جستجوی مقالات مرتبط با کلیدواژه « هوش مصنوعی » در نشریات گروه « اقتصاد »

Introduction :

Artificial Intelligence (AI) permeates all aspects of human life, paving the way for numerous innovations and undoubtedly impacting individuals’ quality of life. Recently, people have increasingly been influenced by AI in their roles as citizens, as governments worldwide are increasingly relying on AI-based technologies. This technology, known by some as the Fourth Industrial Revolution (IR 4.0), will change how tasks are performed and our interactions with others and alter what we know about ourselves. With the continuous advancement of this technology, various facilities and services are being offered in cities, significantly impacting the quality of life for citizens. This technology can contribute to creating smart cities, improving urban security, optimizing public transportation, managing waste, enhancing healthcare services, reducing traffic and air pollution, improving urban communications, and many other aspects, ultimately aiding citizens in leading easier and better lives in their cities. Mashhad, as the second most extensive and populous city in Iran, is increasingly taking steps toward the use of AI technology. This neccessiatefurther studies and necessary infrastructureconcerning this subject to better utilize the technology. In this regard, the present research aims to move forward by identifying the key influential factors and drivers for improving the quality of life using AI in the future of the Mashhad metropolis. Therefore, the research question is: What are the key influential factors driving the role of AI in improving the quality of life for citizens in the Mashhad metropolis in the future? (by 2033).

The upcoming research method is a mixed method. In terms of purpose, it is an applicabile study. Also, in terms of methodology, this study encompasses descriptive-analytical techniques and a future-oriented framework. In this regard, the most influential factors affecting the role of artificial intelligence in improving the quality of life for citizens in the city of Mashhad have been evaluated. Data collection for the research was conducted through documentary methods (library resources and scientific journals) and fieldwork (utilizing questionnaire tools). The research population in this study includes 25 experts, specialists, and urban opinion leaders, including city managers (mayors of districts, deputies, officials of organizations, and urban area managers), members of the City Islamic Council, urban planning experts, and the officials of the Khorasan Razavi Road and Urban Development Department, particularly professors from Ferdowsi University of Mashhad, Islamic Azad University of Mashhad branch, non-profit higher education institutions located in Mashhad, and other urban domain specialists. To identify the key factors, structured interviews with some of the urban planning and information technology experts were conducted using the Delphi method. Subsequently, after consolidating the factors obtained from the interviews and reviewed documents in the research background, the validity and reliability of the variables were confirmed by experts, and ultimately, the factors were evaluated in the form of a future-oriented questionnaire.

By utilizing field and library studies, the most influential factors affecting the role of artificial intelligence in citizens’ quality of life were identified. Subsequently, using a quantitative future-oriented approach and the Micmac software, the significance and impact of the identified factors were measured. The research findings indicate that among the 24 influential factors in improving citizens’ quality of life with the use of artificial intelligence, after extracting the influential key factors (direct and indirect), six key drivers: governmental policy making in the field of information technology and artificial intelligence, coordination and collaboration between governmental, non-governmental, and urban management sectors towards the optimal utilization of AI technology, management of smart energy networks’ development (for example, electricity) in cities through the use of artificial intelligence, organizing the state of international communications and limitations as a prerequisite for artificial intelligence, cost-effectiveness and affordability of utilizing artificial intelligence for citizens, and the role of artificial intelligence in promoting good urban governance have had the most significant impact on improving citizens’ quality of life with the use of artificial intelligence in the city of Mashhad.

