scholarly journals The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle

2018 ◽  
Vol 31 (11) ◽  
pp. 1700-1713
Author(s):  
Daniel Zaborski ◽  
Witold S. Proskura ◽  
Wilhelm Grzesiak
2017 ◽  
Vol 31 (2) ◽  
pp. 108-112
Author(s):  
N. P. Sidorova

Estimates of credit banking risk is one of the topical tasks of banking. Correct and timely assessment of the reliability of the bank's customers who applied for the loan will help reduce the bank's losses associated with credit risks. To increase the efficiency and validity of making decisions on the issuance of a loan, Data Mining methods can be used. The article considers Data Mining technologies, which are applicable for the implementation of the scoring method of the borrower's assessment.


2021 ◽  
Vol 806 (1) ◽  
pp. 012038
Author(s):  
I A Zakharenkova ◽  
T P Belyaeva ◽  
I N Igotti ◽  
T O Terenteva

2014 ◽  
Vol 519-520 ◽  
pp. 189-192
Author(s):  
Zhuo Shi Li ◽  
Ran Shi Jiang ◽  
Jian Li

Honeypot is a new type of active defense security technologies. This paper attempts to use of data mining methods to be mining and analysis of information collected on the honeypot system. Build a Windows system based on virtual machine technology research honeynet. Data collection be standardized and sequential pattern mining. Finding out the correlation between different data records and frequent with time-based sequence of audit data, which found that,select the law of value of the attack.


2014 ◽  
Vol 39 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Paweł Malinowski ◽  
Robert Milewski ◽  
Piotr Ziniewicz ◽  
Anna Justyna Milewska ◽  
Jan Czerniecki ◽  
...  

Abstract The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process.


2004 ◽  
Vol 46 (S1) ◽  
pp. 70-70
Author(s):  
Sabine Glaser ◽  
Susanne Menzler ◽  
Dirk Werber ◽  
Andrea Ammon ◽  
Lothar Kreienbrock

Author(s):  
Chubukova ◽  
Ponomarenko ◽  
Nedbailo

The subject of the research is the approach to the possibility of applying data mining methods in the framework of business analytics in order to optimize the adoption of management decisions by the company.The purpose of writing this article is to study of data mining methods features use of primary data, which act as a valuable resource of the company, which can be used to ensure competitive- ness in a particular market. Methodology. The research methodology is system- structural and comparative analyzes (to study the approaches of data mining data for the complex analysis of first data); monograph (studying the preconditions for the growth of data mining companies’ use in the process of data research); eco- nomic analysis (when assessing the feasibility of using machine learning methods to ensure the goals of business intelligence). The scientific novelty consists the peculiarities of data mining application as one of the directions of business analyt- ics are determined, which makes it possible to determine implicit relationships between known factor and result characteristics on the basis of primary data. The main directions of data manipulation are revealed: classification and forecasting, as well as correlation-regression analysis. The importance of using the basic meth- ods of statistical analysis in the process of studying primary data is proved. The specifics of the use of neural networks as one of the most important methods of machine learning are given. Conclusions. The use of data mining allows companies to increase the efficiency of the use of available data and optimize development strategies accordingly. The presence of a large number of machine learning meth- ods and statistical approaches expands the possibilities of comprehensive data analysis. Innovative technologies and specialized programming languages play an important role in this case.


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