The purpose of this article is to create a convolutional neural network model for identifying and predicting audio deepfakes by classifying voice content using deep machine learning algorithms and python programming language libraries. The audio content datasets are basic for the neural network learning process and are represented by mel spectrograms. The processing of graphic images of the audio signal in the heatmap format forms the knowledge base of the convolutional neural network. The results of the visualization of mel spectrograms in the ratio of the measurement of the frequency of sound and chalk determine the key characteristics of the audio signal and provide a comparison procedure between a real voice and artificial speech. Modern speech synthesizers use a complex selection and generate synthetic speech based on the recording of a person's voice and a language model. We note the importance of mel spectrograms, including for speech synthesis models, where this type of spectrograms is used to record the timbre of a voice and encode the speaker's original speech. Convolutional neural networks allow you to automate the processing of mel spectrograms and classify voice content: original or fake. The experiments conducted on test voice sets proved the success of learning and using convolutional neural networks using images of MFCC spectral coefficients to classify and study audio content, and the use of this type of neural networks in the field of information security to identify audio deepfakes.

Keywords: neural networks, detection of voice deepfakes, information security, speech synthesis models, deep machine learning, categorical cross-entropy, loss function, algorithms for detecting voice deepfakes, convolutional neural networks, mel-spectrograms

The current situation in the practice of designing complex technical systems with metrological support is characterized by the following important features: a) the initial information that can actually be collected and prepared at the early stages of design for solving probabilistic problems turns out, as a rule, to be incomplete, inaccurate and, to a high degree, uncertain; b) the form of specifying the initial information (in the form of constraints) in problems can be very diverse: average and dispersion characteristics or functions of them, measurement errors or functions of them, characteristics specified by a probability measure, etc. These circumstances necessitate the formulation and study of new mathematical problems of characterizing distribution laws and developing methods and algorithms for solving them, taking into account the constraints on the value and nature of change of the determining parameter (random variable) of a complex technical system. As a generalized integral characteristic of the determining parameter, the law of its distribution is chosen, which, as is commonly believed, fully characterizes the random variable under study. The purpose of this work is to develop a method that allows constructing distribution laws of the determining parameter of a complex technical system using the minimum amount of available information based on the application of Chebyshev inequalities. A method for characterizing the distribution law by the property of maximum entropy is presented, designed to model the determining parameter of complex technical systems with metrological support. Unlike the classical characterization method, the proposed method is based on the use of Chebyshev inequalities instead of restrictions on statistical moments. An algorithm for constructing the distribution function of the determining parameter is described. A comparison is given of the results of constructing distribution laws using the developed method and using the classical variational calculus.

Keywords: Chebyshev inequalities, complex technical system, design, determining parameter, characterization of distribution law

The article is devoted to the creation of a highly specialized automated information system for recording the parameters of the technological process of production of an industrial enterprise. The development of such software products will simplify and speed up the work of technologists and reduce the influence of the human factor in collecting and processing data.

Keywords: automated information system, system for recording production process parameters, Rammler-Breich diagram, role-based data access system

One of the most important points in increasing the conversion component of a web resource is identifying the most attractive places for the site user. To identify these locations, a site user activity data visualization tool was created that provides a visual representation of each user action on a site page.

Keywords: heat map, site, oculograph, fixation, priority area