In summary, it can be stated that governmental policymaking and urban management can promote technological development by supporting and providing financial incentives to technology companies. Implementing appropriate policies can help in improving urban services, optimizing resources, reducing costs, and enhancing efficiency, ultimately contributing to the realization of smart cities. Transparency can further bolster citizens’ trust in the government and urban administrators. This sector, while safeguarding citizens’ rights, can also be effective in achieving sustainable development goals. Additionally, coordination and collaboration among governmental, non-governmental, and urban management sectors in utilizing artificial intelligence can have significant positive effects on the efficacy and efficiency of this technology in cities, enabling data sharing, innovative solutions, and more. Organizing the state of international communications can facilitate the exchange of experiences and knowledge, standards and laws development, data and resource exchange, and international collaboration. The cost-effectiveness and affordability of artificial intelligence facilities in cities and organizations can lead to increased technology access, cost reduction, improved efficiency, and accelerated innovation processes, thereby fostering a competitive technological environment. This can contribute to the development of advanced technologies, increase competitive capabilities, and attract investment. Artificial intelligence can play a crucial role in good urban governance, influencing smart decision-making in a large city, and facilitating automatic and precise activities such as traffic management, waste management, pollution control, and urban security monitoring. Moreover, in terms of citizen communications, it can enable citizens to easily communicate with urban organizations, express complaints, suggestions, and improve urban services. In the realm of governance, artificial intelligence’s role in monitoring and preventing crises, prediction, and appropriate planning for dealing with them can be effective.

Government budgeting is of great significance in assisting countries to achieve the economic and social objectives. With the expansion of modern technologies, especially artificial intelligence, new opportunities have emerged to improve financial and budgetary processes. With its advanced capabilities in data analysis and needs forecasting, AI can help increase accuracy and efficiency in resource allocation. These technologies not only transform existing processes but can also make a significant improvement in transparency and accountability in government financial affairs.

The objective of this study is twofold: first, to evaluate the efficiency of using artificial intelligence technologies such as neural networks and genetic algorithms in optimizing the allocation of government budgeting resources. Second, to determine the impact of these technologies on the transparency and accountability of budgeting processes to enhance public participation and trust.

The study employs a mixed-method approach. Data analysis is conducted through scenario modeling and and the Multipol software. Additionally, interviews and questionnaires with experts in related fields have been organized to provide our audience with a better understanding of the existing challenges and opportunities.

Findings indicate that the use of artificial intelligence can significantly increase accuracy, transparency, and efficiency in government budgeting. Artificial intelligence enables policymakers to adjust resource allocation based on more accurate data and optimized forecasts.

These days, with the emergence of technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR) and data visualization (DV), we are witnessing unprecedented developments in the field of sports. These technologies help to improve sports performance analysis, collect data automatically, and create effective training environments to improve decision-making processes. Traditional methods of sports performance analysis were usually based on manual data collection, subjective observations, and the use of limited statistical models, but with recent advances in technology, this practice has become objective and up-to-date. For example, AI has revolutionized sports analytics by simplifying data collection, processing large datasets, and automating sports analytics. Virtual reality enables training in highly realistic environments and allows athletes to practice their skills in controlled settings, and augmented reality provides digital information in the real sports environment and facilitates the tactical planning process. Data visualization techniques visualize complex data and improve understanding of performance metrics. This article examines the potential of these emerging technologies to transform sports performance analysis, providing valuable resources to coaches and athletes. The purpose of this article is to increase athletes' performance, optimize training strategies and improve decision-making processes. It also identifies existing challenges and provides solutions for integrating these technologies into current sports analysis methods. Based on the findings, it can be concluded that faster data is turned into actionable insights, training and competition events can be adjusted more quickly to align with specific goals. In this context, different environments, including virtual worlds and mixed reality, can be used to provide educational strategies Also, quality information can lead to valuable insights, and valuable insights can help achieve goals. These goals can primarily be addressed through training and performance improvement, which requires a continuous cycle of measurement and data processing.

Today, all aspects of human life are influenced by various forms of energy, especially oil and gas. So, competition among countries in the field of energy has reached its highest level. One of the significant and influential countries in this field is Russia. Examining the details of Russia's energy economy and the extent of their oil and gas trade can be useful for increasing Iran’s share in the energy market. Given the recent war between Russia and Ukraine, this matter has become even more crucial. Russia supplied a significant portion of the energy to the European Union, especially in the form of gas. Consequently, the war has brought about changes in this trade. This research explores various aspects of Russia's energy and presents the reasons for the importance of this country. Then, it thoroughly examines the different aspects of the Russia-Ukraine war, the ongoing results, and potential future effects.This research utilizes an artificial intelligence model, Multilayer Perceptron (MLP) to predict the future of Iran's oil and gas exports, aiming to enhance the role of Iran in the world energy market. The applied data, has been collected on a season basis from 1990 to 2022. The findings indicate that the MLP model has accurately predicted the future of Iran's exports. Furthermore, the results of MLP prediction presents that the amount of Iran’s oil export will increase in the near future, but the amount of Gas export will decrease in the next three years.

