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This article analyzes the most famous sign languages, the correlation of sign languages, and also considers the development of a verbal robot hand gesture recognition system in relation to the Kazakh language. The proposed system contains a touch sensor, in which the contact of the electrical property of the user's skin is measured, which provides more accurate information for simulating and indicating the gestures of the robot hand. Within the framework of the system, the speed and accuracy of recognition of each gesture of the verbal robot are calculated. The average recognition accuracy was over 98%. The detection time was 3ms on a 1.9 GHz Jetson Nano processor, which is enough to create a robot showing natural language gestures. A complete fingerprint of the Kazakh sign language for a verbal robot is also proposed. To improve the quality of gesture recognition, a machine learning method was used. The operability of the developed technique for recognizing gestures by a verbal robot was tested, and on the basis of computational experiments, the effectiveness of algorithms and software for responding to a verbal robot to a voice command was evaluated based on automatic recognition of a multilingual human voice. Thus, we can assume that the authors have proposed an intelligent verbal complex implemented in Python with the CMUSphinx communication module and the PyOpenGL graphical command execution simulator. Robot manipulation module based on 3D modeling from ABB.

The detection of weeds at the stages of cultivation is very important for detecting and preventing plant diseases and eliminating significant crop losses, and traditional methods of performing this process require large costs and human resources, in addition to exposing workers to the risk of contamination with harmful chemicals. To solve the above tasks, also in order to save herbicides and pesticides, to obtain environmentally friendly products, a program for detecting agricultural pests using the classical K-Nearest Neighbors, Random Forest and Decision Tree algorithms, as well as YOLOv5 neural network, is proposed. After analyzing the geographical areas of the country, from the images of the collected weeds, a proprietary database with more than 1000 images for each class was formed. A brief review of the researchers' scientific papers describing the methods they developed for identifying, classifying and discriminating weeds based on machine learning algorithms, convolutional neural networks and deep learning algorithms is given. As a result of the research, a weed detection system based on the YOLOv5 architecture was developed and quality estimates of the above algorithms were obtained. According to the results of the assessment, the accuracy of weed detection by the K-Nearest Neighbors, Random Forest and Decision Tree classifiers was 83.3 %, 87.5 %, and 80 %. Due to the fact that the images of weeds of each species differ in resolution and level of illumination, the results of the neural network have corresponding indicators in the intervals of 0.82–0.92 for each class. Quantitative results obtained on real data demonstrate that the proposed approach can provide good results in classifying low-resolution images of weeds.

The given data on the optical arrangement, in which the coordinate distributions of the real and imaginary component of the elements of the Jones matrix of optically thin polycrystalline layers are determined. Algorithms are presented and an experimental method for measuring the real and imaginary component of Jones-matrix images is analyzed. The experimental results of the study of statistical, correlation, and fractal parameters, which characterize the real component of the Jones-matrix image of polycrystalline networks of flat layers of the main types of human amino acids, are presented.

Hereby there is given the speaker identification basic system. There is discussed application and usage of the voice interfaces, in particular, speaker voice identification upon robot and human being communication. There is given description of the information system for speaker automatic identification according to the voice to apply to robotic-verbal systems. There is carried out review of algorithms and computer-aided learning libraries and selected the most appropriate, according to the necessary criteria, ALGLIB. There is conducted the research of identification model operation performance assessment at different set of the fundamental voice tone. As the criterion of accuracy there has been used the percentage of improperly classified cases of a speaker identification.