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Conversion of numeric data to the spiking form and information losses in this process are serious problems limiting usage of spiking neural networks in applied informational systems. While physical values are represented by numbers, internal representation of information inside spiking neural networks is based on spikes — elementary objects emitted and processed by neurons. This problem is especially hard in the reinforcement learning applications where an agent should learn to behave in the dynamic real world because beside the accuracy of the encoding method, its dynamic characteristics should be considered as well. The encoding algorithm based on the Gaussian receptive fields (GRF) is frequently used. In this method, one numeric variable fed to the network is represented by spike streams emitted by a certain set of network input nodes. The spike frequency in each stream is determined by proximity of the current variable value to the center of the receptive field corresponding to the given input node. In the standard GRF algorithm, the receptive field centers are placed equidistantly. However, it is inefficient in the case of very uneven distribution of the variable encoded. In the present paper, an improved version of this method is proposed which is based on adaptive selection of the Gaussian centers and spike stream frequencies. This improved GRF algorithm is compared with its standard version in terms of amount of information lost in the coding process and of accuracy of classification models built on spike-encoded data. The fraction of information retained in the process of the standard and adaptive GRF encoding is estimated using the direct and reverse encoding procedures applied to a large sample from the triangular probability distribution and counting coinciding bits in the original and restored samples. The comparison based on classification was performed on a task of evaluation of current state in reinforcement learning. For this purpose, the classification models were created by machine learning algorithms of very different nature — nearest neighbors algorithm, random forest and multi-layer perceptron. Superiority of our approach is demonstrated on all these tests.

Inhibitors of histone deacetylases (HDACi) have considered as a promising class of drugs for the treatment of cancers because of their effects on cell growth, differentiation, and apoptosis. Angiogenesis play an important role in the growth of most solid tumors and the progression of metastasis. The vascular endothelial growth factor (VEGF) is a key angiogenic agent, which is secreted by malignant tumors, which induces the proliferation and the migration of vascular endothelial cells. Currently, the most promising strategy in the fight against cancer is the creation of hybrid drugs that simultaneously act on several physiological targets. In this work, a series of hybrids bearing N-phenylquinazolin-4-amine and hydroxamic acid moieties were studied as dual VEGFR-2/HDAC inhibitors using simplex representation of the molecular structure and Support Vector Machine (SVM). The total sample of 42 compounds was divided into training and test sets. Five-fold cross-validation (5-fold) was used for internal validation. Satisfactory quantitative structure—activity relationship (QSAR) models were constructed (R²_test = 0.64–0.87) for inhibitors of HDAC, VEGFR-2 and human breast cancer cell line MCF-7. The interpretation of the obtained QSAR models was carried out. The coordinated effect of different molecular fragments on the increase of antitumor activity of the studied compounds was estimated. Among the substituents of the N-phenyl fragment, the positive contribution of para bromine for all three types of activity can be distinguished. The results of the interpretation were used for molecular design of potential dual VEGFR-2/HDAC inhibitors. For comparative QSAR research we used physicochemical descriptors calculated by the program HYBOT, the method of Random Forest (RF), and on-line version of the expert system OCHEM (https://ochem.eu). In the modeling of OCHEM PyDescriptor descriptors and extreme gradient boosting was chosen. In addition, the models obtained with the help of the expert system OCHEM were used for virtual screening of 300 compounds to select promising VEGFR-2/HDAC inhibitors for further synthesis and testing.

Numerical modeling of turbulent flows requires finding the balance between accuracy and computational efficiency. For example, DNS and LES models allow to obtain more accurate results, comparing to RANS models, but are more computationally expensive. Because of this, modern applied simulations are mostly performed with RANS models. But even RANS models can be computationally expensive for complex geometries or series simulations due to the necessity of resolving the boundary layer. Some methods, such as wall functions and near-wall domain decomposition, allow to significantly improve the speed of RANS simulations. However, they inevitably lose precision due to using a simplified model in the near-wall domain. To obtain a model that is both accurate and computationally efficient, it is possible to construct a surrogate model based on previously made simulations using the precise model.

In this paper, an operator is constructed that allows reconstruction of the flow field obtained by an accurate model based on the flow field obtained by the simplified model. Spalart–Allmaras model with approximate nearwall domain decomposition and Spalart–Allmaras model resolving the near-wall region are taken as the simplified and the base models respectively. The operator is constructed using a local approach, i. e. to reconstruct a point in the flow field, only features (flow variables and their derivatives) at this point in the field are used. The operator is constructed using the Random Forest algorithm. The efficiency and accuracy of the obtained surrogate model are demonstrated on the supersonic flow over a compression corner with different values for angle $\alpha$ and Reynolds number. The investigation has been conducted into interpolation and extrapolation both by $Re$ and $\alpha$.

The advent of laser scanning technologies has revolutionized forestry. Their use made it possible to switch from studying woodlands using manual measurements to computer analysis of stereo point images called point clouds.

Automatic calculation of some tree parameters (such as trunk diameter) using a point cloud requires the removal of foliage points. To perform this operation, a preliminary segmentation of the stereo image into the “foliage” and “trunk” classes is required. The solution to this problem often involves the use of machine learning methods.

