درخت حوزه‌های تخصصی

Many studies have been conducted in the field of bankruptcy prediction; But in most of them only financial ratios are used. However, in Iran, many non-financial factors affect bankruptcy. The main purpose of this study is to develop a mathematical model in which financial and non-financial indicators such as management and economics factors are used to predict bankruptcy. In this study, 44 variables that had the greatest impact on bankruptcy forecast were selected and with confirmatory factor analysis, a questionnaire was developed and sent to experts in the fields of management, accounting and economics to rank the impact of these variables. The statistical sample of the study includes 200 bankrupt and non-bankrupt companies listed in the period 2009-2018. After collecting the questionnaires using the OLS regression estimation method, the variables that had a factor load of less than 0.5 were eliminated and in the final model 9 main variables. The research model identified 95% of bankrupt companies and 93% of non-bankrupt companies with 95.4% confidence. Then, for verification, two hypotheses were developed and the model of this research was compared with two existing models. The ability to distinguish bankrupt companies from non-bankrupt ones by our proposed model was 6% more accurate than the Pourheidari et al. model, and 9.4% more accurate than Altman’s model.

The purpose of this study is to present and explain the paradigm model of the role of behavioral characteristics and financial literacy of real investors on the components of their financial management behavior in the Iranian capital market. For this purpose, a 77-item questionnaire derived from qualitative studies using grounded theory method was used. The chi-square value of the model is equal to 3946.370, the degree of freedom of the model is equal to 2174, the result of which is equal to 1.815, and the fit indices of the original model are all in an acceptable and appropriate level. Behavioral characteristics and financial literacy can predict financial management behavior and help real investors analyze stock market trends before making a decision, which leads to investment profitability, financial security and capital satisfaction.

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.

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 portfolio selection problem is one of the main investment management prob-lems. In the portfolio selection problem, robustness is sought against uncertainty or variability in the value of the parameters of the problem. This paper has been conducted for Robust portfolio optimization based on the mean-cvar approach. And introduces the linear mean-cvar model as a criterion for calculating risk and provides an optimal Robust mean-cvar model. Robust approach used in this research is the Bertsimas and Sim. In this approach, Robust counterpart presented for a linear programming model remains linear, maintaining the advantages of the linear programming model in the optimal model. The model developed in this research is randomly selected by real data of 20 stocks of the S&P 500 index for three years, this development help portfolio selection problem to consider uncertainty. Interval optimization is modeling approach to consider parameters uncertainty in this paper. Considering uncertainty make model more realistic. The results of model show that this approach has computational efficiency and on the other hand proposed model produce better solution in risk and portfolio rate of return point of view

Nowadays, customer satisfaction is regarded as a strategic and influential factor for the success of various businesses. On the other hand, tourism, the world's largest and most diverse industry, is also a significant source of income and job creation for many countries. Therefore, the present study examines the effect of nostalgia, experiential marketing, and experiential values as relatively novel concepts on satisfaction in the tourism industry. In light of the COVID-19 outbreak, the research population includes all tourists who have previously visited the Kashan Rosewater Festival. Furthermore, since the population size is uncertain, it is assumed unlimited. A convenience sampling method was used to select sample members from the population. Methodologically, it is a descriptive survey study based on the subject matter under investigation. It is also a correlational study. A partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the questionnaire results. Based on our findings, nostalgia affects pull motivation, patronage behavior, and future travel intention. However, pull motivation does not influence travel intentions.

This research, in line with the strategy of the World Customs Organization, order to provide a structural model for improving the ethics and administrative health of employees based on the use of information technology in customs affairs with a combined approach of fundamental theory and interpretive structural modeling to understand how information technology affects ethics and Administrative health and investigating the power of influence and the degree of dependence of the variables of ethics and administrative health of employees in the form of drawing a comprehensive structuring model. In the implementation of this research, a mixed method was used in the qualitative part of the foundation's data theory according to the research needs and structured interviews were used to collect data. To collect data in a qualitative method, with the help of Maxqda software, research findings through the opinions of 13 experts in three stages of open-centered and selective coding in the form of 6 categories affecting the ethics and administrative health of employees in the form of the Strauss paradigm model and Corbin and it was expressed theoretically. After building the theory, in the quantitative stage, interpretive structural modeling has been used in order to understand the relationship between the variables and the leveling of the promotion of ethics and administrative health of Iran Customs. The results of the research show that the variable "use of smart technologies" is the first level variable in the promotion of ethics and health, and the variables "Analysis of the situation of manpower and customs processes, ethical characteristics of employees and laws and management of information technology and human resources" » is the second level variable. And the variables of "conditions and structure of the organization and the requirements and infrastructure of information technology" are the variables of the third level.

Modern organizations’ strategy is reducing inventory, production cycle time, transportation cost, quality cost, while increasing competitiveness to maximize its benefits. Supply chain management, as part of an organization’s strategy, involves several stakeholders with different interests, each controlling part of the chain and pursuing different goals. This paper presents a comprehensive multi-level model of a supply chain consisting of several simultaneous stakeholders in a systematic way. The developed model first, consider the various transportation factors, such as distribution of goods for various members of the supply chain to achieves the lowest transportation cost at different levels, for both transporting parts and products. The model also provides lowest costs of purchase and production by considering the costs of maintenance, supply shortages and shortage, for all the involved parties. In addition, a competition in the context of a game to select the optimal situation between the stakeholders of the supply chain has been considered to make the best decision in terms of a participatory game by considering different scenarios. Through applying the principles of the game theory, various scenarios were developed to optimize the benefits of parties and to achieve the optimal design. The results of a numerical example showed that the answer provided by the game theory for the value of the objective function was less than the answers of the transport problem, despite the deficit penalty. Therefore, it can be concluded that game theory can achieve a better answer by considering the conditions of alliance between participants in a collaborative game.

