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The classification of solvents based on solvatochromic characteristics: the choice of optimal parameters for artificial neural networks

100%
Pushkarova Y., Kholin Y.
Open Chemistry
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2012
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vol. 10
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issue 4
1318-1327
EN
The Taft-Kamlet-Abboud hydrogen-bond acidity, hydrogen-bond basicity and polarity-polarizability are widely used as empirical characteristics of solvent-solute interactions. These solvatochromic parameters are determined from the absorption band positions of solvatochromic probes in the standard medium and in the medium under study. The practice of solvatochromic probing is growing rapidly, and the values of solvatochromic parameters are refined from time to time. As these values are rather close for many media, the classification of media based on these values can be tedious. This increases the choice of algorithms that can be employed in order to decrease the ambiguity of classification. The classification algorithms stable to small variations of solvatochromic parameters are of special interest. The artificial neural networks (ANN) proved to be a powerful tool for the supervised classification. The paper focuses on the search of optimal parameters of probabilistic, dynamic, Elman, feed-forward, and cascade ANN for the classification of solvent on the basis of their solvatochromic characteristics. Also, the influence of data variation on the stability of classification is examined. The dynamic and probabilistic neural networks have been found to be error-free and stable; they have significantly become such a common tool for supervised classification as linear discriminant analysis. [...]
2
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Diastolic blood pressure cut-off predicts major cerebrovascular events after minor ischaemic stroke: a post-hoc modelling study

88%
Atanassova P., Chalakova N., Dimitrov B.
Open Medicine
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2008
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vol. 3
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issue 4
430-437
EN
Only few follow-up studies have studied in detail the role of most important risk factors, but no reports were found on critical values (cut-offs) for such factors in prospectively predicting cerebrovascular events (CVE) in patients with minor ischaemic stroke (MIS). Estimates of predictive importance of such cut-offs may better inform and contribute to optimize treatment. This was a post-hoc modelling study with unique data from Bulgaria on 54 consecutive patients with MIS, aged ł 40, followed for 12 months for nonfatal or fatal CV events. A set of routine clinical demographic and known risk factors (SBP, DBP, HDL cholesterol, etc.) were explored using univariate statistics and multivariate regression models to identify the most important independent predictors of secondary CVE. An artificial neural network (ANN) model, irrespective of usual statistical constraints, also confirmed the specific role and importance of identified predictors. A receiver operating characteristics (ROC) curve and stratified survival analyses were used to define the best cut-off of most important predictors and validate the final model. During follow-up period of 11.1±2.4 months, 8 secondary CV events (14.8%) were observed only in males with MIS at the 5.8±2.7 months mark. No difference in age of patients with CV event (61.1±12.6 years) vs. those without (62.1±9.6 years) was found (p>0.05). The one-year risk for CVE was.15% (95%CI 7.1, 27.7%). The two most important risk factors in patients with versus without CV events were acute MIS onset (62.5 vs. 13.0%) and mean DBP at day 30 post-MIS (101.3±9.9 vs. 92.3±10.8 mmHg), with a relative importance by ANN of 20.92 versus 15.9 points, respectively. At multivariate logistic analysis only MIS onset and DBP were independently associated with the risk for secondary CVE (79.6% model accuracy, p model=0.0015). An increase of DBP with 1 mmHg was associated with 8% higher risk of CVE [adjusted OR=1.08 (95%Cl 1.004, 1.158)]. With this method, a novel cut-off predictive DBP value of 95 mmHg (ROCAUC=0.79, 95%Cl 0.60, 0.99, p=0.009) for CV events in patients with MIS has been found. In conclusions the new DBP cut-off (sensitivity >87%, specificity >69%) clearly discriminated between absence and presence of secondary CVE as also confirmed by stratified survival analysis (7 vs. 1 events, plog-rank =0.0103). This cut-off may be applied to better precisely evaluate and define, as earlier as possible, MIS patients at increased risk of secondary CV events.
3
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Herbal drug raw materials differentiation by neural networks using non-metals content

88%
Suchacz B., Wesolowski M.
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EN
Three-layer artificial neural networks (ANN) capable of recognizing the type of raw material (herbs, leaves, flowers, fruits, roots or barks) using the non-metals (N, P, S, Cl, I, B) contents as inputs were designed. Two different types of feed-forward ANNs - multilayer perceptron (MLP) and radial basis function (RBF), best suited for solving classification problems, were used. Phosphorus, nitrogen, sulfur and boron were significant in recognition; chlorine and iodine did not contribute much to differentiation. A high recognition rate was observed for barks, fruits and herbs, while discrimination of herbs from leaves was less effective. MLP was more effective than RBF.
4
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Application Of Artificial Neural Network for Prediction of the Cyclic Oxidation Behavior of Electrical Resistance Sintered Gamma-TiAl Intermetallics

75%
Garip Y., Garip Z., Ozdemir O., Garip Y., Garip Z., Ozdemir O.
Archives of Metallurgy and Materials
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2021
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vol. 66
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issue 2
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