Machine learning of emerging markets in pandemic times

Ків, Арнольд Юхимович; Hryhoruk, Pavlo; Khvostina, Inesa; Solovieva, V. I.; Soloviev, Vladimir; Semerikov, Serhiy O.

doi:10.31812/123456789/4122

Cited by 8 publications

(4 citation statements)

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“…Thus, due to the rapid development of computer technologies, artificial intelligence (AI) has entered lives of ordinary Ukrainians, making it simpler and more comfortable. Chatbots built on the basis of neural networks [19][20][21]29] and machine learning [8,9,[34][35][36] technologies can communicate using auditory or textual methods. These computer programs are gradually displacing the usual communication marketing, and can significantly help in education.…”

Section: Resultsmentioning

confidence: 99%

Integration of chatbots into the system of professional training of Masters

et al. 2021

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The article presents and describes innovative technologies of training in the professional training of Masters. For high-quality training of students of technical specialties, it becomes necessary to rethink the purpose, results of studying and means of teaching professional disciplines in modern educational conditions. The experience of implementing the chatbot tool in teaching the discipline “Mathematical modeling of socio-economic systems” in the educational and professional program 124 System Analysis is described. The characteristics of the generalized structure of the chatbot information system for investment analysis are presented and given: input information, information processing system, output information, which creates a closed cycle (system) of direct and feedback interaction. The information processing system is represented by accounting and analytical data management blocks. The investment analysis chatbot will help masters of the specialty system analysis to manage the investment process efficiently based on making the right decisions, understanding investment analysis in the extensive structure of financial management and optimizing risks in these systems using a working mobile application. Also, the chatbot will allow you to systematically assess the disadvantages and advantages of investment projects or the direction of activity of a system analyst, while increasing interest in performing practical tasks. A set of software for developing a chatbot integrated into training is installed: Kotlin programming, a library for network interaction Retrofit, receiving and transmitting data, linking processes using the HTTP API. Based on the results of the study, it is noted that the impact of integrating a chatbot into the training of Masters ensures the development of their professional activities, which gives them the opportunity to be competent specialists and contributes to the organization of high-quality training.

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Section: Resultsmentioning

confidence: 99%

Integration of chatbots into the system of professional training of Masters

et al. 2021

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“…xlii This article highlights further research by the authors, begun in [36,45,46,73,182,196,210,236].…”

Section: Session 5: Cloud-based E-learning Platforms Tools and Servicesmentioning

confidence: 94%

9th Workshop on Cloud Technologies in Education: Report

Kiv

Семеріков

Shyshkina³

et al. 2022

CTE Workshop Proc.

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“…The empirical analysis of problem-solving methods using machine learning, particularly in sectors such as education, healthcare, and life safety, is a burgeoning area of research [3,4,10,[22][23][24][47][48][49]. This paper delves into the exploration of such methods, explicitly focusing on clustering and classification problems [13,36].…”

Section: Introductionmentioning

confidence: 99%

Empirical comparison of clustering and classification methods for detecting Internet addiction

Klochko,

Fedorets,

Klochko

2024

CTE Workshop Proc.

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Machine learning methods for clustering and classification are widely used in various domains. However, their performance and applicability may depend on the characteristics of the data and the problem. In this paper, we present an empirical comparison of several clustering and classification methods using WEKA, a free software for machine learning. We apply these methods to the data collected from surveys of students from different majors, aiming to detect the signs of Internet addiction (IA), a behavioural disorder caused by excessive Internet use. We use Expectation Maximization, Farthest First and K-Means for clustering, and AdaBoost, Bagging, Random Forest and Vote for classification. We evaluate the methods based on their accuracy, complexity and interpretability. We also describe the models developed by these methods and discuss their implications for identifying the respondents with IA symptoms and risk groups. The results show that these methods can be effectively used for clustering and classifying IA-related data. However, they have different strengths and limitations when choosing the best method for a specific task.

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Machine learning of emerging markets in pandemic times

Cited by 8 publications

References 1 publication

Integration of chatbots into the system of professional training of Masters

Integration of chatbots into the system of professional training of Masters

9th Workshop on Cloud Technologies in Education: Report

Empirical comparison of clustering and classification methods for detecting Internet addiction

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