Today, search engines are the gateway to the web. With the increasing popularity of the web, the efforts to exploit it for commercial, social, and political purposes have also increased, making it difficult for search engines to distinguish good content from spam. The concept of web spam was first introduced in 1996 and quickly became recognized as one of the key challenges for the search engine industry. The phenomenon of spam occurs primarily because a significant portion of web page visits comes from search engines, and users tend to check the first search results. The goal of identifying spam pages is to ensure that these pages cannot achieve high rankings using deceptive strategies. Our effort is to provide an effective method for identifying spam pages, thereby reducing the presence of spam in the top search results. In this article, two methods for combating web spam are proposed. The first method, called XGspam, identifies spam pages based on the Xgboost learning algorithm with an accuracy of 94.27%. The second method, named XGSspam, offers a solution to the challenge of imbalanced web data by combining the SMOTE oversampling algorithm with the Xgboost classification model, achieving an accuracy of 95.44% in identifying spam pages.

One of the ways to ensure security is to detect malware in computer systems by malware detection methods. Since this entails a lot of financial, time and human costs, the present research intends to rely on extracting useful information from raw data without the need to perform sampling and classification based on these features, costs reduce the listed. In this regard, for each malware sample, a set of content-based features has been calculated using advanced mechanisms. Also, powerful statistical features are considered as a complement to content-based features. Therefore, according to the research findings on the Microsoft malware database called BIG 2015, a cost-effective and fully automated classifier has been presented. In the proposed method using XGB algorithm and Random Forest, the accuracy of the classifier is 99.81 and the predictor error is set to 0.00470. The findings of this study show that the achievement of this research is to determine the superiority of operator replication features, segment ID replication, images extracted from malware over other features. As a result, by using this research in IDS, IPS and native antivirus systems, it is possible to increase the accuracy of malware detection and also reduce malware detection errors and computer crimes.

Assessing the ultimate strength of the stiffened plates forming the ship structure is the first step in assessing its ultimate strength. Over time and increase the life of the structure, failures such as cracks reduce the load-bearing capacity of the structure. The main purpose of this paper is to present a machine learning method based on Xgboost algorithm to calculate the ultimate compressive strength of stiffened plates with crack failure using the results of multiple finite element analyzes. To achieve the best possible results from the Xgboost algorithm, some of the hyperparameters in this algorithm have been optimized using the Bayesian optimization method. The results of this method show that the accuracy of using the optimized Xgboost algorithm is much higher than conventional methods based on linear regression.