Application Platform and Token Generation Software for Prepayment Meter Administration in Electricity Distribution Companies

Amhenrior, Henry Erialuode; Emagbetere, J.O.

doi:10.11648/j.mlr.20180301.11

Cited by 2 publications

(1 citation statement)

References 3 publications

(4 reference statements)

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“…The data was then prepared for cross-validation with eightfold and 5 repeats. After this, the models were trained using Lasso, Ridge, Elastic Net (Enet), and RandomForest classification methods in Siamcat, which uses the "mlr" package for machine learning based classification (Bischl et al, 2016). The models' performance for cross validation was evaluated using the area under the receiver operating characteristic (AUROC) value.…”

Section: Machine Learning Analysismentioning

confidence: 99%

Characterization of Supragingival Plaque and Oral Swab Microbiomes in Children With Severe Early Childhood Caries

et al. 2021

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The human oral cavity harbors one of the most diverse microbial communities with different oral microenvironments allowing the colonization of unique microbial species. This study aimed to determine which of two commonly used sampling sites (dental plaque vs. oral swab) would provide a better prediction model for caries-free vs. severe early childhood caries (S-ECC) using next generation sequencing and machine learning (ML). In this cross-sectional study, a total of 80 children (40 S-ECC and 40 caries-free) < 72 months of age were recruited. Supragingival plaque and oral swab samples were used for the amplicon sequencing of the V4-16S rRNA and ITS1 rRNA genes. The results showed significant differences in alpha and beta diversity between dental plaque and oral swab bacterial and fungal microbiomes. Differential abundance analyses showed that, among others, the cariogenic species Streptococcus mutans was enriched in the dental plaque, compared to oral swabs, of children with S-ECC. The fungal species Candida dubliniensis and C. tropicalis were more abundant in the oral swab samples of children with S-ECC compared to caries-free controls. They were also among the top 20 most important features for the classification of S-ECC vs. caries-free in oral swabs and for the classification of dental plaque vs. oral swab in the S-ECC group. ML approaches revealed the possibility of classifying samples according to both caries status and sampling sites. The tested site of sample collection did not change the predictability of the disease. However, the species considered to be important for the classification of disease in each sampling site were slightly different. Being able to determine the origin of the samples could be very useful during the design of oral microbiome studies. This study provides important insights into the differences between the dental plaque and oral swab bacteriome and mycobiome of children with S-ECC and those caries-free.

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Section: Machine Learning Analysismentioning

confidence: 99%