Data mining
Ali Esmaili; Hoshang Taghizadeh; Naser Faqhi Farhamand
Abstract
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 ...
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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.
Data mining
Taimour Jafarian Dehkordi; Mohammadreza Dalvi Esfahan; Saeed Aghasi
Abstract
Purpose: The current research was conducted by designing the knowledge-based organizational satisfaction modeling with a data-driven approach using a qualitative and quantitative method of grounded theory and data mining techniques.methods: The data was taken from in-depth and semi-structured interviews ...
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Purpose: The current research was conducted by designing the knowledge-based organizational satisfaction modeling with a data-driven approach using a qualitative and quantitative method of grounded theory and data mining techniques.methods: The data was taken from in-depth and semi-structured interviews with 25 general managers of social security insurance departments in the provinces of the country, based on purposeful sampling. The validity of the research data was checked and confirmed by going back to the participants and external auditors. In the data mining section, registered data and the organization's database were used. Using the data recorded in the Clementine software, the happiness and unhappiness of the employees in the organization were categorized.Findings: The results showed that the model of organizational happiness in the social security organization was identified at three levels, group, individual and organization, including causal factors, intervenors, platforms, strategies and finally consequences. Also, the status of employees was determined based on the proposed model of happiness according to the collected data. Finally, the data mining model showed classification with 66% accuracy for happy and unhappy employees.Conclusion: The human resource management approach based on organizational data leads to correct decision making in organizational performance. The more transparent the collected data is, the more accurately the state of the organization can be predicted. Also, based on the proposed model and implementation in the form of data mining, it is possible to estimate the number of happy employees.
Data mining
Hojat Mahammadi Torkamani; Mohammad Pasban; Yaghoub Alavi Matin; Hakimeh Niki Esfahlan
Abstract
This research aims to design an intelligent model of digital consumer behavior knowledge based on big data. This research was conducted using a qualitative approach. First, the qualitative method of thematic analysis was used, followed by the application of big data analysis techniques. The statistical ...
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This research aims to design an intelligent model of digital consumer behavior knowledge based on big data. This research was conducted using a qualitative approach. First, the qualitative method of thematic analysis was used, followed by the application of big data analysis techniques. The statistical population includes experts in the field of marketing who specialize in qualitative analysis. The sample size was determined to be 10 people using the snowball method and theoretical saturation. The data collection tool includes interviews with experts, which were analyzed using the thematic analysis technique in MAXQDA. In the following, the customer's behavioral trend has been studied based on the Big Data technique model, using the data available in the Digikala company. Coding in MATLAB is done based on specific formulas. The results showed seven components and 48 indicators that were identified and approved by experts in designing consumer behavior patterns using a digital marketing approach. These components include 1. Marketing Practices. 2- Innovation, 3- Digital marketing strategy, 4- Dynamic digital marketing, 5- Customer management, 6- Consumer cooperative behavior, and 7- Consequences of consumer response. The business management has finally decided to expand the intelligent system for consumer behavior. The main evaluation index is relatively unique and cannot effectively stimulate the acquisition of new customers. The only evaluation comes from consumers who have a recorded history of financial behavior on the digital platform. The value network model relies on digital technology because it facilitates interaction between end consumers as a relational medium.
Data mining
Nasim Bakhshaei; Mohammad Reza Bagherzadeh; Yusuf Gholipourkanani; Mohammad Reza Dalvi
Abstract
This research aims to develop a data-based model for fifth-generation universities. Creating a data-driven model in a university environment is essential in education. The primary mission of higher education is to address the specific educational needs of individuals, as well as the needs of society ...
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This research aims to develop a data-based model for fifth-generation universities. Creating a data-driven model in a university environment is essential in education. The primary mission of higher education is to address the specific educational needs of individuals, as well as the needs of society and its economic development. The study was conducted in both qualitative and quantitative sections. The grounded theory is conducted based on the perspectives of the chancellors of Islamic Azad University. 21 people were selected using snowball sampling techniques. In the following, a six-category model is provided. Analysis was done using NVIVO software. The statistical population in the quantitative section consisted of all professors from Islamic Azad University nationwide. A sample size of 381 professors was selected using the Cochran sampling formula. The research tool was a questionnaire created by the researcher. Then, using the model presented and the suggested pattern fit, the performance of the model is predicted based on the K-Mean method in Weka and RapidMiner software. According to the results, the proposed model was approved by experts. The analysis of structural equations was also confirmed. According to the Waode algorithm model, the highest accuracy was 81%.
