At the current moment, all developed polarization methods utilize "single-point" statistical analysis algorithms for laser fields. A relevant task is to generalize traditional techniques by incorporating new correlation-based "two-point" algorithms for the analysis of polarization images. Theoretical foundations of the mutual and autocorrelation processing of phase maps of polarization-structural images of samples of dehydrated serum films are given. The maps of a new polarization-correlation parameters, namely complex degree of coherence (CDC) and complex degree of mutual polarization (CDMP) of soft matter layer boundary field by the example of dehydrated serum film samples are investigated. Two groups of representative samples, uterine myoma patients (control group 1) and patients with external genital endometriosis (study group 2), were considered. We applied a complex algorithm of analytical data processing - statistical (1stand 4th central statistical moments), correlation (Gram-Charlie expansion coefficients of autocorrelation functions) and fractal (fractal dimensions) parameters of polarization-correlation parameters maps. Objective markers for diagnosing extragenital endometriosis were found.

The article develops a technology for finding tweet trends based on clustering, which forms a data stream in the form of short representations of clusters and their popularity for further research of public opinion. The accuracy of their result is affected by the natural language feature of the information flow of tweets. An effective approach to tweet collection, filtering, cleaning and pre-processing based on a comparative analysis of Bag of Words, TF-IDF and BERT algorithms is described. The impact of stemming and lemmatization on the quality of the obtained clusters was determined. Stemming and lemmatization allow for significant reduction of the input vocabulary of Ukrainian words by 40.21% and 32.52% respectively. And optimal combinations of clustering methods (K-Means, Agglomerative Hierarchical Clustering and HDBSCAN) and vectorization of tweets were found based on the analysis of 27 clustering of one data sample. The method of presenting clusters of tweets in a short format is selected. Algorithms using the Levenstein Distance, i.e. fuzz sort, fuzz set and Levenshtein, showed the best results. These algorithms quickly perform checks, have a greater difference in similarities, so it is possible to more accurately determine the limit of similarity. According to the results of the clustering, the optimal solutions are to use the HDBSCAN clustering algorithm and the BERT vectorization algorithm to achieve the most accurate results, and to use K-Means together with TF-IDF to achieve the best speed with the optimal result. Stemming can be used to reduce execution time. In this study, the optimal options for comparing cluster fingerprints among the following similarity search methods were experimentally found: Fuzz Sort, Fuzz Set, Levenshtein, Jaro Winkler, Jaccard, Sorensen, Cosine, Sift4. In some algorithms, the average fingerprint similarity reaches above 70%. Three effective tools were found to compare their similarity, as they show a sufficient difference between comparisons of similar and different clusters (> 20%).
The experimental testing was conducted based on the analysis of 90,000 tweets over 7 days for 5 different weekly topics: President Volodymyr Zelenskyi, Leopard tanks, Boris Johnson, Europe, and the bright memory of the deceased. The research was carried out using a combination of K-Means and TF-IDF methods, Agglomerative Hierarchical Clustering and TF-IDF, HDBSCAN and BERT for clustering and vectorization processes. Additionally, fuzz sort was implemented for comparing cluster fingerprints with a similarity threshold of 55%. For comparing fingerprints, the most optimal methods were fuzz sort, fuzz set, and Levenshtein. In terms of execution speed, the best result was achieved with the Levenshtein method. The other two methods performed three times worse in terms of speed, but they are nearly 13 times faster than Sift4. The fastest method is Jaro Winkler, but it has a 19.51% difference in similarities. The method with the best difference in similarities is fuzz set (60.29%). Fuzz sort (32.28%) and Levenshtein (28.43%) took the second and third place respectively. These methods utilize the Levenshtein distance in their work, indicating that such an approach works well for comparing sets of keywords. Other algorithms fail to show significant differences between different fingerprints, suggesting that they are not adapted to this type of task.

One of the trends in information technologies is implementing neural networks in modern software packages [1]. The fact that neural networks cannot be directly programmed (but trained) is their distinctive feature. In this regard, the urgent task is to ensure sufficient speed and quality of neural network training procedures. The process of neural network training can differ significantly depending on the problem. There are verification methods that correspond to the task’s constraints; they are used to assess the training results. Verification methods provide an estimate of the entire cardinal set of examples but do not allow to estimate which subset of those causes a significant error. This fact leads to neural networks’ failure to perform with the given set of hyperparameters, making training a new one time-consuming.
On the other hand, existing empirical assessment methods of neural networks training use discrete sets of examples. With this approach, it is impossible to say that the network is suitable for classification on the whole cardinal set of examples.
This paper proposes a criterion for assessing the quality of classification results. The criterion is formed by describing the training states of the neural network. Each state is specified by the correspondence of the set of errors to the function range representing a cardinal set of test examples. The criterion usage allows tracking the network’s classification defects and marking them as safe or unsafe. As a result, it is possible to formally assess how the training and validation data sets must be altered to improve the network’s performance, while existing verification methods do not provide any information on which part of the dataset causes the network to underperform.

The problem of the article is related to the improvement of means of covert monitoring of the face and emotions of operators of information and control systems on the basis of biometric parameters that correlate with two-dimensional monochrome and color images. The difficulty in developing such tools has been shown to be largely due to the cleaning of images associated with biometric parameters from typical non-stationary interference caused by uneven lighting and foreign objects that interfere with video recording. The possibility of overcoming these difficulties by using wavelet transform technology, which is used to filter images by combining several identical, but differently noisy monochrome and color images, is substantiated. It is determined that the development of technology for the use of wavelet transforms is primarily associated with the choice of the type of basic wavelet, the parameters of which must be adapted to the conditions of use in a particular system of covert monitoring of personality and emotions. An approach to choosing the type of basic wavelet that is most effective in filtering images from non-stationary interference is proposed. The approach is based on a number of the proposed provisions and efficiency criteria that allow to ensure when choosing the type of basic wavelet taking into account the significant requirements of the task. A filtering procedure has been developed, which, due to the application of the specified video image filtering technology and the proposed approach to the choice of the basic wavelet type, allows to effectively clean the images associated with biometric parameters from typical non-stationary interference. The conducted experimental studies have shown the feasibility of using the developed procedure for filtering images of the face and iris of operators of information and control systems.

The forced oscillations of the damping mechanical system of solids "Ball Vibration Absorber (BVA) with linearly viscous resistance – a movable carrier body" under the influence of external harmonic excitation are considered. Based on Appell's formalism, the dynamic equations for the joint motion of a heavy ball without sliding into a spherical cavity of a carrier body are formulated and numerically studied. The amplitude-frequency characteristic of the damping mechanical system and the curves of the dependences of the maximum amplitude of the oscillations of the carrier body on the values of the radius of the spherical cavity and the coefficient of viscous resistance of the BVA are obtained. The conditions and restrictions on the rolling of a heavy ball in the spherical recess of the absorber without sliding are determined.

A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders’ blurriness. Clusters’ quality is estimated in an online mode with the help of a modified partition coefficient which is calculated in a recurrent form. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.