In recent years, industrial clusters have received considerable attention from economists and industry analysts because they are seen as the main reason for certain economic regions' economic growth and success. For many Industrial States Organization, the selection of industrial clusters has become a critical strategic consideration due to the Budget allocation priority. In this paper, an extended qualitative flexible multiple (QUALIFLEX) methods is used to solve problems regarding the priority among this cluster using probability hesitant fuzzy information, which can lead to allocating the budget for industrial clusters more effectively. For more accuracy, we have applied a Hesitant fuzzy Topsis for prioritizing. Both rankings have been aggregated by the Copeland method. From our research results, the Larestan Muscat is of great importance, and Abade Inlaid Wood, Citrus packaging, Shiraz Marquetry, and Niriz stone have ranked respectively.

Social networks advertisements can be employed as a powerful means of communication to achieve goals such as sales, profitability and reinforcing brand image. Organizations managers have established a department titled “public relations” in their organizations, through which they communicate with individuals inside and outside the organization, so that they can fulfill their goals at lower costs and within shorter time by informing and persuading those individuals. In this regards, the present study is aimed at evaluating the extent to which public relations of private organizations employ social networks to introduce their products and services. Research population consists of managers and public relation experts of private sector’s organizations in Yazd, Iran. The present study takes a survey method and is regarded as field-work research. Sampling method is based probability and availability. Due to the limited population, 76 individuals were selected as the sample through limited population formula. Data was analyzed via Smart-PLS. results reveal that public relations of private organizations employ social networks at an optimal and acceptable level to introduce their products and services. Hence the hypothesis of the study is confirmed.

Organizational DNA expresses a method of investigation, thinking, reflection and thought about organizations, which examines the patterns of organizations, management practices, leadership style and other concepts related to the organization. Organizational DNA uses very diverse approaches to understand and recognize the organization, instead of organizational models and forms, by examining issues such as teamwork, decision-making, and development and improvement of human resources as separate or at least independent variables. In this research, by using the theoretical framework and using quantitative and qualitative methods, it has been done to identify the dominant DNA in Tocafulad Holding companies and determine the relationship model of hidden components using covariance analysis. The statistical population includes 435 employees of Isfahan Tokafulad companies and 6 company experts who used a mixed or mixed design to conduct the research. The technique of structural equations has been used to improve the model and evaluate the components and indicators to check and identify the model. In order to answer the research question, the steps are: 1) presentation of the conceptual model of the relationship between the corporate DNA and the investigated components, 2) quantitative evaluation of the components and indicators of the model, 3) determining the mutual influence of the hidden variables and the obvious variables of the model and 4) the relationship Between the apparent variables of the components with each other and the verification of the initial conceptual model has been carried out and then the model has been improved and finally the dominant DNA has been determined. The research showed that semantic DNA with a factor load of 0.93 is the dominant DNA in 4 out of 6 companies under investigation. It was also found that planning and payment system are the least important and change processes are the most important in line with the improvement model. In the improved model based on covariance analysis, the mutual effects of payment system and planning with mission, leadership style with teamwork, performance management with decision-making, interpersonal relations with change processes and change processes with work group should be considered.

The purpose of this study was to present an optimal model Predicting Future Cash Flows optimized neural network with genetic (ANN+GA) and particle swarm algorithms (ANN+PSO). In this study, due to the nonlinear relationship among accounting information, we have tried to predict future cash flows by combining artificial intelligence algorithms. Variables of accruals components and operating cash flows were employed to investigate this prediction; therefore, the data of 137 companies listed in Tehran Stock Exchange during (2009-2017) were analysed. The results of this study showed that both neural network models optimized by genetic and particle swarm algorithms with all variables presented in this study (with 15 predictor variables) are able to provide an optimal model Predicting Future Cash Flows. The results of fitting models also showed that neural network optimized with particle swarm algorithm (ANN+PSO) has lower error coefficient (better efficiency and higher prediction accuracy) than neural network optimized with ge-netic algorithms (ANN+GA).

In the 21st century, the meaning of places is essential to people. Meanings have gained symbolic importance through tourism heritage. Tourism is a powerful force  in shaping the world and our understanding of different cultures, people, and  places. This research investigates the complexities of religious tourism and the  management of religious destinations. It obtains criteria for managing religious  goals with a detailed look and uses the descriptive-analytical method based on  library documents and the opinion of research experts. Then, using research experts  and ISM-PLS hybrid approach, a three-level framework for managing religious  goals in Yazd city has been presented and analyzed. The results showed that the  government's decisions and actions were the most effective criteria. The ultimate  goal was to create a sense of trust and security among religious tourists. It is hoped  that the findings of this research will be a starting point for managing religious  tourism destinations and a guide for examining the complexities of Islamic tourism  destinations and their management.

In this paper, we discuss a new type of Mann iterative algorithm for finding a common fixed point of two nonexpansive mappings in real Hilbert spaces, and we establish the weak convergence of this fixed point approach by considering new circumstances. We can also show the algorithm’s strong convergence by taking into account an extra requirement. Our results extend the previous results given by Kanzow and Shehu as well as by Cho at et. al. Also in the last section, some applications of the main results are shown, and by some examples, we show the generality of our main results.