, it has been proven that, on average, 87% of changes in investments in securities are due to changes in the number of licensed entities, which on the Chedoch scale indicates a close relationship between the indicators. The results obtained using statistical inference methods indicate a high impact of both external macroeconomic factors that inhibit the development of the securities market and internal, which in turn is reflected in the indicators of assessing the effectiveness of investment processes in the securities market.

The main aim of this thesis is to investigate the factors that could affect the students to adopt e-learning system in Libyan universities. This study is quantitative approach, a questionnaire was adopted from previous studies and distributed among the students to collect the data. The sample of the study consists 365 students from Libya. AMOS software was used to analysis the data. The results indicated that Performance Expectancy, Efforts Expectancy, Facilitating Conditions, Habit and Trust have significant impact on behavioural intention. Moreover, the relationship between Behavioural intention and use behaviour is also significantly positive. However, the relationship of social influence and behavioural intention was not found significant. Finally, the moderation effect was significant and supported between social influence, trust, and Habit with behavioural intention.

Learning effective segment depictions and resemblance measures are fundamental to the recuperation execution of a substance based picture recuperation (CBIR) structure. Regardless of wide research tries for a significant long time, it stays one of the most testing open gives that broadly impedes the achievements of real-world CBIR structures. The key test has been credited to the extraordinary "semantic hole" subject that happens between low-level photo pixels got by technologies and raised close semantic thoughts saw by a human. Among various techniques, AI has been successfully analyzed as a possible course to interface the semantic gap in the whole deal. Impelled by late triumphs of significant learning techniques for PC vision and various applications, in this paper, we try to address an open issue: if significant learning is a longing for spreading over the semantic gap in CBIR and how much updates in CBIR endeavors can be cultivated by exploring the front line significant learning methodology for learning feature depictions and likeness measures. Specifically, we explore a structure of significant learning with application to CBIR assignments with a wide game plan of definite examinations by investigating front line significant learning methodologies for CBIR endeavors under moved settings. From our exploratory examinations, we find some encouraging results and compress some huge bits of information for upcoming research.