By using artificial intelligence methods, tax evasion of legal entities which is a continuous concern of tax system, has been detected based on financial data recorded in financial transactions of collections. The phenomenon of tax evasion can be considered in the sense that it reduces the ratio of tax revenues to GDP while reducing government revenues and increasing the level of the tax gap. Since data mining technology has many predictive and classification capabilities, it can facilitate the decision-making process in financial matters. The different methods for transaction data modeling are based on matching labeled data and the reviews of legal taxpayers' files by tax experts, which are marked as tax/non-tax evasion cases. There are various methods for modeling on this method. With the increasing use of artificial intelligence modeling, this article, by using three modern methods of artificial intelligence, on the aforementioned data set, validates the use of these methods by using the parameters of accuracy, sensitivity, and precision, which determine the quality of the produced model from various aspects. It validates, the feasibility of using these models has also been done on local financial transaction data. Therefore, in this research, it has been tried to use artificial intelligence algorithms such as artificial neural network algorithms, support vector machine and artificial bee colony algorithm to detect tax evasion. The statistical population of the current research includes 3600 taxpayers in the studied years (2002-2012) of Tehran province. One of the new methods of financial data modeling is the use of hybrid methods, in which the combination of several artificial intelligence methods is used to increase the quality of the model. It is obvious that the combination of the mentioned methods has favorable results in some situations and may not be so in other situations. Therefore, in this article, this issue has been investigated in detail and the use of different methods and a combination of artificial intelligence methods has been discussed. Therefore, by using the combined algorithms in this research, we were able to with an accuracy of 85.59%; For training data and accuracy of 83.79% for test data, let's identify and classify taxpayers who are late in paying taxes and taxpayers who pay taxes on time. Therefore, according to the obtained results, we find that the proposed method in this research has a high ability to identify taxpayers who cheat on paying taxes. The use of artificial intelligence systems can be used as an economic stimulus in order to reduce tax evasion. In this regard, it is suggested to first form an integrated system as an economic data center, and this center will take on the responsibility of keeping the country's financial data safe. Then, a standard for the use of artificial intelligence and data mining of financial data should be developed. With this method of scientific and academic research, without providing sensitive data to outside the required organizations, it is possible to take a step forward from the scientific ability of experts regarding factors such as tax evasion detection, terrorism financing detection and money laundering in order to improve the country's economic system.

Charitable activities in different ways and based on different intentions have always been emphasized and paid attention to in heavenly religions and human civilizations with different intensity and weaknesses (Shivji, 2007: 62).
In the teachings of Islam, charitable activities such as waqf, endowment, benevolence, and charity are highly emphasized. One of the important cases in which voluntary activities could play a serious role as an auxiliary arm of governance was the Corona crisis (Kifle Mekonen & Adarkwah, 2022: 2). In general, the experiences gained from charitable activities during the Corona era played a significant role in managing the challenges and economic problems of that era, and by supporting medical and therapeutic measures, it also played a facilitating role in reducing the epidemic cycle. The continuation of difficult economic conditions and the creation of new crises in the country indicate the necessity of continuing and expanding charitable activities. Based on this, the current research intends to explain the future trend of these activities based on the experience and lessons learned from charitable activities during the Corona epidemic.

The upcoming research has an exploratory qualitative approach in terms of its nature and a developmental approach in terms of its goal. To investigate the experiences of donors during the Corona era and also predict the future trend of this range of charitable activities, a thematic analysis method was determined for this research. Also, the semi-structured interview tool was determined as the main strategy of data collection to achieve the goals of this research. The statistical population of the current research was formed by donors and experts of the Imam Khomeini (RA) Relief Committee across the country, and 31 people were selected from this population using the purposeful sampling method. After conducting the interviews, the data was coded with a line-by-line review approach and with the help of Maxqda.20 software. Also, the validity and reliability of this research was confirmed through Creswell (2000) guidelines (Creswell & Miller, 2000). The research method was forward-looking.

