مطالب

The financial potential of agricultural enterprises has important for the development of the domestic economy. Violation of positive dynamics of the development of the country's economic system highlighted the urgent problems and existing prospects for the development of the domestic economy. A special role in this process belongs to the agrarian sector, therefore it is advisable to analyze the state of the financial potential of the agrarian sector enterprises in the context of modern development of the domestic economic system. In the article we have analyzed indicators that characterize the level of enterprises development in the agricultural sector of Ukraine. The article defines the main strategic orientations Increasing the financial potential of an agricultural enterprise should be the effective implementation and use of information technologies, ensuring financial security, financial independence and financial stability. The main sources of financial potential of agricultural enterprises are determined. It has been proven that the use of information technologies allows to speed up and simplify the process of providing financial services to agricultural enterprises, optimize the cost structure and improve their management. It has been proved that such changes are a confirmation of the need to strengthen the financial potential of agricultural enterprises by increasing the shares of additional and reserve capital. It was established that the financial potential of agricultural enterprises is high, but it is characterized with descending dynamics. The financial stability of the enterprise is determined relatively its functioning and possible development prospects. It is considered as the system's ability to maintain planned development despite changes in internal and external factors of influence and their forms of manifestation. Ignoring obstacles and the possibility of overcoming them along with the preservation of internal ones of characteristics is an important property of countering obstacles. A certain set of obstacles and prerequisites for the development of the financial potential of agricultural enterprises allowed to identify the most urgent problem areas, to update and focus attention on the main problems of the potential of enterprises. Definitely problematic the range of questions made it possible to identify the main areas of solution problems of formation and development of the financial potential of enterprises, the formation of a system of its tools, to justify the need to create a concept of support and development of the financial potential of agrarian enterprises.

A greater community of researchers widely studies fog computing as it reduces the massive data flow to the existing cloud-connected network and performs better for real-time systems that expect a quick response. As the fog layer plays a significant role in a fog-cloud system, all of the devices participating in fog computing must be balanced with appropriate load to upstretched the system performance. The proposed method is founded on a tree-based dynamic resources arrangement mechanism that refreshes the fog clusters created using Fuzzy C Mean (FCM) to increase the speed of resource allocation. With the help of Fuzzy rule-based load calculation and intra-cluster job allocation, the load inside the group is maintained. The system also has the facility of inter-cluster job forwarding, which works on demand. A novel load balancing strategy, Real-Time Flexi Forwarded Cluster Refreshing System (RTFRS) is proposed by which all the tasks can be handled efficiently within the fog cloud system. The proposed system is designed so that overall complexity is not upraised and becomes suitable for fog computing architecture with low processing capacity by maintaining the quality of service. Experimental results show that the proposed model outperforms standard methods and algorithms used in fog computing concerning average turnaround time, average waiting time, resource utilization, average failure rate, and the load on the gateway.

In the development process, achieving a better life and increasing well-being is achieved by focusing on access to a life of health and knowledge. The main purpose of this study is to investigate the effect of financial development on human development, including life expectancy and primary enrollment rates in developing countries by gender in the period 2016-2020. The experimental model of this research was estimated using the Generalized method of Moments (GMM) panel method and the variables of facilities granted to the private sector, the ratio of money to GDP, the degree of openness of the economy, the effect of educational expenditures and health expenditures. The results of model estimation indicate that the facilities granted to the private sector have a positive and significant effect on human capital. The effect of the ratio of money supply to GDP is negative due to the destructive effect of inflation on all models. The coefficient of degree of openness of the economy in all models (except the female registration rate) is positive and significant. Educational expenses have a positive and significant effect on the male and female registration rate as well as men's life expectancy. The effect of health expenditures is also positive and significant in most models (life expectancy of men and women as well as women's registration rate). Therefore, governments should pay attention to encouraging exports, reforming the tariff system based on encouraging the import of capital goods, currency stability, facilitating banking transactions, facilitating the conditions for opening letters of credit.

