Knowledge Extraction
Ali Zare Abarghouei; Mohammad Reza Dalvi; Zahra Dashtlaali
Abstract
The current research was conducted to apply knowledge extraction in the classification of jobs to identify the key role players using a mixed method (qualitative and sufficient data). The application of expert systems or decision support systems based on organizational data is increasing in the selection ...
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The current research was conducted to apply knowledge extraction in the classification of jobs to identify the key role players using a mixed method (qualitative and sufficient data). The application of expert systems or decision support systems based on organizational data is increasing in the selection and hiring of personnel. The data was derived from in-depth and semi-structured interviews with 17 subject experts in bank human resources, who were selected based on purposeful sampling.Data analysis was done based on the Strauss and Corbin model in the form of open, axial, and selective coding in the Atlas TI8 software. The results showed that the classification of jobs for the key role players in public and private banks includes causal conditions (requirement of talent substitution, human resource management developments, and organizational challenges), intervening conditions (organizational limitations and fear and resistance), and contextual conditions (strengthens and drivers) strategies (developmental, supportive and creating) and short-term and long-term consequences are among the components of the job classification model for the key role players in public and private banks. Next, based on the database with the CART method, the data mining of job classification was done. Regarding the performance of the model, it showed variance values of 311.92 and a risk value of 288.19. The predictions in the model explained 28.9% of the differences observed in the variable "employment status of A employees' category".
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.
Mila Malekolkalami; Mohammad Hassanzadeh; Atefeh Sharif; Mansour Rezghi
Abstract
This study aims to conduct a bibliometric analysis of knowledge extraction to examine its grassroots and interdisciplinary interactions based on papers in the Scopus database between 1980 and 2022. The study uses Biblimetrix, performance analysis, and science mapping techniques using 307 articles extracted ...
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This study aims to conduct a bibliometric analysis of knowledge extraction to examine its grassroots and interdisciplinary interactions based on papers in the Scopus database between 1980 and 2022. The study uses Biblimetrix, performance analysis, and science mapping techniques using 307 articles extracted from the Scopus database. The study used Biblimetrix (R package) and VOSviewer as a tool to carry out the performance analysis and science mapping analysis. The results show that the number of publications has significantly increased in the past decade, 1.53% of authors contribute at least a single article, and 98.46% of authors published multi-authored. China, the USA, and Japan were the most prolific countries in terms of the total number of citations and foreign collaborations. Expert Systems with Applications and the Journal of Knowledge Management are the top journals for knowledge extraction; Advances in Intelligent Systems and Computing (book series), and Lecture Notes in Computer Science are the top conference proceedings series in this field. Implications of knowledge extraction as an emerging discipline have been discussed based on the evidence and trends. The bibliometrics analysis can be helpful for professionals, scholars, and academics interested in bibliometric studies. it also provides essential information for making decisions on the vitality of disciplines.
