Wyniki wyszukiwania - BazTech

1

Simulating Caspian Sea surface water level by artifcial neural network and support vector machine models

Khaledian M. R., Isazadeh M., Biazar S. M., Pham Q. B.

Acta Geophysica

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2020

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Vol. 68, no. 2

553--563

EN

Reduction in sea water level can make services in nearshore structures difcult, and sea water level rise increases the risk to residential areas or the surrounding felds. For strategic planning, it is vital to take into account the present and future fuctuations of Caspian Sea water level. In this study, support vector machine and artifcial neural network are used to estimate water level of the Caspian Sea. A 34-year period dataset is used as input data for water level on the scale based at Anzali, Iran. Performances of these two models are compared according to some statistical indices. Results of this study indicate that support vector machine with an error of 4.782 mm and r=0.96 simulated the time series better, as compared with artifcial neural network with an error of 5.014 mm and r=0.957; furthermore, the uncertainty of this model is lower than that of the artifcial neural network, i.e., 0.04 verses 0.22.

2

A hybrid approach for the delineation of brain lesion from CT images

Gautam A., Raman B., Raghuvanshi S.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

504--518

EN

Brain lesion segmentation from radiological images is the most important task in accurate diagnosis of patients. This paper presents a hybrid approach for the segmentation of brain lesion from computed tomography (CT) images based on the combination of fuzzy clustering using hyper tangent function as the robust kernel and distance regularized level set evolution (DRLSE) function as the edge based active contour method. Kernel based fuzzy clustering method divides the image into different regions. These regions can be used to find region of interest by using DRLSE algorithm to generate the optimal region boundary. The proposed method results in smooth boundary of the required regions with high accuracy of segmentation. In this paper, results are compared with standard fuzzy c-means (FCM) clustering, spatial FCM, robust kernel based fuzzy clustering (RFCM) and DRLSE algorithms. The performance of the proposed method is evaluated on CT scan images of hemorrhagic lesion, which shows that our method can segment brain lesion more accurately than the other conventional methods.

3

Performance of the Support Vector Machines for medical classification problems

Ćwiklińska-Jurkowska M.

Biocybernetics and Biomedical Engineering

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2009

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Vol. 29, no. 4

63-81

EN

In the Support Vector Machines classification technique the best possible discriminating hyperplane between two populations is looked for by maximizing of margin between the populations' closest points. This idea is also applied for obtaining nonlinear discriminant boundaries by using different kernels for transformations, thus obtaining a nonlinear Support Vector Machines method. The nonlinear Support Vector Machines method is based on pre-processing of data to represent patterns in high dimension- usually much higher than the original variable feature space. In the presented work the dependency of Support Vector Machines performance on the kind of kernel and Support Vector Machines parameters is presented. The performance was assessed by resubstitution, 10- fold cross-validation, leave-one-out error, learning curves and Receiver Operating Characteristic curves. The kind and shape of the kernel is more important than regularization constant allowing different levels of overlapping classes. Combining boosting and Support Vector Machines did not improved performance in comparison to Support Vector Machines method alone, because both Support Vector Machines procedure and boosting are focused on observations difficult to classify.