Predicting financial distress, which normally happens before bankruptcy, is a challenging phenomenon and a crucial issue in all firms. The importance of data mining tools is well recognized, such that nowadays they are widely used in different financial issues such as, prediction of bankruptcy, financial distress, company's performance prediction, management fraud discovery and credit risk assessment. Using Support Vector Machine and combinations of cash flow components, this research attempts to predict financial distress of companies. Combinations of cash flows, as input variables (data) of the model, are selected based on specific criteria of financial distress. Results reveal that among Kernel functions of the model, polynomial function has the most power of prediction in year of financial distress or one and two years prior to year of distress.

Preparing a flood susceptibility map is necessary and the first step in reducing the damage caused by floods. Due to a lack of information in most of the basins, many researches uses data mining techniques for hydrological studies, especially floods. The aim study is to identify areas with flood susceptibility using a Support Vector Machine ((SVM)) in the Nekaroud basin. For this purpose, 12 geomorphologic, hydrological and physiographic parameters including slope, aspect, elevation classes, temperature, land use, rainfall, density and distance from the fault, density and distance from the drainages, density and distance from the road, which are provided in the ArcGIS, SAGA GIS and ENVI software’ s environments. The GPS device was also used to acquire flood points. Finally, all variables and flood points were entered into the R software in ASCII format with the same pixel size (12. 5 m). To evaluate model accuracy, ROC was used in the R software environment. The results of the evaluation showed that the (SVM) model has good accuracy in identifying flood susceptibility areas in the study area. In addition, the results of this study showed that flood susceptibility areas are more in the northern and northwest regions of the basin and in portions where the concentration of human settlements is higher, while the central regions of the basin with dense vegetation have a low sensitivity to flooding. The results of this study can help planners and researchers to do appropriate actions to prevent and reduce future flood risks. It can also be used to identify suitable and safe areas for construction development.

The downstream scour of the vertical drop can be one of the causes of instability and failure of this structure. In the present study, the downstream scour depth of this structure predicted using the Support Vector Machine ((SVM)) method. For this purpose, 104 experimental data used to estimate the scour depth. hese data are a function of the two dimensionless parameters of dansimetric Froude number (Frj) and tailwater depth (yt / yj) that have been entered into the (SVM) in three different models. To evaluate the results, the evaluation criteria of R2, NRMSE, DC, and MARE used. The results showed that model number (1) with the input combination (Frj and yt / yj) with R2 = 0.9777, DC = 0.929, NRMSE = 0.0775, and MARE = 11.89% for the test stage leads to the best result. The (SVM) method also has appropriate accuracy, acceptable results, and desirable performance in estimating the scour depth. Also, it was found that the densimetric froude number has a greater effect on estimating the relative scour depth compared to the tailwater depth.

In this study, a Support Vector Machine ((SVM)) based technique for timing irrigation projects is presented, and one of the most accurate predictive models in calculating the final project duration within the contract documents, where the research problem is projects are not completed within the contract period because most of the total project duration is determined In an unthoughtful manner by the employer. Linear regression models were applied to data and information for several projects, and a significant improvement in forecast accuracy was obtained.

Over time, numerous studies have been conducted to read license plates and recognize license plates. However, it is noteworthy that these studies usually do not have the ability to learn complex structures in images with high accuracy. For this purpose, this paper uses the high capacities of deep neural networks to learn license plate identifiers. The proposed model in this paper includes two main steps: highlighting license plates and reading the ID. In the proposed model, the Support Vector Machine ((SVM)) network is used to select the best range. After identifying the range of the license plate, its characters must be recognized. In this step, a gated convolutional neural network (GCNN) will be used. The proposed model is evaluated on two datasets, FZU Cars and Stanford Cars, and the results of the experiments show that this model has higher accuracy than other methods presented in both datasets.

Checking for leakage flow in hydraulic and marine structures during design practice is a crucial step, as uncontrolled leakage can cause irreparable damage. . Soft computing methods can be used to easily model, analyze and control complex systems. This study uses Support Vector Machine ((SVM)) method to predict leakage discharge of coastal dykes. Five different models are used to achieve this goal, with parameters including the length of the cutoff blanket, dyke depth, and water head considered. The best Support Vector Machine model is checked using a multivariate adaptive regression spline model (MARS) for prediction. Results show that the model including all parameters predicts settlement discharge with very good accuracy compared to the laboratory model, with a coefficient of determination and root mean square coefficient of 0. 949 and 0. 058 respectively in the test stage and 0. 93 and 0. 06 in the test phase estimates. The dyke depth parameter has the greatest effect on leakage flow, while the water head has the least effect among input parameters to the model. Although the adaptive regression multivariate spline model accurately estimates the annual dyke leakage flow rate, it is less accurate than the Support Vector Machine method.