Decision support system for the machine learning methods selection in big data mining

Rudnichenko, Nickolay; Vychuzhanin, Vladimir; Petrov, I. B.; Shibaev, Denis

doi:10.32782/cmis/2608-65

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…CART is implemented by splitting a lot of data by gender at each step. On one branch of the tree there are examples where the logical rule is executed successfully, and on the other it is not successful [1]. In the process of increasing the KB structure, at each DT node, an enumeration of all possible attributes is performed, separating only the one that maximizes the target indicator.…”

Section: Methods For Diagnostics and Prediction Cts Risk Failures In ...mentioning

confidence: 99%

“…Examples of CTS are telecommunications, energy, transport systems, oil and gas production, mining and processing industries. Ensuring reliability is possible by using methods, diagnostic tools and predicting the technical state of systems at the stages of CTS design and operation [1,2]. Assessment of the technical state of elements and IEC is based on the analysis of the CTS failure risk, which combines the probabilities and possible damage from failures [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Data Mining Information System for Complex Technical Systems Failure Risk Evaluation

Vychuzhanin¹,

Rudnichenko²,

Otradskya³

et al. 2022

Computer Modeling and Intelligent Systems

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Section: Methods For Diagnostics and Prediction Cts Risk Failures In ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Mining Information System for Complex Technical Systems Failure Risk Evaluation

Vychuzhanin¹,

Rudnichenko²,

Otradskya³

et al. 2022

Computer Modeling and Intelligent Systems

View full text Add to dashboard Cite

Using Blockchain to Protect Data from Biometric Systems for Analysis and Access Control

Rudnichenko,

Vychuzhanin,

Shibaeva

et al. 2023

2023 IEEE 1st Ukrainian Distributed Ledger Technology Forum (UADLTF)

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A Framework to Optimize Student Performance using Machine Learning

Joshi,

Kapse

2024

IJRTE

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For scholars, mining data and extracting information from huge databases has emerged as an intriguing field of study. Since a few decades ago, the concept of using data mining techniques to extract information has been around. The dataset was originally intended to be partitioned and the inherent features examined using classification and clustering algorithms. They base their predictions on these characteristics. These forecasts have been made in the area of educational data mining for a variety of reasons, including to predict student success based on personal characteristics and help students find the right professors and courses. These goals have been drawn from the attrition and retention of students. These objectives are the focus of our research on student attrition and retention. Additionally, we have found exciting variables that aid in predicting students' success, suggesting the most qualified instructors, and assisting them in course selection.

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Decision support system for the machine learning methods selection in big data mining

Cited by 4 publications

References 5 publications

Data Mining Information System for Complex Technical Systems Failure Risk Evaluation

Data Mining Information System for Complex Technical Systems Failure Risk Evaluation

Using Blockchain to Protect Data from Biometric Systems for Analysis and Access Control

A Framework to Optimize Student Performance using Machine Learning

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