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.

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.