The present research was conducted to promote the charitable activities of the country by using the experiences during the Corona epidemic. In this regard, the opinions of 31 experts, managers, and activists in the field of charitable activities were analyzed through semi-structured interviews and using the thematic analysis method. Based on the data analysis, the two main networks of this research were obtained as follows:Fig 1. The main themes of the present research Based on the figure above, the lessons learned from Corona were identified in five axes: economic, health support, jihadi battle, spiritual support, and rule of law. Also, taking into account the social and economic problems of the country, the role of charitable activities in the future was identified in the five axes of strengthening the livelihood of the needy, educational jihad, improving the level of health, generating culture, and governance of artificial intelligence.

The experiences of charitable activities during the outbreak of Corona in the country showed that the capacities of this field can be used in other sensitive parts of the country in addition to health issues. The use of modern tools such as artificial intelligence to identify people in need and predict the future situation of this field as well as creating communication networks between benefactors to synergize the capacities of these people will be one of the most important features of such affairs. In the meantime, the innovative and new part that the specialists of this research mentioned was the use of new technologies such as artificial intelligence and big data analysis by donors. In the field of charitable activities, artificial intelligence has the potential to help improve the performance and effectiveness of these activities. One of the important advantages of artificial intelligence in the field of charitable activities mentioned in the research of researchers is the collection and analysis of data and the provision of intelligent solutions to identify the needs of people covered by the relief committee.

The unemployment rate and Gross Domestic Product (GDP) are among the most important economic indicators that understanding their current and future trends can help policymakers and decision-makers adopt appropriate solutions to prevent crises and improve the country’s economic situation. Accurate prediction of these two indicators can be useful in future planning and improving the country’s economy and people’s livelihoods. In recent years, artificial intelligence techniques and tools, given their many capabilities, can play a very important role in predicting important economic indicators. Therefore, given the high importance of the two indicators of unemployment rate and GDP on the economy of our country Iran, this article intends to first predict these two indicators separately and then predict the rate of GDP growth based on the unemployment rate using artificial intelligence techniques. For this purpose, in this research, seasonal data related to GDP and its components and the unemployment rate for the years 1976-2022 have been used. Also, machine learning models based on regression have been used for prediction. The results show that the predictions of the mentioned models have an appropriate accuracy in terms of evaluation criteria such as root mean square error, mean absolute error, mean absolute percentage error, which indicates.

Artificial intelligence (AI) is one of the emerging technologies that has attracted a lot of attention in recent years. The tremendous economic effects of this technology and, on the other hand, the potential risks and challenges have caused policymakers in different countries to pay special attention to appropriate policies for developing AI innovation ecosystems. This research identified the basic functions of AI in the world by examining the innovation ecosystem development policies in 6 countries, the United States, China, England, Russia, India, and the UAE, and interviewing AI experts in the Iran. In addition, the present research has identified the actors of this ecosystem and the roles and mutual relationships between them by presenting the structural and functional mapping of the AI innovation ecosystem and offers some policies to improve the development of the AI ecosystem in Iran. The seven basic AI functions identified in this research are policy and governance, education, financing, research, networking, innovative and startup activities, and technical infrastructure development. Also, the results of this research show that Iran's AI ecosystem has 41 governmental, quasi-governmental and private major actors. Moreover, the research results indicate that to develop the country's AI innovation ecosystem and prevent lagging behind other countries, high-level coordination of government, industry and university actors is needed.

Growing popularity of smart devices and 5G Internet, loT technology has also developed. The increase in the number of smart objects has led to an increase in data volumes and computational loads on a large scale. For this reason, cloud computing is used as a solution for this amount of data. However, given the importance of service quality, the cloud computing solution may not be responsive to latency-sensitive requests. Allocating resources in cloudy computing also reduces the cost of latency. Dynamic programming has been used due to the large number of requests and problem constraints. The proposed method reduces the cost of latency for loT requests. In this study, the proposed system modeling and algorithms were implemented for four cases. In these cases, two proposed methods have been the contribution of this research that these proposed algorithms have seen a significant reduction in the total latency cost. But as expected, the backward algorithm had a better response than the forward algorithm.

