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Bind 3168, number 1
2. July 2024
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29–30 december 2023
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research article| 02. July 2024
My Kummie Naveen Kumar;
V. sheeja kumari;
V. sheeja kumariB)
Institut for Computer Science and Engineering, Saveetha University
, Chennai, Tamil Nadu,
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B)Please contact the author:sheejakumari.ssse@saveetha.com
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S. Ramesh
S. RameshC)
Institut for Computer Science and Engineering, Saveetha University
, Chennai, Tamil Nadu,
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B)Please contact the author:sheejakumari.ssse@saveetha.com
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Naveenkumark19@saveetha.com
C)
Rameshunmugam.ssse@saveetha.com
AIP Conf.Proc.3168, 020032 (2024)
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My Kummie Naveen Kumar,,V. sheeja kumari,,S. Ramesh;Comparison of the efficiency of the nearest neighbor and naive bayes with classification of anonymous spam.AIP Conf.Proc.2. July 2024;3168 (1): 020032.https://doi.org/10.1063/5.0218305
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The primary goal of this research is to show that the method of the K -densest near neighbor is better than the Naive Bayes method when it comes to identifying anonymous spammers.Neighbor and group 2 as naive bayes.begge These groups are described below.Thieves are twenty people in each of the groups that form the sample and the size of the sample was estimated using the Clincalc software with a GPOWER test at 80%, threshold of 0.05 and a reliability interval of 95%.Dan NB -Gorithm.a Independent significant value of 0.001 was found using SPSS and the probability level turned out to be 0.05.This shows that there is a difference between the two approaches charged in this study, which is statistically significant.Contrasts with naive bayes, k gives the nearest neighbor -Algorithm finds with a higher accuracy, making it more suitable for effective exploration of anonymous spam.
subjects
Artificial intelligence,, Machine Learning,, Bayesian inferior,, Statistical analysis
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