scholarly journals Prediction of Protein Interaction with Neural Network-Based Feature Association Rule Mining

2006 ◽  
pp. 30-39
Author(s):  
Jae-Hong Eom ◽  
Byoung-Tak Zhang
Keyword(s):  
Neural Network ◽  
Protein Interaction ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Feature Association

scholarly journals Analysis of Usability for the Dice CAPTCHA

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 221
Author(s):  
Alessia Amelio ◽  
Ivo Rumenov Draganov ◽  
Radmila Janković ◽  
Dejan Tanikić
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Internet Usage ◽  
Solution Time ◽  
The Internet ◽  
Rule Mining ◽  
Artificial Neural

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.

scholarly journals Code Edit Recommendation Using a Recurrent Neural Network

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).

Fuzzy Hyper-line Segment Neural Network by using Association Rule Mining

2018 ◽  
Vol 6 (12) ◽  
pp. 25-31
Author(s):  
B. S. Shetty ◽  
U. V. Kulkarni ◽  
Preetee M. Sonule ◽  
Manisha N. Shinde
Keyword(s):  
Neural Network ◽  
Line Segment ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining
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