A Novel Software Reliability Prediction Algorithm Using Fuzzy Attribute Clustering and Nave Bayesian Classification

Rastogi, Neeta; Rastogi, Shishir; Darbari, Manuj

doi:10.26438/ijcse/v7i2.7382

Cited by 2 publications

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“…The pruning results are verified by Bayesian network to obtain the verified frequent large itemset, so as to formulate the recommendation strategy. In the following algorithm, algorithm 1 calls algorithm 2 [26]. 3.…”

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confidence: 99%

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Wang

2023

IJCAI

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This article addresses the problem of learners' information trek as well as overload and meet the learners' personalized learning needs by realizing learners' personalized development. In this article, the development scheme of e-learning resources personalized recommendation system based on Bayesian algorithm is proposed. This paper studies the personalized Association recommendation model integrating association rule mining and Bayesian network, thereby improving the association rule mining algorithm by combining historical record pruning and Bayesian network verification. In the process of association rule mining, the proposed methodology is combined with user history and the frequent item sets in association rules are filtered. The item sets below the given threshold are pruned. The pruned item set is input into the Bayesian verification network for personalized verification, and the verification results are sorted and recommended according to the ranking. This is done to give priority to the readers who really like the books. The recommendation model solves the problem of weak personalization in the existing recommendation system to a certain extent. The experiments show that Bayesian network can improve the personalization of association recommendation.Povzetek: Članek obravnava personalizacijo e-učenja z uporabo Bayesovega algoritma in učenjem asociativnih pravil, ki izboljšuje personalizacijo priporočil in povečuje učinkovitost.

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confidence: 99%

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Wang

2023

IJCAI

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“…One way to overcome this problem is to use a fuzzy approach (Rastogi et al (2019); Soares and Moraes (2018); Aziz et al (2016); Ferreira et al (2015); Moraes (2015)). This paper discusses the classi cation of cans using a fuzzy approach to the Naïve Bayes model to obtain better classi cation results than the original Naïve Bayes.…”

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confidence: 99%

Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach

Resti

Burlian

Yani

et al. 2020

sci. technol. indones.

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Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%. Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%.

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A Novel Software Reliability Prediction Algorithm Using Fuzzy Attribute Clustering and Nave Bayesian Classification

Cited by 2 publications

References 0 publications

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Personalized Recommendation System of E-learning Resources Based on Bayesian Classification Algorithm

Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach

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