This article discusses the issue of assessing the quality of predicting the dynamics of the human body in conditions of cardiovascular disease using intelligent software systems. To improve the forecast accuracy, the voting method of 3 competing systems was used, as well as the elimination of sparse data columns. Assessment of the quality of the prognosis of complications of cardiovascular diseases is carried out in terms of the accuracy and specificity of the diagnosis. The constructed system for 10 predicted diagnoses out of 12 showed a prediction accuracy of more than 90% with a specificity of more than 85%. This result shows a fairly high predictive ability of the created system when solving the problem of predicting the reaction of the human body to the onset of cardiovascular diseases (for example, complications of myocardial infarction).

The article solves the problem of creating models for predicting the course and complications of cardiovascular diseases. Artificial neural networks based on the Keras library are used. The original dataset includes 1700 case histories. In addition, the dataset augmentation procedure was used. As a result, the overall accuracy exceeded 84%. Furthermore, optimizing the network architecture and dataset has increased the overall accuracy by 17% and precision by 7%.

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The article deals with the computer vision system of the smart refrigerator “Robimarket”. The equipment of the working area of the refrigerator, the selection of a set of chambers, the collection of a training sample for the computer vision system are described. The choice of the artificial intelligence architecture of the computer vision system was made by comparative testing of the YOLOv3 and Mask R-CNN architectures. The comparison was made on one hardware platform, one training set and a set of test cases. As a result, a comparison table was created for the speed and quality values of each model. As a result, the Mask-RCNN architecture was chosen, which showed a significantly higher detection accuracy in the video stream with acceptable performance for this task.

The problem of increasing the efficiency of maintenance and repair is being solved by applying flexible strategies using expert systems implemented in mobile applications. An expert system is a set of programs that accumulates knowledge of specialists in a particular domain and replicates this empirical experience for consultations of less skilled professionals. Mobile application is a modern software which intended for working on tablets, smartphones and other mobile devices. This article deals with a detailed description of the work of the expert system, the rules and facts of work are formed. The output of solutions diagnosing expert system is beeing developed. The implementation of the automatic synchronization of the mobile application with the technical documentation of troubleshooting is scheduled. The improvement of the mechanism for predicting the malfunction and solution as an expert system dialogue with the user is occurred. The organization of work in offline mode in the absence of a network connection has been planned.