Given the high exchange rate fluctuations in Iran, its prediction is one of the major issues and challenges for different groups in the country. This study investigates the performance of six different static and dynamic neural networks for forecasting exchange rates using fundamental, technical and hybrid approaches and using seasonal data over the period (1) 2004- (4) 1396 for variables influencing exchange rate including inflation, Liquidity and GDP for the two countries, Iran and the United States. The findings show that the number of neurons did not have a regular effect on the performance of the networks and that the best results occurred at breaks of three and four. The results also show that the best performance of the static neural network is achieved by a technical approach with a structure of sixteen neurons and four interruptions which provides a relatively accurate exchange rate prediction despite the low number of input data.

I International relations are full of complexities due to their multifaceted and multilateral nature. To understand decision making processes and the payoffs of their strategies, players are enabled to utilize their capabilities to impact the strategic decision payoffs. As an example of this decision structure we can refer to the international disputes and conflicts including the sanctions. The Islamic Republic of Iran’s interests in international arena have been influenced by the changing interests of the other stakeholders in the coalition of the United States sanction against Iran. In order to analyze and predict the equilibriums of the players positions in the complex international space of the sanctions, game theory, mechanism design, multi-agent systems, and artificial intelligent as new instruments of decision theory are utilized to resolve the transactions and processes influenced by the human decisions. In this research, a policy spectrum is defined according to the strategies of Iran, the United States of America and other stakeholder’s countries. Modeling and simulating the behavior of players on this continuous spectrum which includes two extremes of capitulation and confrontation shows that in the current situation, Iran’s equilibrium and dominated strategy is emphasizing the maintenance of the current agreement in JCPOA, although this position is in the lower range of the spectrum of the current agreement. In these sanctions there is a possibility of a tragedy of the commons. In order to prevent its occurrence, the results of this study imply that the dominant strategy, given that all the players are rational, is to preserve stability, security, and integrity of Iran as a regional power. In order to achieve a stable equilibrium of the game, the stakeholders try to maximize the social welfare function instead of individual participants’ payoff. The rational strategy of Iran is to stay in JCPOA, strengthening political ties with the European players, and to empower its own military and social securities.

The deregulation in power market is lead to competition among market participant to increase efficiency. In electricity market generation is the best candidate for iterance in competition to improve productivity and efficiency in resource allocation and offer lowest price by highest quality will be yielded. In the pool-based electricity market, every Genco submits a bidding price in ten step offer to the Independent System Operator (ISO) for every hour of the next day. One of the most important parameters affecting the profitability of Genco’s and their bidding pattern is the cost of energy generation. Therefore, in this paper, taking into account the costs of fuel consumption, depreciation, maintenance, operation and utilization and other costs as factors affecting the production cost. In this article artificial intelligence algorithms applied to calculate the energy generation cost function at different levels of production (base load, partial load, and full load), broken down by fuel type. According to the results, the average cost per kilowatt-hour of energy with gas fuel is 398 Rials and diesel fuel is 500 Rials

With the electronic taxpayers system being operationalized and the digital storage of tax data developed in Iran، it is now possible to design different models to analyze the available data. There are two main areas that have not been the focus of the fairly limited current studies in this field; one being the parallel optimization of parametric AI models and the other area is the selection of input variable combination. For this reason، in present study، we have used the harmony search (HS) optimization algorithm to do parallel optimization of multilayer perceptron (MLP) neural network parameters and also to find a suitable combination of input variables. In addition to that، the results have been compared with logistic regression results as the core of the system. In the present research، 21 initial input variables are selected for the system based on the survey done on similar studies in the last thirty years and it takes into account the specifications of the tax system in Iran and the opinions of the experts in the field are asked. After running the system on the data from the food and textile sectors and comparing the results from the neural network and logistic regression، we have concluded that neural network can produce more accurate results and the difference is statistically meaningful.