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Huge economic, social, political, and technological changes in recent decades have brought increasing complexity and decreasing predictability of organizations' environments. One of the solutions offered to deal with such environments is the agile approach to human resources. This research was conducted with the aim of investigating the driving factors in human resource agility based on government policies. The current research is developmental in terms of purpose and descriptive survey in terms of the method with a combined (qualitative and quantitative) approach. The research community in the qualitative part included professors, experts in the field of human resources, and senior managers of the human resources department, and in the quantitative part, the senior and middle managers of the country's tax affairs organization who had a bachelor's degree or higher. Relevant data were extracted from theoretical literature and semi-structured open interviews in the qualitative section. The tool for data collection was a researcher-made questionnaire in the quantitative part. The results of the qualitative part led to the identification of 13 main categories, which are categorized into four main clusters (The propellants of human resource agility, Human resource agility policies, operational components of human resource agility, and Results and Functions of human resource agility) and became a five-point Likert scale questionnaire. The results of confirmatory factor analysis showed that all items have a significant relationship with the structure and the proposed structural model has a good fit. The results of the path analysis showed that the operational components of human resource agility have a mediating role in the relationship between the drivers of human resource agility and the results and functions of human resource agility. Human resource agility policies have a mediating role in the relationship between the propellants of human resource agility and the results and performance of human resource agility.

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.

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.

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).

The application of machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fetal death. The application of innovative machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an open-source tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a Dull-Razor preprocessing method to remove extra details such as hair. Next, histogram adjustments and lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results show that ANN could achieve better accuracy (83.5%). In order to mitigate the biases in existing studies, the source code of this research is available at hadi-naghavipour.com/ml to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.

The spread of volatility between financial indices indicates the process of information transfer between markets. Despite the relationship between financial markets, the information created in one market can affect other markets as well. Therefore, the main purpose of this study is to investigate the volatility of energy and steel prices and the experimental test of inter-market volatility spillover. To do this, the monthly data of steel and energy price (oil and gas) during 2009 to 2019 were collected from valid databank using VECM and GARCH Family and VAR model and ICSS algorithm were analyzed by considering and without considering structural failure. Then, the causal relationship between them is examined through Granger causality test. The results show that there is volatility in the energy market (oil and gas) as well as the steel market during the studied time period. Results also show that the price of steel as well as its return and index are changed significantly by energy price effect. However, there is a causal link between energy prices and steel products and these results are consistent with the theoretical basics of the study and review of literature.

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.

This study was an attempt to evaluate the progress of capital market efficiency in Iran. Optimal resource allocation and micro and macro investments play a key role in the capital market. The capital market's main task is to circulate capital and allocate resources efficiently and optimally. The main task of this market is to flow capital and allocate resources efficiently and optimally. Is there a regular pattern for determining the stock price? Market efficiency gains significance as it is important to know what factor or factors are effective in determining the price of the stock in the stock market or whether there is a regular pattern for determining the price of a stock. Thus, this study examined the efficiency of the capital market in Iran. In this regard, the researchers used the daily data of the total index of the Tehran Stock Exchange for 2008-2017. Artificial neural network and time series training tests were used to perform the test. The test results showed weak efficiency in the Tehran Stock Exchange and this inefficiency did not change significantly compared to the first period. In other words, in the Tehran Stock Market, one can predict returns using artificial intelligence.

The aim of this study was to investigate the ranking of effective factors on how to implement timely management of goods and equipment and its evaluation criteria in the power distribution company of the whole country using Analytic Hierarchy (AHP). Five dimensions (production technical factors, managerial level management factors, factors related to IT processes and infrastructure, factors related to processes and factors related to training and manpower) were determined and the research hierarchical tree was drawn. Based on the opinions of experts in this tree, five Next, on how to implement timely management of goods and equipment in time and its criteria interact with each other. To determine their weight, the combined approach of fuzzy AHP and fuzzy DEMATEL was used. The influential and expressive dimensions of each other are the cause, and the dimensions of the factors related to IT processes and infrastructures are part of the influential or expressive dimensions of other effects.

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.