One of the most popular classifiers used for segmentation of stereo images of trees is a random forest. This classifier is quite demanding on the amount of memory. At the same time, the size of the machine learning model can be critical if it needs to be sent by wire, which is required, for example, when performing distributed learning. In this paper, the goal is to find a classifier that would be less demanding in terms of memory, but at the same time would have comparable segmentation accuracy. The search is performed among classifiers such as logistic regression, naive Bayes classifier, and decision tree. In addition, a method for segmentation refinement performed by a decision tree using logistic regression is being investigated.

The experiments were conducted on data from the collection of the University of Heidelberg. The collection contains hand-marked stereo images of trees of various species, both coniferous and deciduous, typical of the forests of Central Europe.

It has been shown that classification using a decision tree, adjusted using logistic regression, is able to produce a result that is only slightly inferior to the result of a random forest in accuracy, while spending less time and RAM. The difference in balanced accuracy is no more than one percent on all the clouds considered, while the total size and inference time of the decision tree and logistic regression classifiers is an order of magnitude smaller than of the random forest classifier.

Examining the spectral response of plants from data collected using remote sensing has a lot of potential for solving real-world problems in different fields of research. In this study, we have used the spectral property to identify the invasive plant Heracleum sosnowskyi Manden from satellite imagery. H. sosnowskyi is an invasive plant that causes many harms to humans, animals and the ecosystem at large. We have used data collected from the years 2018 to 2020 containing sample geolocation data from the Moscow Region where this plant exists and we have used Sentinel-2 imagery for the spectral analysis towards the aim of detecting it from the satellite imagery. We deployed a Random Forest (RF) machine learning model within the framework of Google Earth Engine (GEE). The algorithm learns from the collected data, which is made up of 12 bands of Sentinel-2, and also includes the digital elevation together with some spectral indices, which are used as features in the algorithm. The approach used is to learn the biophysical parameters of H. sosnowskyi from its reflectances by fitting the RF model directly from the data. Our results demonstrate how the combination of remote sensing and machine learning can assist in locating H. sosnowskyi, which aids in controlling its invasive expansion. Our approach provides a high detection accuracy of the plant, which is 96.93%.

The rheological behavior of aqueous suspensions based on nanoscale silicon dioxide particles strongly depends on the dynamic viscosity, which affects directly the use of nanofluids. The purpose of this work is to develop and validate models for predicting dynamic viscosity from independent input parameters: silicon dioxide concentration SiO₂, pH acidity, and shear rate $\gamma$. The influence of the suspension composition on its dynamic viscosity is analyzed. Groups of suspensions with statistically homogeneous composition have been identified, within which the interchangeability of compositions is possible. It is shown that at low shear rates, the rheological properties of suspensions differ significantly from those obtained at higher speeds. Significant positive correlations of the dynamic viscosity of the suspension with SiO₂ concentration and pH acidity were established, and negative correlations with the shear rate $\gamma$. Regression models with regularization of the dependence of the dynamic viscosity $\eta$ on the concentrations of SiO₂, NaOH, H₃PO₄, surfactant (surfactant), EDA (ethylenediamine), shear rate γ were constructed. For more accurate prediction of dynamic viscosity, the models using algorithms of neural network technologies and machine learning (MLP multilayer perceptron, RBF radial basis function network, SVM support vector method, RF random forest method) were trained. The effectiveness of the constructed models was evaluated using various statistical metrics, including the average absolute approximation error (MAE), the average quadratic error (MSE), the coefficient of determination $R^2$, and the average percentage of absolute relative deviation (AARD%). The RF model proved to be the best model in the training and test samples. The contribution of each component to the constructed model is determined. It is shown that the concentration of SiO₂ has the greatest influence on the dynamic viscosity, followed by pH acidity and shear rate γ. The accuracy of the proposed models is compared to the accuracy of models previously published. The results confirm that the developed models can be considered as a practical tool for studying the behavior of nanofluids, which use aqueous suspensions based on nanoscale particles of silicon dioxide.

Social media is a crucial indicator of the position of assets in the financial market. The paper describes the rigid solution for the classification problem to determine the influence of social media activity on financial market movements. Reputable crypto traders influencers are selected. Twitter posts packages are used as data. The methods of text, which are characterized by the numerous use of slang words and abbreviations, and preprocessing consist in lemmatization of Stanza and the use of regular expressions. A word is considered as an element of a vector of a data unit in the course of solving the problem of binary classification. The best markup parameters for processing Binance candles are searched for. Methods of feature selection, which is necessary for a precise description of text data and the subsequent process of establishing dependence, are represented by machine learning and statistical analysis. First, the feature selection is used based on the information criterion. This approach is implemented in a random forest model and is relevant for the task of feature selection for splitting nodes in a decision tree. The second one is based on the rigid compilation of a binary vector during a rough check of the presence or absence of a word in the package and counting the sum of the elements of this vector. Then a decision is made depending on the superiority of this sum over the threshold value that is predetermined previously by analyzing the frequency distribution of mentions of the word. The algorithm used to solve the problem was named benchmark and analyzed as a tool. Similar algorithms are often used in automated trading strategies. In the course of the study, observations of the influence of frequently occurring words, which are used as a basis of dimension 2 and 3 in vectorization, are described as well.