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Enhancing Fault Divination Accuracy Using Naïve Bayes Classifier with PYTHON and PHP
M. Vijaya Bharathi,Rodda Sireesha 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.8
Programming Fault forecast has turned out to be most essential in programming Development uncommonly in programming Testing. The exact extrapolation of issues in conundrum can help to patch test effort, which decreases expenses and repair the nature of programming. Issue forecast model utilizing object situated measurements for code, datasets as info qualities to anticipate the issue probability by Naïve Bayes Classifier and these mock-ups have been far and wide utilized for bunching and grouping likewise exceptionally flawless eccentric to Bayesian systems for expansive range likelihood evaluation, generally in shortcoming expectation. In this paper, Naive Bayes classifier has been actualized on different consistent datasets.
Dregea taynguyenensis (Apocynaceae, Asclepiadoideae), a new species from Vietnam
TRAN, THE BACH,VAN, HAI DO,THU, HA BUI,CHOI, SANGHO,EUM, SANGMI,RODDA, MICHELE Magnolia Press 2018 Phytotaxa Vol.333 No.2
<P>The new species Dregea taynguyenensis from Vietnam is described, illustrated and compared with the other Dregea species occurring in Vietnam and neighbouring countries D. cuneifolia, D. sinensis, D. volubilis and D. yunnanensis. Dregea taynguyenensis differs from these species by the leaf blade secondary veins number, calyx lobes shape, and corolla lobes size.</P>