2Introduction: In our current era, the prevalence of cancer and its associated mortality rates have become a pressing concern. As such, finding effective methods for treating cancer has become a matter of significant importance. Abnormal angiogenesis is one of the common characteristics of different types of cancer. So far, the inhibition of vascular endothelial growth factor receptor 2 signaling pathway has received much attention due to its pro-angiogenic role. Therefore, finding reliable computational models to identify inhibitors can be effective in reducing time and cost. The purpose of this study was to use the support vector machine method to classify compounds into two inhibitory and non-inhibitory groups. Methods: In order to implement the machine learning model, the ligands studied in this research were extracted from the https://www.bindingdb.org database and after passing the necessary pre-processing, some filter-based and embedded feature selection methods were used.  After extracting the descriptors from the data, using the feature selection algorithm based on correlation, the dimensions of the data have been reduced in order to avoid overfitting the model. The classification task utilized a support vector machine model, employing various kernels such as Radial Basis Function (RBF), Polynomial, Sigmoid, and Linear. Results: The implementation of the support vector machine model with the RBF kernel along with the feature selection method based on correlation has resulted in a higher accuracy of 82.4% (P=0.008) compared to other feature selection methods used in this study. Conclusion: Observations indicate that the correlation-based feature selection method is more accurate than other methods used in this study.

support vector machine (SVM) is a powerful classification algorithm that separates samples by finding an optimal decision boundary. Its performance can degrade when feature variances differ across classes, potentially leading to suboptimal decision boundaries. A variance-weighted framework is proposed that reduces the influence of high-variance features while enhancing the impact of low-variance features, resulting in more accurate and robust decision boundaries. The method is applicable in both linear and nonlinear settings. Evaluation on synthetic datasets and real-world datasets, including Breast cancer and a9a, using cross-validation demonstrates that the variance-weighted SVM achieves higher accuracy and F1-score compared to soft SVM and LDM, particularly in scenarios with significant variance differences between classes.

The fuzzy support vector machine is one of the most exceptional methods to deal with uncertainty in the classification problem. The membership function is a proper way to model uncertainty. The goal of the membership function is to distinguish the different points in terms of their importance. The ordinary design of the membership function relies on the distance of the observations to the class center. However, the class center is affected by the presence of outliers. To prevent this effect, we utilized an unsupervised learning method called the Gaussian mixture model in the structure of the membership function. The proposed membership function is presented in two different categories distance-based and Bayes-based. Unlike the classical membership function, the contribution of outliers in the training phase decreased by diminishing their degree of importance. Hybridizing the classic fuzzy support vector machine classifier with the Gaussian mixture model will enhance the classification accuracy and also will prevent overfitting problems. The superiority of the proposed methods assessed by the synthetic and benchmarking dataset. The statistical significance is assessed by using the non-parametric Friedman and post-hoc Nemenyi tests.

In machine learning, models are derived from labeled training data where labels signify classes and features define sample attributes. However, noise from data collection can impair the algorithm’s performance. Blanco, Japón, and Puerto proposed mixed-integer programming (MIP) models within support vector machines (SVM) to handle label noise in training datasets. Nonetheless, it is imperative to underscore that their models demonstrate an observable escalation in the number of variables as sample size increases. The nonparallel support vector machine (NPSVM) is a bi-nary classification method that merges the strengths of both SVM and twin SVM. It accomplishes this by determining two nonparallel hyperplanes by solving two optimization problems. Each hyperplane is strategically po-sitioned to be closer to one of the classes while maximizing its distance from the other class. In this paper, to take advantage of NPSVM’s fea-tures, NPSVM-based relabeling (RENPSVM) MIP models are developed to deal with the label noises in the dataset. The proposed model adjusts observation labels and seeks optimal solutions while minimizing compu-tational costs by selectively focusing on class-relevant observations within an ϵ-intensive tube. Instances exhibiting similarities to the other class are excluded from this ϵ-intensive tube. Experiments on 10 UCI datasets show that the proposed NPSVM-based MIP models outperform their counter-parts in accuracy and learning time on the majority of datasets.

In various researches, implementation of meteorological parameters in drought prediction is studied. In the current work, meteorological drought classes based on Standardized Precipitation Index (SPI) for six seasonal scenarios (autumn, winter, spring, autumn +winter, winter +spring, and autumn +winter +spring) and meteorological predictors contained ground and sea surface temperature, weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) wide of North (0, 60) and East (0, 90) was applied in prediction models based on data from 1975 to 2005. In these models, temporal range of meteorological predictors is between Octobers to April month on the same predicted SPI. SPI was calculated based on mean precipitation at seasonal time scale in the main watershed of Tehran (Taleghan, Mamloo) by verse Weighted Distance method. The well-known statistical supervised machine learning method, support vector machine (SVM), is applied to predict SPI. Regarding to selected data points, the effective regions on Tehran precipitation are southern, southwestern and northwestern of Iran in spring, northern and northwestern in autumn and northwestern and western in winter. SVM depicted accurate results in prediction of SPI, spatially prediction of SPI in all scenarios, and it can be proposed as a very suitable statistical learning method in investigating of nonlinear behavior of meteorological phenomena with a short samples. The predicted SPI in spring and autumn are more accurate than the other scenarios. 

The rock mass permeability is one of the most important parameters regulating to the groundwater flow through the fracture’s rocks. The permeability distribution is an important part of estimating inflow into tunnels. The common methods to rock mass permeability estimation such as lugeon tests are expensive and very time consuming. The use of intelligent methods to estimate or classify data, especially in engineering problems, has been common in recent decades. Many algorithms have been designed and optimized for this purpose. support vector machines (SVM) is one of these methods. In this paper, using the SVM method, the Amirkabir tunnel has been classified from the permeability point of view. In order to optimize the parameters of this algorithm, random search method has been selected. The results show that the accuracy of modelling using this method based on experimental data is around 94.59%. Based on this result, amount 85% of tunnel length is classified in the low permeability category and water inflow into tunnel from this part of tunnel is negligible

Aims: Information of the protein structure is essential to understand the protein functions. Flexibility is one of the most important characteristics related to protein functions. Knowledge about flexibility of the protein structures can be helpful to improve protein structure prediction and comprehend their function. This study was conducted with the aim of investigating the flexibility prediction of protein structures, using support vector machine. Materials & Methods In this study, a balanced dataset containing 95 proteins was used. The features used in the present study for modeling amino acids formed a 33-dimensional vector. Some of them were obtained by crawling a window with the length of 17 focusing on the target amino acid on the protein chain, and some were only related to the target amino acid. To define the flexibility factor, the characteristics based on the information derived from the twodimensional angular variations was used. The information was calculated for each amino acid by considering the position of each amino acid alone and for the adjacent amino acid pairs in a seventeenth window, and the support vector machine method was used for prediction. Findings The accuracy was 73. 1%, F-measure was 71%, precision was 73%, and sensitivity was 73. 2%. Acceptable superiority of the proposed method was confirmed in comparison with the current methods. The angular representation of each protein was able to accurately demonstrate the 3D characteristics and properties of the protein structure. Conclusion The accuracy is 73. 1%, F-measure is 71%, precision is 73%, and sensitivity is 73. 2% and angular aspect is the best descriptor for flexibility prediction. Angular representation of each protein can accurately reflect the 3D characteristics and properties of the protein structure.