Advances In Cyber-Physical Systems. – 2021. – Vol. 6, No. 2

Permanent URI for this collectionhttps://ena.lpnu.ua/handle/ntb/57236

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Засновник і видавець Національний університет «Львівська політехніка». Виходить двічі на рік з 2016 року.

Advances in Cyber-Physical Systems / Lviv Polytechnic National University ; editor-in-chief A. Melnyk. – Lviv : Lviv Politechnic Publishing House, 2021. – Volume 6, number 2. – 86 p. : il.

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Author: search.filters.author.Yurchak, IrynaHas files: Yes

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    Research of content-based image retrieval algorithms
    (Lviv Politechnic Publishing House, 2021) Yakubchyk, Eduard; Yurchak, Iryna; Lviv Polytechnic National University
    Finding similar images on a visual sample is a difficult AI task, to solve which many works are devoted. The problem is to determine the essential properties of images of low and higher semantic level. Based on them, a vector of features is built, which will be used in the future to compare pairs of images. Each pair always includes an image from the collection and a sample image that the user is looking for. The result of the comparison is a quantity called the visual relativity of the images. Image properties are called features and are evaluated by calculation algorithms. Image features can be divided into low-level and high-level. Low-level features include basic colors, textures, shapes, significant elements of the whole image. These features are used as part of more complex recognition tasks. The main progress is in the definition of high-level features, which is associated with understanding the content of images. In this paper, research of modern algorithms is done for finding similar images in large multimedia databases. The main problems of determining high-level image features, algorithms of overcoming them and application of effective algorithms are described. The algorithms used to quickly determine the semantic content and improve the search accuracy of similar images are presented. The aim: The purpose of work is to conduct comparative analysis of modern image retrieval algorithms and retrieve its weakness and strength.
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    Recommendation system for purchasing goods based on the decision tree algorithm
    (Lviv Politechnic Publishing House, 2021) Kohut, Yurii; Yurchak, Iryna; Lviv Polytechnic National University
    Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. ecommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer's preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer's favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, ssessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models – the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive ecommendation system for short period of time and minimum costs.