Data mining
Aliakbar Vakili; Mahdi Bagheri; Sirajuddin Mohebi; Kobra Haji Alizadeh
Abstract
This research aims to identify the knowledge management infrastructure due to reducing employee absenteeism based on data mining. Examining the status and reports of employees using data recording systems, creating information dashboards, and applying data mining techniques is important for the transparency ...
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This research aims to identify the knowledge management infrastructure due to reducing employee absenteeism based on data mining. Examining the status and reports of employees using data recording systems, creating information dashboards, and applying data mining techniques is important for the transparency of the mental state of employees. The mixed research method (qualitative-quantitative) has been done in two phases. The first phase was conducted with a qualitative-inductive approach using the Delphi method and a semi-structured interview tool. In the second step, codes were grouped in a common axis and 13 axis codes based on the similarity and distinction between the extracted codes. The interview sample was 10 people selected using the purposeful sampling method. In the second phase, the quantitative research method was data mining; Then, according to the research literature and experts' opinion, the researcher-made questionnaire was designed with a five-point Likert scale. The data mining technique is based on neural networks and decision trees in Rosseta and Weka software. The results showed that knowledge management can increase the quality of organizational processes based on data, increase the empowerment of employees, and reduce absenteeism. The knowledge obtained from the data mining of organizational information dashboards is important for strengthening the mental health systems of employees and increasing productivity.
Data mining
Tina Malekolkalami; Khadijah Khodabakhshi Parijani; Maliheh Alifari
Abstract
The purpose of this research is to use data mining to detect accounting fraud in the database of stock exchange member companies. The combination of discrete and continuous data has increased the necessity of using data mining and machine learning methods in the field of fraud detection. This research ...
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The purpose of this research is to use data mining to detect accounting fraud in the database of stock exchange member companies. The combination of discrete and continuous data has increased the necessity of using data mining and machine learning methods in the field of fraud detection. This research is applied in terms of purpose and descriptive in terms of method. The document review method was used to collect information in the field of literature and research background. The prepared questionnaire includes 7 main indicators consisting of 48 questions for each of the variables. This questionnaire was made available by the researcher to 400 accountants of companies admitted to the Tehran Stock Exchange by sampling method. In order to fit the model, the structural equation method was used in SMARTPLS software. In the data mining section, all IB1, IBK, LWL, KSTAR, and KNN algorithms were used to simulate the proposed model in Rapidminer software. Effectiveness of internal control, compensation system, asymmetry of information, compliance with accounting rules, management ethics, and ethical principles are effective and meaningful on accounting fraud. In evaluating parameters and according to the graphs, the K-STAR algorithm has better performance than other algorithms. The proposed data mining model for financial fraud detection showed that since the amount of data creation in financial companies is increasing day by day with the development of technology, it is possible to provide early detection of fraud by reviewing and analyzing the data.
Data mining
Ali Shahgharar; Majid Bagherzadeh Khajeh; Sahar khoshfetrat; Naser Faqhi Farhamand
Abstract
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 ...
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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.
Data mining
Seyed Rohollah Abbasi; Abdul Khalegh Gholami Chenaristan Alia; Foad Makvandi
Abstract
This study aimed to design a data-driven decision-making model for managers in the direction of empowering human resources in the police headquarter of Kohgiluyeh and Boyer-Ahmad province. Increasing amount of information and rapid changes in the environment and the need to create continuous communication ...
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This study aimed to design a data-driven decision-making model for managers in the direction of empowering human resources in the police headquarter of Kohgiluyeh and Boyer-Ahmad province. Increasing amount of information and rapid changes in the environment and the need to create continuous communication with the complex and dynamic environment requires management, acquisition and distribution of knowledge as well as, proper organizing and analysis of information. The present research uses a qualitative approach. The statistical population included experts in the police force, 19 people were selected through purposive sampling and interviewed. The identified indicators of the data-driven decision-making model of managers in empowering employees were extracted in the form of 3 main categories, 17 sub-categories, and 69 concepts. The identified model was also tested based on the AdaBoost regression algorithm in Rapidminer software which led to development of the intelligence of the managers' decision-making model compared to the traditional model. The findings showed structural factors (including strategic orientations, organizational structure dynamics, performance management system, training and improvement, knowledge management system, job design system, and information technology system) behavioral factors (including management orientations, leadership style, development of psychological characteristics of employees), development of decision-making skills and competences of employees, human relations system, job attitudes, and organizational culture) and environmental factors (including legal factors, political factors, and economic factors). Based on the proposed model, the accuracy of data-driven decision-making of managers was tested and the results indicated the significance of intelligence and information in the organization.
