In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature were used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, were considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data and the training algorithm is described. Less than 55% of the experimental data were used to significantly reduce the total number of input and target data points needed for training the model. Satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, was found. It is concluded that the generalized regression neural network is a powerful tool for intelligently modelling the curing process of rubber blends even in the case of a small dataset, and it can find a wide range of practical applications in the rubber industry.

• Klíčová slova
• curing process, generalized regression neural network, modelling, rubber blends,
• Publikační typ
• časopisecké články MeSH

Artificial neural network based modeling is a generic approach to understand and correlate different complex parameters of biological systems for improving the desired output. In addition, some new inferences can also be predicted in a shorter time with less cost and labor. As terpenoid indole alkaloid pathway in Vinca minor is very less investigated or elucidated, a strategy of elicitation with hydroxylase and acetyltransferase along with incorporation of various precursors from primary shikimate and secoiridoid pools via simultaneous employment of cyclooxygenase inhibitor was performed in the hairy roots of V. minor. This led to the increment in biomass accumulation, total alkaloid concentration, and vincamine production in selected treatments. The resultant experimental values were correlated with algorithm approaches of artificial neural network that assisted in finding the yield of vincamine, alkaloids, and growth kinetics using number of elicits. The inputs were the hydroxylase/acetyltransferase elicitors and cyclooxygenase inhibitor along with various precursors from shikimate and secoiridoid pools and the outputs were growth index (GI), alkaloids, and vincamine. The approach incorporates two MATLAB codes; GRNN and FFBPNN. Growth kinetic studies revealed that shikimate and tryptophan supplementation triggers biomass accumulation (GI = 440.2 to 540.5); while maximum alkaloid (3.7 % dry wt.) and vincamine production (0.017 ± 0.001 % dry wt.) was obtained on supplementation of secologanin along with tryptophan, naproxen, hydrogen peroxide, and acetic anhydride. The study shows that experimental and predicted values strongly correlate each other. The correlation coefficient for growth index (GI), alkaloids, and vincamine was found to be 0.9997, 0.9980, 0.9511 in GRNN and 0.9725, 0.9444, 0.9422 in FFBPNN, respectively. GRNN provided greater similarity between the target and predicted dataset in comparison to FFBPNN. The findings can provide future insights to calculate growth index, alkaloids, and vincamine in combination to different elicits.

• Klíčová slova
• Artificial neural network, Generalized regression neural network, MATLAB, Vinca minor,
• MeSH
• alkaloidy biosyntéza   MeSH
• kořeny rostlin metabolismus   MeSH
• neuronové sítě * MeSH
• Vinca metabolismus   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• alkaloidy MeSH

Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems.

• Klíčová slova
• Differential polynomial neural network, General partial differential equation composition, Multi-variable function approximation, Sum derivative term substitution,
• MeSH
• algoritmy MeSH
• interpretace statistických dat MeSH
• matematika * MeSH
• nelineární dynamika MeSH
• neuronové sítě * MeSH
• počasí MeSH
• počítačová simulace MeSH
• strojové učení MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Galanthamine is a third-generation cholinesterase inhibitor used against Alzheimer's disease. New analytical methods for the determination of galanthamine in pharmaceutical preparatives and biological fluids, such as urine and serum, were developed. An experimental design and artificial neural network approach were used for method optimization. Thirty-five ppb of galanthamine were determined in serum samples (with addition of 10 mM magnesium chloride and using solid-phase preconcentration).

• MeSH
• Alzheimerova nemoc farmakoterapie   MeSH
• cholinesterasové inhibitory analýza   MeSH
• elektroforéza kapilární metody   MeSH
• galantamin analýza   krev   moč   MeSH
• koncentrace vodíkových iontů MeSH
• léčivé přípravky chemie   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• regresní analýza MeSH
• reprodukovatelnost výsledků MeSH
• tělesné tekutiny chemie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• cholinesterasové inhibitory MeSH
• galantamin MeSH
• léčivé přípravky MeSH

In this work the advantages of using artificial neural networks (ANNs) combined with experimental design (ED) to optimize the separation of amino acids enantiomers, with alpha-cyclodextrin as chiral selector, were demonstrated. The results obtained with the ED-ANN approach were compared with those of either the partial least-squares (PLS) method or the response surface methodology where experimental design and the regression equation were used. The ANN approach is quite general, no explicit model is needed, and the amount of experimental work can be decreased considerably.

