In this study, we propose a novel adaptive algorithm for approximate nearest neighbor (ANN) search, based on the inverted file (IVF) index (cluster-based index) and online query complexity classification. The concept of the classical IVF search implemented in vector databases is as follows: all data vectors are divided into clusters, and each cluster is assigned to its central point (centroid). For an ANN search query, the closest centroids are determined, and the further search continues in the corresponding clusters only. In our study, the complexity of each query is assessed and classified with the use of results of an initial trial search in a limited number of clusters. Based on this classification, the algorithm dynamically determines the presumably sufficient number of clusters which is sufficient to achieve the desired Recall value, thereby improving vector search efficiency. Our experiments show that such a complexity classifier can be built with the use of a single feature, and we propose an algorithm for its training. We studied the impact of various features on the query processing and discovered a strong dependence on the number of clusters that contains at least one nearest neighbor (productive clusters). The new algorithm is designed to be implemented on top of the IVF search which is a well-known algorithm for approximate nearest neighbor search and uses existing IVF indexes that are widely used in the most popular vector database management systems, such as pgvector. The results obtained demonstrate a significant increase in the speed of nearest neighbor search (up to 35%) while maintaining a high Recall rate of 0.99. Additionally, the search algorithm is deterministic, which might be extremely important for tasks where the reproducibility of results plays a crucial role. The developed algorithm has been tested on datasets of varying sizes up to one billion data vectors.

This paper presents a method for detecting epileptic seizures based on electroencephalogram (EEG) analysis using a deep learning model based on Bidirectional Long Short-Term Memory (BiLSTM). The proposed model architecture allows taking into account temporal dependencies and nonlinear dynamics of EEG signals, which makes it effective for recognizing patterns associated with epileptic seizures. The model uses frequency, dynamic, fractal, correlation and statistical characteristics of the EEG signal as informative features. The study includes the stages of data preprocessing, feature extraction and neural network training. To improve the accuracy of the model, data normalization and regularization methods were used. The experimental results obtained on the publicly available TUH EEG dataset demonstrate high performance of the model in detecting epileptic activity: Sensitivity 96.2, Specificity 99.8, F1-score 0.77, AUC 0.98.

This paper is focused on the application of the fractal approach to the problem of preliminary processing of electroencephalogram (EEG) data to detect and remove physical and physiological artifacts. We study the fractal properties of EEG data containing artifacts and present the results of the Hurst exponent estimation obtained by the R/S analysis for various types of artifacts and areas of artifact-free data. We show that the Hurst exponent can be used as an informative feature for algorithms identifying and removing artifacts.

Solving high dimentional, multimodal, non-smooth global optimization problems faces challenges concerning quality of solution, computational costs or even the impossibility of solving the problem. Evolutionary algorithms, in particular, differential evolution algorithm proved itself as good method of global optimization. On the other side, approach based on subgradient methods are good for optimizing non-smooth functions. Combination of these two approaches enables to improve the quality of the algorithm, using the best features of both methods. In this paper, a new hybrid evolutionary approach based on differential evolution and subgradient algorithm as the local search procedure is proposed. Behavior of the proposed SSGDE algorithm was studied in a numerical experiment on three groups of generated tests. Comparison of the new hybrid algorithm with the pure DE approach showed the advantage of the SSGDE. It has been experimentally established that the proposed method finds the global minimum in the best way for all considered dimensions of the problem with respect to the differential evolution method. The SSGDE algorithm showed the best results with a significant increase in the number of functions.

The paper proposes a binary classification model designed to analyze electroencephalograms data to detecting pathologies associated with epilepsy. The model is based on the Convolutional Neural Network. As input data for the neural network, images obtained by transforming the values of the original electroencephalograms time series based on the Gramian Angular Field matrix were used. The model was trained on data from the Temple University Hospital electroencephalograms Seizure Corpus open data source. The proposed model demonstrated high performance metrics: accuracy – 91%, precision – 92%, recall – 95%, F1-0.93.