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.

Background: Diagnosing of obstructive sleep apnea (OSA) is an important subject in medicine. This study aimed to compare the performance of two data mining techniques, Support Vector Machine (SVM), and logistic Regression (LR), in diagnosing OSA. The best‑ fit model was used as a substitute for polysomnography (PSG), which is the gold standard for diagnosing this disease. Materials and Methods: A total of 250 patients with sleep problems complaints and whose disease had been diagnosed by PSG and referred to the Sleep Disorders Research Center of Farabi Hospital, Kermanshah, between 2012 and 2015 were recruited in this study. To fit the best LR model, a model was first fitted with all variables and then compared with a model made from the significant variables using Akaike’ s information criterion (AIC). The SVM model and radial basis function (RBF) kernel, whose parameters had been optimized by genetic algorithm, were used to diagnose OSA. Results: Based on AIC, the best LR model obtained from this study was a model fitted with all variables. The performance of final LR model was compared with SVM model, revealing the accuracy 0. 797 versus 0. 729, sensitivity 0. 714 versus 0. 777, and specificity 0. 847 vs. 0. 702, respectively. Conclusion: Both models were found to have an appropriate performance. However, considering accuracy as an important criterion for comparing the performance of models in this domain, it can be argued that SVM could have a better efficiency than LR in diagnosing OSA in patients.

Data represents a compendium of information that perpetually expands with each passing moment, contributed by individuals worldwide. Within the domain of medical science, this reservoir of data accumulates at an almost exponential rate, doubling in volume annually. The emergence of advanced Machine learning tools and techniques, subsequent to a substantial evolution in data mining strategies, has bestowed the capacity to glean insights and discern concealed patterns from vast datasets, thus enabling extensive analytical pursuits. This study delves into the application of Machine learning algorithms to enhance societal well-being by harnessing the transformative potential of Machine learning advancements in the domain of blood glucose concentration estimation through Regression analysis. The culmination of this investigation involves establishing a correlation between glucose concentration and hematocrit volume. The dataset employed for this research is sourced from clinically validated electrochemical glucose sensors (commonly referred to as glucose strips). It encompasses diverse levels of both glucose concentration and hematocrit volume, the latter being furnished by an undisclosed source to ensure copyright compliance. This dataset comprises four distinct variables, and the aim of this research involves training the dataset using Regression techniques to predict two of these variables. Our results indicate that when utilizing linear Regression, the R2 score for GC is approximately 0.916, whereas for HV, it reaches around 0.537. In contrast, employing the Support Vector regressor yielded R2 scores of about 0.961 for GC and 0.506 for HV.