درخت حوزه‌های تخصصی

The main role of financial reporting in capital markets is to provide the necessary conditions for the optimal allocation of resources and making correct and timely decisions. Achieving this goal is possible if the financial statements are consistent with economic realities or even have the slightest deviation from economic performance. However, over the past few decades, fraud detected in corporate financial reporting has increased the risk of financial reporting. Therefore, the current paper aims at investigating the impact of internal control weaknesses on financial reporting risk in companies listed on the Tehran Stock Exchange. In order to achieve the purpose of the research, using the method of systematic elimination of information related to 143 companies among the companies listed on the Tehran Stock Exchange in the period from 2009 to 2019 was collected. A multivariate regression model based on composite data was used to test the research hypothesis. The research findings show the significant positive impact of internal control weaknesses on financial reporting risk. The results indicate that reducing the weaknesses of internal control can reduce the risk of financial reporting and reduce information asymmetry and consequently improve accountability processes.

Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank.

Management of innovation processes is one of the functions of local governments and, therefore, they should be the initiators and moderators of communication between research organizations and enterprises. The program formation of agglomeration innovative development involves the creation of promoting innovation body, which allows to achieve the maximum involvement degree of all the participants in the innovation process. The article is devoted to the research of urban agglomeration innovative development, the need to create a special body or center for innovation, which will form a set and interconnected, and will be integrated into the urban agglomeration and carry out innovation and technological activities as part of research and production infrastructure. The article develops a method for predicting the effectiveness of the advancement of this body through the digital space using trend models. It is expected to receive three forecasts: optimistic, realistic and pessimistic. This will accelerate the establishment of links between the players of the regional innovation market and contribute to a qualitative change in the spatial and functional structure of urban agglomerations. The development of information and communication technologies allows to create effective systems that will stimulate the agglomerations innovative development . Therefore, the communicative activities of regional governments should be carried out through the use of information and communication technologies. Thus, the urgency of developing a methodology for assessing the increase of the innovative component of agglomeration economic development is due to the low percentage of implementation research results, low science-intensive gross value added in the Ukrainian regions, the possibility of using information and communication technologies. Increasing the number of targeted visits will simplify and speed up the process of establishing links between innovation market players at the agglomeration spatial level in both the short and long term

The purpose of this study is to develop proposals and recommendations for the implementation of a process model for the development of leadership qualities of public servants and justification of the conditions for ensuring its effectiveness in terms of digital transformation. The relevance of this study is due to the need to ensure development of the process of professionalization of the senior civil service personnel on the basis of development of leadership qualities that will contribute to the effective operation of the civil service of Ukraine, change management and successful implementation of reforms in Ukraine, taking into account the best world practices. The methodology for assessing the level of managerial competencies of public servants according to the degree of implementation of strategic (key) competencies has been developed. The assessment of managerial competencies according to the degree of their significance for civil servants, the expert group identified the most important management competencies. An approach to understanding has proposed interaction of leadership competencies with managerial competencies, a diagnostic model for assessing the leadership of public servants has been developed. To implement the model, a system of indicators has been developed - single, complex and integrated indicators of civil servants' leadership, using tools: a tree of civil servants' leadership indicators, matrices for the calculated civil servants' leadership indicator, measurement scales for the corresponding level of indicators.

On a global scale, one of the prevalent causes of dementia is Alzheimer’s disease (AD). It will cause a steady deterioration in the individual from the mild stage to the severe stage, and thus impair their capacity to finish any tasks with no aid. The diagnosis is done with the utilization of existing methods which include medical history; neuropsychological testing as well as MRI (Magnetic Resonance Imaging), a lack of sensitivity as well as precision does affect the consistency of efficient procedures. With the deep learning network’s utilization, it is possible to create a framework for detecting specific AD characteristics from the MRI images. While automatic diagnosis is done with the application of diverse machine learning techniques, the existing ones do suffer from certain constraints with regards to accuracy. Thus, this work’s key goal is to increase the classification’s accuracy through the inclusion of a pre-processing approach prior to the deep learning model. The Alzheimer's disease Neuroimaging Initiative (ADNI) database of AD patients was used to develop a deep learning approach for AD identification. In addition, this study will present ideas for Haralick features, feature extraction from Local Binary Pattern (LBP), Artificial Neural Network (ANN), and Visual Geometry Group (VGG)-19 network techniques. The results of the experiments show that the deep learners offered are more effective than other systems already in use.

The increase in non-performing loans considerably reduces profit in the banking systems. To solve these problems, factors influencing the collection of non-performing loans have to be examined in a hopeful attempt to reduce it to an ac-ceptable level. The present study is conducted to analyze and study factors influ-encing the collection of non-performing loans in Shahr Bank. This research is an applied study regarding its goal. It was conducted in two sections, namely the qualitative and quantitative sections. In the qualitative sections, the factors influ-encing the collection of receivables were identified using the theoretical literature and interviews with senior managers of Shahr Bank through the encoding process. In the quantitative section, data was collected by surveying the opinions of 12 experts, including senior managers of Shahr Bank in 2020 using a questionnaire. Thereafter, the factors were selected using the fuzzy Delphi technique and the relationships between them were determined using the fuzzy DEMATEL method. Finally, the factors were weighted and prioritized using the fuzzy ANP method. The research findings showed that non-performing loans can be collected through six types of factors including organizational, regulatory, customer, banking, envi-ronmental, and operational factors. The environmental factor is the most influential factor, while the operational factor is the most influenced and most important factor. In addition, behavioural, contextual, and structural sub-factors have the highest level of importance in the collection of non-performing loans in the order mentioned. These findings can help bank managers make decisions to improve the collection of receivables.

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.

An administrator is employed to identify network security breaches in their organizations by using a Network Intrusion Detection and Prevention System (NIDPS), which is presented in this paper that can detect and preventing a wide range of well-known network attacks. It is now more important than ever to recognize different cyber-attacks and network abnormalities that build an effective intrusion detection system plays a crucial role in today's security. NSL-KDD benchmark data set is extensively used in literature, although it was created over a decade ago and will not reflect current network traffic and low-footprint attacks. Canadian Institute of Cyber security introduced a new data set, the CICIDS2017 network data set, which solved the NSL-KDD problem. With our approach, we can apply a variety of machine learning techniques like linear regression, Random Forest and ID3. The efficient IDPS is indeed implemented and tested in a network environment utilizing several machine learning methods. A model that simulates an IDS-IPS system by predicting whether a stream of network data is malicious or benign is our objective. An Enhanced ID3 is proposed in this study to identify abnormalities in network activity and classify them. For benchmark purposes, we also develop an auto encoder network, PCA, and K-Means Clustering. On CICIDS2017, a standard dataset for network intrusion, we apply Self-Taught Learning (STL), which is a deep learning approach. To compare, we looked at things like memory, Recall, Accuracy, and Precision.