scholarly journals Towards Rational Biosurfactant Design—Predicting Solubilization in Rhamnolipid Solutions

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 534
Author(s):  
Ilona E. Kłosowska-Chomiczewska ◽  
Adrianna Kotewicz-Siudowska ◽  
Wojciech Artichowicz ◽  
Adam Macierzanka ◽  
Agnieszka Głowacz-Różyńska ◽  
...  
Keyword(s):  
Experimental Data ◽  
Support Vector Machines ◽  
Evolutionary Algorithm ◽  
Environmental Conditions ◽  
Molecular Volume ◽  
Support Vector ◽  
Micellar Solubilization ◽  
Vector Machines ◽  
Predictive Relationships ◽  
Kernel Support Vector Machines

The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logPBS, measure of purity), solubilizate (logPsol, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R2val of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research.

Support Vector Machine for Recognition of White Blood Cells of Leukaemia

2011 ◽  
pp. 93-122 ◽  
Author(s):  
Stanislaw Osowski ◽  
Tomasz Markiewicz
Keyword(s):  
Support Vector Machines ◽  
Blood Cells ◽  
White Blood Cells ◽  
Myelogenous Leukaemia ◽  
Image Feature ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Vector Machines ◽  
Kernel Support Vector Machines ◽  
The Individual

This chapter presents an automatic system for white blood cell recognition in myelogenous leukaemia on the basis of the image of a bone-marrow smear. It addresses the following fundamental problems of this task: the extraction of the individual cell image of the smear, generation of different features of the cell, selection of the best features, and final recognition using an efficient classifier network based on support vector machines. The chapter proposes the complete system solving all these problems, beginning from cell extraction using the watershed algorithm; the generation of different features based on texture, geometry, morphology, and the statistical description of the intensity of the image; feature selection using linear support vector machines; and finally classification by applying Gaussian kernel support vector machines. The results of numerical experiments on the recognition of up to 17 classes of blood cells of myelogenous leukaemia have shown that the proposed system is quite accurate and may find practical application in hospitals in the diagnosis of patients suffering from leukaemia.

scholarly journals Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

2016 ◽  
Vol 59 ◽  
pp. 04003
Author(s):  
Nuraddeen Muhammad Babangida ◽  
Muhammad Raza Ul Mustafa ◽  
Khamaruzaman Wan Yusuf ◽  
Mohamed Hasnain Isa ◽  
Imran Baig
Keyword(s):  
Support Vector Machines ◽  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Degree Polynomial ◽  
Low Degree ◽  
Vector Machines ◽  
Kernel Support Vector Machines
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