• MeSH
• aminokyseliny analýza   MeSH
• elektroforéza kapilární MeSH
• neuronové sítě * MeSH
• stereoizomerie MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• aminokyseliny MeSH

The precise experimental estimation of mechanical properties of rubber blends can be a very costly and time-consuming process. The present work explores the possibilities of increasing its efficiency by using artificial neural networks to study the mechanical behavior of these widely used materials. A multilayer feed-forward back-propagation artificial neural network model, with a strain and the carbon black content as input parameters and stress as an output parameter, has been developed to predict the uniaxial tensile response of vulcanized natural rubber blends with different contents of carbon black in the form of engineering stress-strain curves. A novel procedure has been created for the simulation of the optimized artificial neural network model with input datasets generated by a regression model of an experimental dependence of tensile strain-at-break on the carbon black content in the investigated blends. Errors of the prediction of experimental stress-strain curves, as well as of tensile strain-at-break, tensile stress-at-break and M100 tensile modulus were estimated for all simulated stress-strain curves. The present study demonstrated that the performance of a developed neural network model to predict the stress-strain curves of rubber blends with different contents of carbon black is also exceptionally high in the case of a network that had never learned the input data, which makes it a suitable tool for extensive use in practice.

• Klíčová slova
• artificial neural network, carbon black filled rubber blends, mechanical properties and characteristics, stress-strain curves, tensile tests of polymers,
• Publikační typ
• časopisecké články MeSH

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

• Klíčová slova
• airfield, airport, artificial neural network, bearing strength, fuzzy logic, prediction model, surface, unpaved,
• Publikační typ
• časopisecké články MeSH

Infertility has emerged as a significant public health concern, with assisted reproductive technology (ART) is a last-resort treatment option. However, ART's efficacy is limited by significant financial cost and physical discomfort. The aim of this study is to build Machine learning (ML) decision-support models to predict the optimal range of embryo numbers to transfer, using data from infertile couples identified through literature reviews. Binary classification models were developed to classify cases into two groups: those transferring two or fewer embryos and those transferring three or four. Four popular ML algorithms were used, including random forest (RF), logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN), considering seven criteria: the woman's age, sperm origin, the developmental qualities of four potential embryos, infertility duration, assessment of the woman, morphological qualities of the four best embryos on the day of transfer, and number of oocytes extracted. The stratified 3-fold cross-validation results show that the SVM model obtained the highest average accuracy (95.83%) and demonstrated the best overall performance, closely followed by the ANN and LR models with an average accuracy equal to 91.67%. The RF model achieved a slightly lower average accuracy (88.89%), which demonstrated the lowest variability. Testing on a new dataset revealed all models performed well, with ANN and SVM models classified all test set instances correctly, while the RF and LR models achieved 91.68% accuracy. These results highlight the superior generalization and effectiveness of the ANN and SVM models in guiding ART decisions.

• Klíčová slova
• Artificial neural network, assisted reproductive technology, embryo transfer, infertility, multi-criteria decision aiding, number of embryos,
• MeSH
• asistovaná reprodukce MeSH
• dospělí MeSH
• infertilita terapie   diagnóza   MeSH
• lidé MeSH
• neuronové sítě MeSH
• přenos embrya * metody   MeSH
• strojové učení * MeSH
• support vector machine MeSH
• těhotenství MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• těhotenství MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

There is an increasing interest in the use of hospital admission for Chronic obstructive pulmonary disease (COPD) in studies of short-term exposure effects attributed to air pollutants. However, little is known about the effect of air pollutants on COPD symptoms. This study was undertaken to determine whether there was an association between air pollutant levels and both hospital admissions and symptoms for COPD. For model comparison, we present Generalized Linear Model, Generalized Additive Model and a general approach for Bayesian inference via Markov chain Monte Carlo in generalized additive model. Furthermore, for comparing the predictive accuracy, Artificial Neural Networks (ANN) approach is given.

• MeSH
• Bayesova věta MeSH
• chronická obstrukční plicní nemoc epidemiologie   etiologie   MeSH
• hospitalizace statistika a číselné údaje   MeSH
• lidé MeSH
• lineární modely MeSH
• metoda Monte Carlo MeSH
• neuronové sítě MeSH
• Poissonovo rozdělení MeSH
• prediktivní hodnota testů MeSH
• znečištění ovzduší škodlivé účinky   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

• MeSH
• organizační inovace * MeSH
• rozhodování MeSH
• společenská třída MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Evropa MeSH