فهرست مطالب naser faqhi farhamand

This research aims to study the data-driven model of gas consumption management, with a focus on addressing unauthorized use through the analysis of information systems. Research was conducted using a metasynthesis approach and technique in the field of gas consumption management and mathematical programming with genetic algorithms. ATLAS.ti software was used for analysis. The influencing factors related to a specific period of time were examined and searched for in this research. Internal and external sources from the years 2006 to 2023 were analyzed. 27 studies were selected based on the Critical Appraisal Skills Programme (CASP) technique. In the continuation of mathematical modeling using MATLAB software, the simulation was conducted to compare the performance of three proposed algorithms. Based on the results obtained from the meta-combination technique, the main categories include the use of renewable energy, gas consumption management, shortcomings, obstacles, data-driven solutions, consequences of gas consumption management, and economic growth. All three models also demonstrated the basis for optimal gas consumption and the reduction of unauthorized consumption. The utilization of data analysis can enhance system efficiency, pinpoint weaknesses and losses, boost productivity, and optimize the utilization of gas energy. Based on the analysis, it was shown that data mining can be very useful in managing gas energy consumption and identifying unauthorized breaches. Overall, simulating gas energy consumption management using a genetic algorithm can provide efficient and effective solutions, handle complex and dynamic scenarios, and offer insights into optimizing gas consumption and energy efficiency.

Investment attraction is considered one of the most important economic variables in most countries of the world, so that most of the countries' economic goals can be achieved with initiatives in this field. The aim of the current research is to provide a model of value creation with the approach of attracting and developing investment in free zones according to the conditions of Iran. In the present study, experts were interviewed in free zones using the researcher-made in-depth interview tool. Interviews were analyzed with Max Kyuda qualitative data analysis software and three-stage open, central and selective coding. The research findings include causal conditions (national factors, globalization of free zones, infrastructural and technical, depth of knowledge, culture building, quality of statistics and information), intervening conditions (political and economic factors, instability of management in free zones, competitiveness and legal system) and finally background conditions (environmental conditions in terms of territorial development of free zones, potentials of free zones, scientific potentials, geopolitical situation, scientific potentials, economic, political and international situation). The analyzes show that "achieving value creation, and value creation with the approach of investment attraction and development" is the central category and the most important value creation strategy in free zones, support for the organization of free zones, revision of laws and free zones, improvement of foreign relations, the development of the knowledge-based economy and the strengthening of competitiveness. Finally, in order to validate the designed model, the content value of each index in measuring the proposed concepts was analyzed by structural equation modeling.

The current research aims to study the effectiveness of artificial intelligence (AI) on sustainable and intelligent supply chain management in the food industry of East Azarbaijan province. The use of intelligent technologies and sustainability components based on organizational knowledge in the product supply chain not only improves the information level of the supply chain but also reduces the risk of product security problems, especially perishable products, by controlling the supply chain. Also, when a product security problem occurs, companies can help solve this problem through intelligentization and knowledge management. In this research, by comparing the regression rate, which is closer to the desired number of one, and the MSE rate of the obtained error value, which is very close to zero, in the best case, the results related to one hidden layer and two neurons were selected. Then, by calculating the sum of the weights of each layer and normalizing the weights, the importance of each input layer was determined. The research results showed that the order of importance of the independent variables in the neural network structure model is cultural factors, economic factors, environmental factors, and social factors. measures to be taken by businesses to realize digital transformation.

The purpose of this research is to design a service innovation model and investigate the factors affecting service innovation in Mellat Bank of East Azerbaijan province. The current research is developmental-applicative in terms of its purpose and descriptive-analytical in terms of the research method. The statistical population of this research is 14 experts (university professors) who are familiar with the subject in order to formulate the service innovation model. A questionnaire was used to collect data. Content validity was used to determine the validity of the questionnaire, and Cronbach's alpha coefficient and composite reliability were used to determine reliability. In order to analyze the data, the fuzzy Dimetal method was used. The research findings show that in order to design the service innovation model, seven main components of idea generation, organization and management, organizational structure, new service development process, resources, market synergy and strategy can be used. The findings of Dimtel's method show that the organization and management component is the only influential component among service innovation components and other components are considered as influential components