It has been nearly 86 years since the Egyptian bronze ram-headed statuette, known as Harsaphes, was found outside the Treasury in the Garrison Street of Persepolis during the archaeological excavations by the Oriental Institute of the University of Chicago. The iconographic features of this little-known statuette is associated it with an Egyptian deity, which makes its genre unique in the heartland of the Persian Empire. It should be noted that the four ram-headed deities: Heryshef, Khnum, Amun, and Banebdjedet, sometimes bear a striking resemblance in the formal Egyptian art. This paper re-opens the discussion on the statuette’s identity, and offers a detailed comparison between this statuette and other Egyptian ram-headed deities. The results from a comparative analysis are critical to the supposed attribution of Harsaphes to the statuette, and examine the likelihood of being all four ram-headed gods.

The artistic background of the Good Shepherd motif can be traced to ancient eras of the East and the West. This motif later gained prominence in early Christian art. The ram-bearer (carrying a sacrificial or an offering animal) first appeared in Near Eastern art and later became an important motif in Greco-Roman art to represent certain roles of the god Hermes. This motif was considered by Christian converts as a symbol of Jesus Christ, as they represented him in the figure of a young shepherd caring for his flock. This theme can also be seen on some Sasanian seals, which can be considered as the Good Shepherd motif. In this article, the authors introduce the aforementioned motif depicted on these seals and examine their possible connection with the Christian communities. The motif of the Good Shepherd, as seen on the seals, demonstrates modifications in the original iconography. These changes can be observed in the longer dress of the ram-bearer or the engraving of celestial symbols. These modifications are probably a sign of the localization process of an imported motif in the Sasanian Empire.

This paper deals with an old Judeo-Persian letter called Dandān-Uiliq II (Abbreviated as DU II); which probably dates back to 9th century CE and further to its contents, has some important linguistic features, often without existence in later Persian texts written in Arabic script, for instance, the use of the adverb bē = but (as in Qur’an-e Qods translation), use of the conjunction kū, the use of the verb h- (to be) and ancient subjunctive mood. This paper also presents a new translation of the letter and its ancient linguistic features discussed in comparison to Early Persian texts (in Perso-Arabic script) and some early Persian translations of Qur’an, especially Qur’an-e Quds (probably written in Sistan in 12th CE). These features represent language transmission from Middle Persian to Neo-Persian (Dari). The given translation is not the same as previous English and Chinese versions and reflects my understanding of its syntax and morphology.

The objective of this study is to present a framework to increase the return and profitability and reduce credit risk of Mellat Bank customers by developing the RFM model. In this study, which was conducted as a case study in Mellat Bank of Iran, first the variables of RFM model were identified. In the next step, relevant weights of RFM variables were calculated using AHP technique. In the next step, using the K-means algorithm, customers were clustered based on weighted RFM and extended RFM. The result included customer clusters. The results indicated that the three clusters 5, 1, and 7 obtained the highest scores for receiving facilities and the coefficients for receiving facilities were equal to 0.271, 0.173, and 0.556, respectively. By determining the facility coefficient for the cluster and consequently for the customers presented in these top groups, granting facility becomes more transparent and more purposeful, and therefore, it will help the company increase profitability, reduce the churn among high-efficiency customers, and create value for customers. This research demonstrates a systematic method for granting facilities to recognize the true value based on the capability and prevention of arbitrary acts

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.

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.

Managing a single portfolio is a basic assumption in the most of research. However, in reality, an advisor manages many accounts at the same time; therefore, there is a significant dependency among portfolios and correlation between decisions on one portfolio with the performance of others, so the results of multi portfolio is different with classic models (single portfolio management, that portfolios are optimized independently) due to market impact and the trade dependency of one account to the other accounts. We propose a structural model to optimize accounts simultaneously, considering interdependences, decision’s correlation and mutual behavioral effects of managed portfolios. Moreover, to compare and analyze both single portfolio and multi portfolio approaches, real data from Tehran Stock Exchange in 1398 are used and model is solved with GAMS. Results indicate that multi portfolio optimization excel other approach and consequence notable improvement on the perspective of customer and advisor. Also, for the validation of the proposed model, the selected stocks are considered in pairs to solve the model and the results show the proper performance of the model with different stocks, thus indicating the validity of the model.

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.

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.