data preprocessing

Social networks are primarily represented and analyzed in the form of graphs with a large number of vertices and edges, structured as an adjacency matrix. The edges indicate relationships between individuals and act as connections between the vertices. The structural characteristics of each network are determined by the features of the edges and vertices within it. In this research, conducted on various types of social network data from the Stanford University database, a preprocessing method was employed using a competitive colonial algorithm for feature selection with the highest merit (lowest cost). To evaluate the impact of feature selection on the final output, experiments were conducted both with and without feature selection operations using various algorithms commonly used in this field. Valid metrics such as accuracy, precision, sensitivity, and recall were independently measured on the output results with an average of 10 program executions. The comparison of results between scenarios with and without feature selection showed a significant impact on all metrics of the final outcome. Many features in the datasets were either unused or contained minimal information. Not removing these features not only increased the computational burden but also affected the accuracy of the output results due to time-consuming execution.

As the best linear estimator, Kriging is now a well-established method in all types of  2D and 3D modeling, including geochemical mapping, rock types modeling, geophysical mapping, and resource estimation. In general, a multi-stage approach can be used for evaluating kriging parameters. The first step in the assessment of mineral resources using linear geostatistics is to remove outlier data and to find the best de-cluster size. After this stage, variogram models in the area under study must be provided by a spherical model. In this context, investigating kriging performance has always been of interest to numerous researchers. Evaluating kriging implementation for different applications has been a growing field of study in the last few decades. Although many authors have discussed various kriging parameters, it seems necessary to conduct more detailed reviews on range searching, high and low nugget effect, as well as 2D and 3D estimations. In this paper, an optimal search range was determined using quantitative kriging neighborhood analysis (QKNA), and the utility of this search range was explored by assessing kriging efficiency. To this end, the borehole dataset of the Gol-E-Gohar No.1 mine was used. In total, 2579 samples (of length 3 m) make up the database for this study.  In this research, the dataset was divided into two zones based on their associated geological domains. Based on the aforementioned parameters, 180 estimation strategies were generated for each rock type. The results indicate that the optimal search ranges of zone 1 are 688, 226, and 152, and the optimal search ranges of zone 2 are 482, 233, and 303.