Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning

Lytvyn, Vasyl; Vysotska, Victoria; Демчук, А. Б.; Демків, І. І.; Ukhanska, Oksana; Hladun, Volodymyr; Kovalchuk, Roman; Petruchenko, Oksana; Dzyubyk, Lyudmyla; Sokulska, Nataliia

doi:10.15587/1729-4061.2019.164441

Cited by 12 publications

(7 citation statements)

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“…The paper [27] suggested that recently, there has been a discernible spike in interest across a very wide variety of application areas in the field of machine learning [27]. In [28], the findings agreed with [27] and referred to the importance of machine learning in the SH environment. Both [27,28] attempted to solve the issue of using ML from the technical perspective.…”

Section: Literature Review and Problem Statementmentioning

confidence: 72%

Developing a model of smart home usage among it specialists: the role of machine learning

Sharef

2022

EEJET

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Models of smart home usage dominate in developed countries, while in developing countries, they are still lacking. Technology Acceptance Model (TAM) is widely used in the context of smart home, and few studies examined other technology acceptance theories. The purpose of this study is to examine the experience of using smart home by Information Technology (IT) specialists in the Gulf Cooperation Council (GCC). The study deploys existence theories and proposes that the effect of relative advantage, convenience, accessibility, and cost on the intention to use smart home is positive. In addition, it was suggested that intention to use, as well as facilitating condition, directly affects the actual use of smart home. The knowledge of machine learning was proposed as a moderator between intention to use and actual use. The data were collected from IT specialists in the GCC using purposive sampling. The analysis was conducted using the Analysis of moment structures (AMOS). The findings showed that convenience, accessibility, and relative advantage have a positive effect, while cost has a negative effect on the intention to use smart home. The intention to use and facilitating condition affected positively the actual use. Knowledge in machine learning moderated positively the effect of intention to use on actual use. Decision makers are recommended to enhance the benefits of using the Internet of Things smart home and create a customized plan to enable using smart home at all levels. The knowledge of machine learning is critical for smart home usage, and customized courses in this regard are critical to boost the usage of smart home.

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Section: Literature Review and Problem Statementmentioning

confidence: 72%

Developing a model of smart home usage among it specialists: the role of machine learning

Sharef

2022

EEJET

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“…Smart Space is a working and living space embedded with computing, equipment information, and multi-module sensing devices. It has a natural and convenient HCI that provides interactive services for people's work and life (Lytvyn et al, 2019). The HCI not merely passively executes display and operation commands but actively interacts with people.…”

Section: The Service Design Of the Smart Space By The Vr-ilmmentioning

confidence: 99%

Designing Smart Space Services by Virtual Reality-Interactive Learning Model on College Entrepreneurship Education

Pan

2022

Front. Psychol.

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The purpose was to improve the limitations of traditional entrepreneurship education, realize the virtual interactive learning between college students and teachers, and stimulate students’ exploration of entrepreneurship. This work first discusses the working principle of Virtual Reality (VR) and builds an Interactive Learning Model (ILM) using VR. Then, the VR-ILM is used to design the Smart Space services. Harris Corner Detector (HCD) is used to detect the pixel grayscale change in the Smart Space image window. Further, the VR-ILM-based Smart Space is proposed according to the Smart Space design requirements and principles. Finally, the proposed VR-ILM-based Smart Space is applied to College Entrepreneurship Education (CEE). Its impact on the CEE market, employment in different industries, and students’ satisfaction with CEE are studied. The results show that the proposed VR-ILM-based Smart Space has increased the entrepreneurship teaching courses, entrepreneurship coaching activities, and entrepreneurship practice activities by 4, 6, and 24%, respectively. It has reduced entrepreneurship competitions and other forms of entrepreneurship education by 4 and 16%. The proposed VR-ILM-based Smart Space has dramatically improved the practical teaching of CEE. Meanwhile, real estate services have felt the most significant impact of the proposed VR-ILM-based Smart Space, with an employment increase of 43%. Lastly, students’ satisfaction with entrepreneurship education practice and teaching methods has increased by nearly 50%. The satisfaction with the internal environment has increased to 78%. The satisfaction with the curriculum system, teachers, and industry financing has increased from 30 to 45%, 24 to 36%, and 45 to 63%, respectively. The satisfaction with the teaching goal has increased to 62%. Thus, the proposed VR-ILM-based Smart Space has dramatically improved students’ satisfaction with CEE and has a different impact on the market, industry, and satisfaction with CE. The finding has a certain reference for the VR interactive model.

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“…That is, those clients that cannot find the information required leave the Web-resource unsatisfied; they would probably never visit it again, given the extremely competitive Internet market. According to [2], up to 40 % of visitors typically use the search feature of a Web-resource, thereby demonstrating an intention to buy a product based on title or code. Therefore, obtaining the required personalized search results and their further analysis by means of artificial intelligence based on the collaborative filtering and Machine Learning is essential for the successful development of e-business.…”

Section: Design Of a Recommendation System Based On Collaborative Filmentioning

confidence: 99%

“…Another form of user's assessment consists of tags associated by the user with the elements that the system provides [33]. For example, at MovieLens (https:// movielens.org/) the RS tags show the way the MovieLens users describe a movie, for instance: "drag" or "fantastic" [2]. Technically, RS employ multiple recommendation strategies based on editing algorithms such as Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic Filtering (DF), Knowledge-Based Filtering (KBF) [34].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user

Lytvyn

Vysotska

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Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning

Cited by 12 publications

References 56 publications

Developing a model of smart home usage among it specialists: the role of machine learning

Developing a model of smart home usage among it specialists: the role of machine learning

Designing Smart Space Services by Virtual Reality-Interactive Learning Model on College Entrepreneurship Education

Design of a recommendation system based on collaborative filtering and machine learning considering personal needs of the user

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