Web pages are crawled and indexed by search engines for fast accessing data on the web. One of the challenges in the search engines is web spam pages. There are many approaches to web spam pages detection such as measurement of HTML code style similarity, pages linguistic pattern analysis and machine learning algorithm on page content features. One of the famous algorithms has been used in machine learning approach is Support Vector Machine (SVM) classifier. Unfortunately SVM could not achieve a reasonable accuracy in this scope. In order to classify non-linear data in a linear manner, the SVM needs to use the idea of the kernel, which leads to enhanced classification capabilities. A kernel, implicitly maps the data to a higher-dimensional space. Recently basic structure of SVM has been changed by new extensions called Twin SVM (TSVM) to increase robustness and classification accuracy using two separate hyperplanes. Because of using two separate hyperplanes in TSVM, it is better to use multiple kernels in it. Kernel functions are designed based on specific data sample. Therefore they cannot use for general purpose. In this paper we improved accuracy of web spam detection by using two nonlinear kernels into TSVM as an improved extension of SVM. These two kernels have been created based on genetic algorithm. The classifier ability to data separation has been increased by using two separated kernels for each class of data. Effectiveness of new proposed method has been experimented with two publicly used spam datasets called UK-2007 and UK-2006.

In order to control and minimize the damaging impacts of floods, flood modeling or simulation is a fundamental solution. Identifying effective models for this purpose is crucial in watershed management. This study evaluates the accuracy of support vector machine models combined with the support vector machine (SVM), Grasshopper algorithm (SVM-GOA) and least square support vector machine (LS-SVM) in simulating the flood peak discharge of Poldokhtar station in the Karkheh basin. For this study, 74 flood events from 2009 to 2016 at the Poldokhtar station and data from 13 daily rainfall stations in the upstream area for the same period were utilized. Subsequently, 52 events were allocated for training, and 22 for validation. The comparison of results was conducted using three statistical indicators: Correlation coefficient (R2), Root mean square error (RMSE), Nash efficiency (Ns), and Standard error (SE). Additionally, uncertainty analysis was performed using two indexes: ARIL and POC. The results indicate the relative superiority of the LS-SVM model with SE=0.407, RMSE=110.16, NS= 0.91 and R2=0.92 compared to the SVM model with SE=0.5, RMSE=137.70, NS= 0.87 and R2=0.88 and SVM-GOA model with SE=0.519, RMSE=144.53, NS= 0.83  and R2=0.9. The study's overall conclusion is that the LS-SVM model is more accurate, faster, and easier to implement compared to the SVM and SVM-GOA models. As a result, it can be confidently preferred over the SVM and SVM-GOA models due to its significant advantages. The research emphasizes the critical importance of precise flood modeling and simulation in watershed management for mitigating the destructive impact of floods.

Purpose: Together with the increase of orthodontic treatment needs the use of functional apparatus has been increased. Furthermore, different studies has been done on skeletal and dental changes after the use of these functional appliances, but few studies discussed soft tissue changes in the field. So, this study was carried out in order to evaluate soft tissue changes following the use of Twin Block functional appliance.Methods & Materials: This study was quazi experimental done on 13 patients (7 girls and 6 boys) with 7 years and 7 months to 12 years and 6 months of age. All patients were Cl II Div.I malocclusion, Iranian, Muslim, resident of Tehran and with no previous orthodontic treatment and tooth extraction. The period of treatment with the apparatus varied between 7 to 17 months. The studied angular variables were nasolabial, nasofacial, facial convexity,H angle, mentocervical, nasomental and mentolabial. The liner variables were lower and upper lip to E line, upper and lower lip thickness, prominence and length, upper and lower face height. All variables were compared before and after the use of Twin Block and recorded in a questionnaire and were analyzed by T - test. Results: The study showed that nasolabial, nasofacial, Lower lip to E line and Upper lip length decreased slightly with the use of apparatus, while facial convexity, H angle, upper lip prominence, upper lip thickness and upper lip to E line decreased significantly. As well as, mentocervical, upper lip thickness, lower lip prominence and soft tissue lower face height increased slightly and nasomental, mentolabial, soft tissue upper face height and lower lip length increased significantly.Conclusion: Following the treatment with Twin Block, the lower face soft tissue moved anteriorly leading to decrease in profile convexity. The upper and lower face soft tissue height increases and their ratio improved. The upper and lower lip thickness reduced, but upper lip length did not change and lower lip length increased after the treatment. The space between upper and lower lip edge to E - line moved to a normal range and labiomental fold became wider.

Purpose: The aim of this paper is to present an enhanced variant of Twin Parametric-Margin Support Vector Machine (TPMSVM) that improves classification performance. Methodology: By replacing a variable in the objective function, we keep the samples of one class farther from the parametric margin hyperplane of the other class. Findings: The enhanced model is convex for both linear and nonlinear cases. Also, numerical experiments on UCI datasets show that the enhanced model performs better compared to two similar models for both linear and nonlinear cases. Originality/Value: The previous studies of TPMSVM that increased the accuracy through approaches such as assigning weights to data sample, converting it into an unconstrained model and adding a new term in the objective function, did not guarantee that all samples will be far and on the negative side of the margin hyperplane. However, this study provides an approach to overcome this disadvantage of TPMSVM.