Machine Learning and Cyber Security

Cybersecurity has become an important subject of national, international, economic, and social importance that affects multiple nations. The literature review of known sources is forming theoretical bases of calculations on Sleptsov networks. The universal network of Sleptsov is a prototype of the Sleptsov network processor. The authors in the article research the emerging trends and theoretical perspectives of cyber security development using machine-learning technique with avatar-based management at the platform of Sleptsov net-processor and propose further prospects for development of hyper-computation.

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Predicting Dropout in Higher Education: a Systematic Review

10.5753/sbie.2021.217437 ◽

2021 ◽

Author(s):

Jailma Januário da Silva ◽

Norton Trevisan Roman

Keyword(s):

Higher Education ◽

Machine Learning ◽

Systematic Review ◽

Literature Review ◽

Decision Tree ◽

Systematic Literature Review ◽

Machine Learning Technique ◽

Student Dropout ◽

Learning Technique ◽

Research Questions

In this article, we present a systematic literature review, carried out from February to March 2020, on the application of a machine learning technique to predict student dropout in higher education institutions. Besides describing the protocol followed during our research, which includes the research questions, searched databases and query strings, along with criteria for inclusion and exclusion of articles, we also present our main results, in terms of the attributes used by current research on this theme, along with adopted approaches, specific algorithms, and evalution metrics. The Decision Tree technique is the most used for the construction of models, and accuracy and recall and precision being the most used metric for evaluating models.

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Machine Learning With Avatar-Based Management of Sleptsov Net-Processor Platform to Improve Cyber Security

Machine Learning and Cognitive Science Applications in Cyber Security - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-8100-0.ch006 ◽

2019 ◽

pp. 139-153 ◽

Cited By ~ 1

Author(s):

Vardan Mkrttchian ◽

Leyla Ayvarovna Gamidullaeva ◽

Sergey Kanarev

Keyword(s):

Machine Learning ◽

Literature Review ◽

Petri Net ◽

Cyber Security ◽

Theoretical Basis ◽

Network Processor ◽

Security Applications ◽

The Future ◽

Universal Network ◽

Special Case

The literature review of known sources forming the theoretical basis of calculations on Sleptsova networks and on the basis of authors' developments in machine learning with avatar-based management established the basis for the future solutions to hyper-computations to support cyber security applications. The chapter established that the petri net performed exponentially slower and is a special case of the Sleptsov network. The universal network of Sleptsov is a prototype of the Sleptsov network processor. The authors conclude that machine learning with avatar-based management at the platform of the Sleptsov net-processor is the future solution for cyber security applications in Russia.

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What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

SSRN Electronic Journal ◽

10.2139/ssrn.3702749 ◽

2020 ◽

Author(s):

Si Cheng ◽

Ruichang Lu ◽

Xiaojun Zhang

Keyword(s):

Machine Learning ◽

Mutual Fund ◽

Machine Learning Technique ◽

Learning Technique

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The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽

10.3390/atmos11010111 ◽

2020 ◽

Vol 11 (1) ◽

pp. 111 ◽

Cited By ~ 2

Author(s):

Chul-Min Ko ◽

Yeong Yun Jeong ◽

Young-Mi Lee ◽

Byung-Sik Kim

Keyword(s):

Machine Learning ◽

Heavy Rainfall ◽

Extreme Rainfall ◽

Machine Learning Techniques ◽

Precipitation Forecast ◽

Machine Learning Technique ◽

Rainfall Forecast ◽

Quantitative Precipitation Forecast ◽

Correction Technique ◽

Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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