Кафедра авіаційних комп'ютерно-інтегрованих комплексів (НОВА)

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Відповідальний за розділ: Провідний фахівець кафедри авіаційних комп'ютерно-інтегрованих комплексів інституту інформаційно-діагностичних систем Шугалєй Людмила Петрівна. E-mail: shugaley2005@ukr.net

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Comparative Analysis of Text Vectorization Methods
(National Aviation University, 2023-06-27) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Savenko I. M., I. M.; Савенко, Ілля Михайлович
The paper considers methods of vectorization of textual properties of natural language in the context of the task of intellectual text analysis. The most common methods of statistical analysis of feature extraction and methods that taking into account the context are analyzed. The work describes the above types of text embeddings and their most common variations and implementations. Their comparative analysis was performed, which showed the relationship between the type of task of intellectual text analysis and the method showing the best metrics. The topology of the neural network, which is the basis for solving the problem and obtaining metrics, is described, and implemented. The comparative analysis was carried out using the relative time analysis of the theory of algorithms and classification metrics: accuracy, f1-score, precision, recall. The classification metrics are taken from the results of building a neural network model using the described framing methods. As a result, in the task of analyzing the tonality of the text, the statistical method of framing based on n-grams of character sequences turned out to be the best.
Determination of Characteristics of Infectious Endocarditis Based on Intelligent Processing of Ultrasonic Images
(National Aviation University, 2022-12-27) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Chumachenko, O. I.; Чумаченко, Олена Іллівна; Kolomoiets, S. O.; Коломоєць, Сергій Олексійович
The paper presents the pathogenetic factors in the development of infective endocarditis and identifies its predictors. The need for an echographic study associated with the search for the anatomical characteristics of infective endocarditis is shown: vegetation, destructive lesions (valve aneurysms, perforation or prolapse, etc.), the presence of abscesses, in the case of a prosthesis, a new divergence of the valve prosthesis may be a characteristic feature. A classification of research methods is presented that includes classical approaches of echocardiography (transthoracic, transesophageal) and new multidetector computed tomographic angiography and positron emission tomography with 18F-fluorodeoxyglucose and the need for their use in different cases is determined. A block diagram of an intelligent diagnostic system for infective endocarditis has been developed. To process the obtained images in order to diagnose and determine the geometric dimensions, shapes, quantity, location, characteristics of infective endocarditis, it is proposed to use convolutional neural networks that allow solving the problem of image segmentation.
Determination of Marketing Parameters for Building a Demand Forecasting Model using Neural Networks
(National Aviation University, 2023-12-27) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Novikov, Mikhaylo; Новіков, Mихайло Сергійович
This article is devoted to finding marketing parameters for building a demand forecasting model using neural networks using real data. The work deals with the problem of modeling product demand on the market in marketing using artificial intelligence and machine learning methods. The main features of existing approaches to building models of products on the market, their advantages and disadvantages are shown. The need for their improvement has been identified. A new methodology for solving the problem is presented. The model's demonstrated ability to predict consumer demand based on a variety of marketing parameters helps businesses plan inventory, production, and personnel more effectively and can lead to significant cost savings and improved efficiency.
Long-term Demand Forecasting: using an Ensemble of Neural Networks to Improve Accuracy
(National Aviation University, 2023-09-29) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Samoshyn, Andrii; Самошин, Андрій Олександрович
This research paper proposes a method of long-term demand forecasting based on an ensemble of neural networks that considers the novelty of the data. A tool for creating the ensemble was developed that uses a bagging technique as well as a modification that allows for the relevance and novelty of the data to be considered when creating training samples for each model in the ensemble. The study examines and compares the developed method with known approaches to long-term demand forecasting. Experimental results have indicated that the proposed approach allows for obtaining more accurate and reliable demand forecasts compared to existing methods. The results emphasize the importance of data in the demand forecasting process and indicate the potential of the proposed method to eventually improve inventory management strategies and product planning.
