Association Rule Mining Based Algorithm for Recovery of Silent Data Corruption in Convolutional Neural Network Data Storage

This paper explores the usability of the Dice CAPTCHA via analysis of the time spent to solve the CAPTCHA, and number of tries for solving the CAPTCHA. The experiment was conducted on a set of 197 subjects who use the Internet, and are discriminated by age, daily Internet usage in hours, Internet experience in years, and type of device where a solution to the CAPTCHA is found. Each user was asked to find a solution to the Dice CAPTCHA on a tablet or laptop, and the time to successfully find a solution to the CAPTCHA for a given number of attempts was registered. Analysis was performed on the collected data via association rule mining and artificial neural network. It revealed that the time to find a solution in a given number of attempts of the CAPTCHA depended on different combinations of values of user’s features, as well as the most meaningful features influencing the solution time. In addition, this dependence was explored through prediction of the CAPTCHA solution time from the user’s features via artificial neural network. The obtained results are very helpful to analyze the combination of features having an influence on the CAPTCHA solution, and consequently, to find the CAPTCHA mostly complying to the postulate of “ideal” test.

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Data Mining Application using Association Rule Mining ECLAT Algorithm Based on SPMF

MATEC Web of Conferences ◽

10.1051/matecconf/201816401019 ◽

2018 ◽

Vol 164 ◽

pp. 01019

Author(s):

Jason Reynaldo ◽

David Boy Tonara

Keyword(s):

Data Mining ◽

Open Source ◽

Data Storage ◽

Association Rule ◽

Association Rule Mining ◽

Search Space ◽

Rule Mining ◽

Master Data ◽

Research Domain ◽

Data Mining Application

Data mining is an important research domain that currently focused on knowledge discovery database. Where data from the database are mined so that information can be generated and used effectively and efficiently by humans. Mining can be applied to the market analysis. Association Rule Mining (ARM) has become the core of data mining. The search space is exponential in the number of database attributes and with millions of database objects the problem of I/O minimization becomes paramount. To get the information and the data such as, observation of the master data storage systems and interviews were done. Then, ECLAT algorithm is applied to the open-source library SPMF. In this project, this application can perform data mining assisted by open source SPMF with determined writing format of transaction data. It successfully displayed data with 100 % success rate. The application can generate a new easier knowledge which can be used for marketing the product.

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Code Edit Recommendation Using a Recurrent Neural Network

Applied Sciences ◽

10.3390/app11199286 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9286

Author(s):

Seonah Lee ◽

Jaejun Lee ◽

Sungwon Kang ◽

Jongsun Ahn ◽

Heetae Cho

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Association Rule ◽

Association Rule Mining ◽

Recommendation System ◽

Software Evolution ◽

Rule Mining ◽

Mining Technique ◽

Recommendation Accuracy ◽

The Given

When performing software evolution tasks, developers spend a significant amount of time looking for files to modify. By recommending files to modify, a code edit recommendation system reduces the developer’s navigation time when conducting software evolution tasks. In this paper, we propose a code edit recommendation method using a recurrent neural network (CERNN). CERNN forms contexts that maintain the sequence of developers’ interactions to recommend files to edit and stops recommendations when the first recommendation becomes incorrect for the given evolution task. We evaluated our method by comparing it with the state-of-the-art method MI-EA that was developed based on the association rule mining technique. The result shows that our proposed method improves the average recommendation accuracy by approximately 5% over MI-EA (0.64 vs. 0.59 F-score).

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