scholarly journals Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology

2017 ◽  
Vol 6 (3) ◽  
pp. 250-255
Author(s):  
Razia Sulthana A ◽  
Subburaj Ramasamy
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
The Internet ◽  
Fuzzy Logics ◽  
Rule Mining ◽  
Customer Reviews ◽  
The Relationship ◽  
New Framework

The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.

scholarly journals Investigating Health Equity and Healthcare Needs among Immigrant Women Using the Association Rule Mining Method

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 195
Author(s):  
Ming-Hseng Tseng ◽  
Hui-Ching Wu
Keyword(s):  
Association Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Immigrant Women ◽  
Parental Knowledge ◽  
Physical Factors ◽  
Rule Mining ◽  
Socioeconomic Characteristics ◽  
Healthcare Needs ◽  
The Relationship

Equitable access to healthcare services is a major concern among immigrant women. Thus, this study investigated the relationship between socioeconomic characteristics and healthcare needs among immigrant women in Taiwan. The secondary data was obtained from “Survey of Foreign and Chinese Spouses’ Living Requirements, 2008”, which was administered to 5848 immigrant women by the Ministry of the Interior, Taiwan. Additionally, descriptive statistics and significance tests were used to analyze the data, after which the association rule mining algorithm was applied to determine the relationship between socioeconomic characteristics and healthcare needs. According to the findings, the top three healthcare needs were providing medical allowances (52.53%), child health checkups (16.74%), and parental knowledge and pre- and post-natal guidance (8.31%). Based on the association analysis, the main barrier to the women’s healthcare needs was “financial pressure”. This study also found that nationality, socioeconomic status, and duration of residence were associated with such needs, while health inequality among aged immigrant women was due to economic and physical factors. Finally, the association analysis found that the women’s healthcare problems included economic, socio-cultural, and gender weakness, while “economic inequality” and “women’s health” were interrelated.

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).

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