Modification of Semi-supervised Algorithm Based on Gaussian Random Fields and Harmonic Functions
(National Aviation University, 2023-06-27) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Chumachenko, O. I.; Чумаченко, Олена Іллівна; Lesohorskyi, K. S.; Лесогорський, Кирило Сергійович
In this paper we propose an improvement for a semi-supervised learning algorithm based on Gaussian random fields and harmonic functions. Semi-supervised learning based on Gaussian random fields and harmonic functions is a graph-based semi-supervised learning method that uses data point similarity to connect unlabeled data points with labeled data points, thus achieving label propagation. The proposed improvement concerns the way of determining similarity between two points by using a hybrid RBF-kNN kernel. This improvement makes the algorithm more resilient to noise and makes label propagation more locality-aware. The proposed improvement was tested on five synthetic datasets. Results indicate that there is no improvement for datasets with big margin between classes, however in datasets with low margin proposed approach with hybrid kernel outperforms existing algorithms with a simple kernel.
Recommender Systems Based on Reinforced Learning
(National Aviation University, 2023-06-27) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Sheruda, A. V.; Шеруда, Андрій Володимирович
This article is devoted to the problem of building recommender systems based on the use of artificial intelligence methods. The paper analyzes the algorithms of recommender systems. Analyzes the Markov decision-making process in the context of recommender systems. Approaches to the adaptation of reinforcement learning algorithms to the task of recommendations (transition from the task of supervised learning to the task of reinforcement learning) are considered. Reinforcement learning algorithms Deep Deterministic Policy Gradient and Twin Delayed DDPG were implemented with their own environment simulating the user's reaction, and the results were compared. The structure of a recommender system has been developed, in which the recommender agent generates a list of offers for an individual user, using his previous history of ratings. In the system itself, the user has the ability to interact only with the space of recommended films. This can be compared to the main YouTube page, which is a feed with suggestions, but we have a user interacting only with this feed and his reaction to objects in the recommendation space falls into recommender agent, which regulates the parameters of the model in the learning process.
Semi-supervised Learning Based on Graph Stochastic Co-Training
(National Aviation University, 2023-09-29) Sineglazov, Victor; Синєглазов, Віктор Михайлович; Yarovyi, Serhi; Яровий, Сергій Сергійович
This article is devoted to the development of a new approach in semi-supervised machine learning. The goal of this article is to analyze the accuracy of the single-view co-training system, based on the use of a modified graph-based stochastic label propagation algorithm for a multiclass classification problem. Graph transformation of data is preceded by feature decomposition, with three algorithms being compared: Singular Value Decomposition, Truncated Singular Value Decomposition, Iterative Primary Component Analysis, Kernel Primary Component Analysis. To improve the accuracy of the proposed method, additional parameter was included in the label propagation algorithm, allowing for the usage of the algorithm in co-training systems. Further performance increases are achieved via optimization of data modification, which is achieved by applying feature decomposition methods and parallelizing the calculation-heavy processes. As examples of practical use were considered solutions to the problem of multiclass classification for standard datasets of the library sklearn and for the real dataset Traffic Signs Preprocessed. Analyses of the results of the implementation of the proposed approach showed improvements in accuracy and of performance solving the multiclass classification problem.
Semi-supervised Support Vector Machine
(National Aviation University, 2023-03-27) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Samoshyn, A. O.; Самошин, Андрій Олександрович
The article considers a new approach to constructing a support vector machine with semi-supervised learning for solving a classification problem. It is assumed that the distributions of the classes may overlap. The cost function has been modified by adding elements of a penalty to it for labels not in their class. The penalty is represented as a linear function of the distance between the label and the class boundary. To overcome the problem of multicriteria, a global optimization method known as continuation is proposed. For a combination of predictions, it is suggested to use the voting method of models with different kernels. The Optuna framework was chosen as the tool for configuring hyperparameters. The following were considered as training samples: type_dataset, banana, banana_inverse, c_circles, two_moons_classic, two_moons_tight, two_moons_wide.

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