Dr. Ali Naimi-Sadigh, Dr. Mohammad Rabiei, Dr. Alireza Seghatoleslami,
Volume 9, Issue 2 (9-2022)
Abstract
Objectives: The National Conference of Information Technology Managers is conceivably the most significant of its nature in the country. Chief competitive advantage to comparable ones is its observant of products in IT as an alternative to papers. The purpose is a comprehensive design for the scientific evaluation of information technology products.
Method: For the evaluation plan of the premium information technology award (FAB), three main scientific-technological, managerial-economical and cultural-social axes were studied. At the end, the criteria and sub-criteria were interviewed using the experts' focus group method. Initially, the information technology products were classified according to their nature. Then, the importance of criteria and sub-criteria were determined for all their features. Finally, evaluation of the products was done based on their identified importance.
Findings: The products are divided into 9 groups according to the type of customer and the product usage. Each of these 9 groups will have unique features and different sub-criteria. They receive their total weight by points given via expert judges that will be their score compared to the other products, and therefore could rank the products.
Conclusion: After receiving the products, in the first stage, they are evaluated in the internal scientific evaluation committee of the conference. In the next stage, the products receiving the highest points are for the evaluation of the presence of internal and external judges.
Mojtaba Mazoochi, Dr Leila Rabiei, Dr Mohammad Moradi,
Volume 9, Issue 4 (1-2023)
Abstract
Introduction: Errors in data collection and failure to pay attention to data that is noisy in the collection process for any reason cause problems in data-based analysis and, as a result, wrong decision-making. Therefore, solving the problem of missing or noisy data before processing and analysis is of vital importance in analytical systems. The purpose of this paper is to provide a method to identify noisy data, outliers, and missing data and provide a suitable solution for these data.
Methods: This study is applied research. Data mining techniques including binning smoothing and regression models have been used to identify and replace outlier and noisy data.
Results: The results of the tests performed in the real environment related to the data of social networks show the proper performance of the proposed method. It has also been shown that the proposed method has higher accuracy compared to the methods of binning smoothing, average and linear regression. So that for the data related to the tweet section, the mean squared error obtained for the proposed method was equal to 0.04, the binning smoothing method was equal to 0.38, the linear regression method was equal to 0.05 and the average method was equal to 0.06.
Conclusion: The method presented in this article can initially identify outlier data through one-third and two-thirds normal, and then replace the outlier data with a linear regression model, which results in improving the performance of using and processing information and improving human-information interaction
