Purpose: To determine the impact of document summarization parameters on the evaluation metrics of Classification algorithms for Persian texts. Methodology: 1000 news texts were collected from yjc. ir news agency website based on the number of visits, with at least 100 and at most 350 words, out of which 250 were selected randomly. Titles, summaries, and the texts of the 250 docs were included in three groups. The number of documents were increased by 100 percent in two stages, to 500 and 1000. After text preprocessing and deleting stop words by programming code, TF-ISF summarization technique was implemented on them. 12 Excel files were created from the words of original texts. Then, Bayesian, Decision tree, SVM and Rulebased algorithms implemented by Rapid Miner software, which provided 120 Excel output files for verifying accuracy, precision, and recall. Finally, five comparisons between the results were considered including comparing of results with 100% increase in the number of documents, comparing the parameters of TF and ISF summarizer, comparison of Bayesian Classification algorithms, decision tree, Rule and SVM, comparing the original text and summary and comparison of the documents labels. Findings: The results indicated the superiority of evaluation criteria in Classification of 1000 documents relative to those of 250 and 500, which in 84% of cases. Meanwhile, the ISF summarizer method compared to TF in 82% of comparison showed a greater impact on Classification accuracy. In addition, the values of the accuracy in Bayesian Classification and the SVM were better. The highest value obtained from the accuracy (96. 67%) in the SVM Classification by 1000 documents of original text and ISF summarizer technique. Conclusion: Appropriate parameters for summarization and efficient Classification Techniques can improve the accuracy of Persian text Classification process, while the required time also decreases. The best results obtained in the evaluations show that ISF summarizer, Bayesian and SVM algorithms, 1000 documents, as well as the main text are more effective.

The rock mass Classification system is utilized to categorize rocks, and has been used in engineering projects and stability investigations. It focuses on the parameters of rock mass and engineering applications, which include tunnels, slopes, foundations, etc. Rock mass Classification is valuable in the areas where the collection of samples and yielding of observation is difficult. With the advancement in technology, various machine-based model algorithms have been used, i.e., ANN and MLR in rock mass Classification from prior few years. In the present work, the rock mass Classification has been discussed, i.e., rock load, stand up time, RQD, RMR, Q, GSI, SMR, and RMi along with their applications. Considering all the parameters, it is concluded that for slope stability in a poor rock condition, the applicability of GSI is sufficient when compared with RMR. GSI also provides a highly accurate valuation of geo-mechanical properties, making it a valuable tool for the engineers and geologists. Also, the RMR values obtained from the ANN model provide better results for tunnels when compared with MLR and the conventional method. The ARMR Classification of Slate, Shale, Quartz Schist, Gneiss, and Calcschist at 5 different locations of the world were 51-54, 66-70, 57-60, 35, 65-70, respectively.  The range for slate and shale was found to be moderately anisotropic, while quartz schist, gneiss, and calcschist were found to be slightly anisotropic and highly anisotropic.

Medical data mining has been a widespread data mining area of late. Mainly, diagnosing cancers is one of the most important topics that many researchers studied to develop intelligent decision support systems to help doctors. In this research, three different classifiers are used to improve the performance in terms of accuracy. The classifiers are Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Random forests (RF). Two machine learning repository datasets are used to evaluate and verify the Classification methods. Classifiers are trained using the 10-fold cross-validation strategy, which splits the original sample into training and testing sets. In order to assess classifier efficiency, accuracy (AC), precision, recall, specificity, F1, and area under the curve are used (AUC).  The Experiments showed that the AdaBoost classifier’s achieved an accuracy of 100\% which is superior in both datasets in comparison with SVM and RF with AC of 97\%. The accuracy is also compared with another study from the previous work that uses the same datasets, and the results demonstrated that the current research has better accuracy than the other study.

Quality management tools and Techniques play an important role in preventive waste management and resources improvement. Consideration of some of the important factors could make the application of approaches more efficient and effective and enables managers to select their alternatives, properly. In this paper, strengths and weaknesses of quality management approaches are demonstrated. In the light of the importance of the availability of information for all Techniques and tools, their optional use with respect to their application has been emphasized. In the following discussions different Classification methods have been proposed, based on (i) graphic and non-graphic tools, (ii) collection, organizing and utilization of data, (iii) product development phases, and (iv) the role and the application of approaches, considering improvement recognition and evaluation, data collection and analysis, and also development, implementation and monitoring of action plans. Frameworks for the use of quality approaches in the process of continuous improvement, as well as a seven steps procedure for quality improvement have been proposed. In each of the stages, the type and amount of the usage of the quality management tools and Techniques have been addressed. Some of the considerable outcomes of the paper includes (i) most of the reviewed approaches are in the form of graphics and non-numerical, (ii) reviewed Techniques and tools have different shares in data collection, organizing an utilization, and (iii) the role of approaches in data utilization for action planning is less effective, compared to data collection and organizing.

Narrative methods is one of the complex and ongoing human challenges. Perhaps the first human question in the communication process has been, "How do I quote this?" This is a question that still persists today. In addition, other questions arise in the minds of narrators: "What are the main elements of storytelling?", "What are the narrative methods?" And "What method should I use to tell this story?" Evidence shows that, apart from the serious but general efforts of Plato and Aristotle or the unqualified theorizing of other scholars, nothing did in this field and didn’t establish a comprehensive system for the Classification of these methods. This qualitative research was conducted by descriptive-analytical method using reliable library documents and sources, to achieving a comprehensive Classification of narrative methods for the use of narrators. So, the definitions of the narrative and the main elements of the narrative were analyzed, and then are identified, and then a comprehensive model was provided to classifying Narrative methods in the performing arts using these elements and considering the previous Classifications and describing and analyzing important structural theories about the narrative.

The present study proposes a Customer Behavior Mining Framework on the basis of data mining Techniques in a telecom company. This framework takes into account the customers’ behavior patterns and predicts the way they may act in the future. Firstly, clustering technique is used to implement portfolio analysis and previous customers are divided based on socio-demographic features using k-means algorithm. Then, the cluster analysis is conducted based on two criteria, i. e., the number of hours the telecom services used and the number of the services selected by customers of each group. Six groups of customers are identified in three levels of attractiveness according to the results of the customer portfolio analysis. The second phase has been devoted to mining the future behavior of the customers. Predicting the level of attractiveness of newcomer customers and also the churn behavior of these customers are accomplished in the second phase. This framework effectively helps the telecom managers mine the behavior of their customers. This may lead to develop appropriate tactics according to customers’ attractiveness and their churn behavior. Improving managers’ abilities in customer relationship management is one of the obtained results of the study.