African buffalo optimized multinomial softmax regression based convolutional deep neural network for software fault prediction

Saravanan, P.; Sangeetha, V.

doi:10.1016/j.matpr.2021.08.097

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Using different AF and optimizer, there are four AFs for the hidden layer that were considered: Swish [ 78 , 79 , 80 ], Tanh [ 81 , 82 , 83 ], Elu [ 84 , 85 , 86 ], and Sigmoid [ 87 , 88 , 89 ]. Two AFs are considered for the output layer: Sigmoid [ 79 , 81 , 88 , 89 ] and Softmax [ 90 , 91 ]. Lastly, three optimizers are used, namely, Adam [ 92 , 93 , 94 ], RMSProp [ 90 , 95 , 96 ], and SGD [ 97 , 98 ].…”

Section: Methodsmentioning

confidence: 99%

Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines

Ong

Prasetyo

Tayao

et al. 2022

IJERPH

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The National Health Insurance, “PhilHealth”, is the healthcare provider for Filipino citizens in the Philippines. The study focused on determining the effects of members’ satisfaction with PhilHealth among Filipino members. The study utilized 10 latent variables from the integrated Service Quality (SERVQUAL) and Expectation-Confirmation Theory (ECT). There are 500 respondents that are used and analyzed through Structural Equation Modeling (SEM) and a Deep Learning Neural Network (DLNN). Utilizing SEM, it was revealed that Reliability, Responsiveness, Socio-Economic Factors, Expectation, Perceived Performance, Confirmation of Beliefs, and Members’ Satisfaction are significant factors in the satisfaction of PhilHealth members. Utilizing DLNN, it was found that Expectation (EX) is the most significant factor, and it is consistent with the results of the SEM. The government can use the findings of this study for the improvement of PhilHealth. The framework that is used for the analysis can be extended and can apply to future research with regard to its provided services. The overall results, framework, and concept utilized may be applied by other service industries worldwide.

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Section: Methodsmentioning

confidence: 99%

Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines

Ong

Prasetyo

Tayao

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Studies related to software fault prediction area are summarized in this section. Saravanan et al (Saravanan et al, 2021) proposed an African buffalo optimizer based convolution neural network for fast training in the software fault prediction field. Kassaymeh et al (Kassaymeh et al, 2021) used a salp swarm optimizer for neural network training instead of backpropagation.…”

Section: Related Workmentioning

confidence: 99%

A hybrid approach based on k-nearest neighbors and decision tree for software fault prediction

Manpreet

Chhabra

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Software testing is a very important part of the software development life cycle to develop reliable and bug-free software but it consumes a lot of resources like development time, cost, and effort. Researchers have developed many techniques to get prior knowledge of fault-prone modules so that testing time and cost can be reduced. In this research article, a hybrid approach of distance-based pruned classification and regression tree (CART) and k- nearest neighbors is proposed to improve the performance of software fault prediction. The proposed technique is tested on eleven medium to large scale software fault prediction datasets and performance is compared with decision tree classifier, SVM and its three variations, random forest, KNN, and classification and regression tree. Four performance metrics are used for comparison purposes that are accuracy, precision, recall, and f1-score. Results show that our proposed approach gives better performance for accuracy, precision, and f1-score performance metrics. The second experiment shows a significant amount of running time improvement over the standard k-nearest neighbor algorithm.

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SS-WDRN: sparrow search optimization-based weighted dual recurrent network for software fault prediction

Brundha Elci,

Nandagopalan

2023

Knowl Inf Syst

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African buffalo optimized multinomial softmax regression based convolutional deep neural network for software fault prediction

Cited by 5 publications

References 15 publications

Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines

Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines

A hybrid approach based on k-nearest neighbors and decision tree for software fault prediction

SS-WDRN: sparrow search optimization-based weighted dual recurrent network for software fault prediction

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