Face recognition model based on privacy protection and random forest algorithm

Abstract Background Hypervirulent Klebsiella pneumoniae (hvKP) infections can have high morbidity and mortality rates owing to their invasiveness and virulence. However, there are no effective tools or biomarkers to discriminate between hvKP and nonhypervirulent K. pneumoniae (nhvKP) strains. We aimed to use a random forest algorithm to predict hvKP based on core-genome data. Methods In total, 272 K. pneumoniae strains were collected from 20 tertiary hospitals in China and divided into hvKP and nhvKP groups according to clinical criteria. Clinical data comparisons, whole-genome sequencing, virulence profile analysis, and core genome multilocus sequence typing (cgMLST) were performed. We then established a random forest predictive model based on the cgMLST scheme to prospectively identify hvKP. The random forest is an ensemble learning method that generates multiple decision trees during the training process and each decision tree will output its own prediction results corresponding to the input. The predictive ability of the model was assessed by means of area under the receiver operating characteristic curve. Results Patients in the hvKP group were younger than those in the nhvKP group (median age, 58.0 and 68.0 years, respectively; P < .001). More patients in the hvKP group had underlying diabetes mellitus (43.1% vs 20.1%; P < .001). Clinically, carbapenem-resistant K. pneumoniae was less common in the hvKP group (4.1% vs 63.8%; P < .001), whereas the K1/K2 serotype, sequence type (ST) 23, and positive string tests were significantly higher in the hvKP group. A cgMLST-based minimal spanning tree revealed that hvKP strains were scattered sporadically within nhvKP clusters. ST23 showed greater genome diversification than did ST11, according to cgMLST-based allelic differences. Primary virulence factors (rmpA, iucA, positive string test result, and the presence of virulence plasmid pLVPK) were poor predictors of the hypervirulence phenotype. The random forest model based on the core genome allelic profile presented excellent predictive power, both in the training and validating sets (area under receiver operating characteristic curve, 0.987 and 0.999 in the training and validating sets, respectively). Conclusions A random forest algorithm predictive model based on the core genome allelic profiles of K. pneumoniae was accurate to identify the hypervirulent isolates.

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An Effective Intrusion Detection Model based on Random Forest Algorithm with I-SMOTE

Proceedings of the 23rd International Conference on Enterprise Information Systems ◽

10.5220/0010393801750182 ◽

2021 ◽

Author(s):

. Weijinxia ◽

. Longchun ◽

. Wanwei ◽

. Zhaojing ◽

. Duguanyao ◽

...

Keyword(s):

Random Forest ◽

Intrusion Detection ◽

Random Forest Algorithm ◽

Detection Model ◽

Model Based

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A Signal Strength Fluctuation Prediction Model Based on the Random Forest Algorithm

Elektronika ir Elektrotechnika ◽

10.5755/j01.eee.20.5.7112 ◽

2014 ◽

Vol 20 (5) ◽

Cited By ~ 1

Author(s):

P. Dohnalek ◽

M. Dvorsky ◽

P. Gajdos ◽

L. Michalek ◽

R. Sebesta ◽

...

Keyword(s):

Random Forest ◽

Prediction Model ◽

Signal Strength ◽

Random Forest Algorithm ◽

Model Based

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FRMN - A Face Recognition Model Based on Convolutional Neural Network

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/585/1/012101 ◽

2019 ◽

Vol 585 ◽

pp. 012101

Author(s):

Shifeng Shang ◽

Haiyan Liu ◽

Qiang Qu ◽

Guannan Li ◽

Jie Cao

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Recognition Model ◽

Model Based

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Pig Face Recognition Model Based on a Cascaded Network

Applied Engineering in Agriculture ◽

10.13031/aea.14482 ◽

2021 ◽

Vol 37 (5) ◽

pp. 879-890

Author(s):

Rong Wang ◽

ZaiFeng Shi ◽

Qifeng Li ◽

Ronghua Gao ◽

Chunjiang Zhao ◽

...

Keyword(s):

Face Recognition ◽

Face Detection ◽

Individual Recognition ◽

Recognition Performance ◽

Individual Identification ◽

List Type ◽

Recognition Model ◽

Model Based ◽

Face Images ◽

Cascaded Model

HighlightsA pig face recognition model that cascades the pig face detection network and pig face recognition network is proposed.The pig face detection network can automatically extract pig face images to reduce the influence of the background.The proposed cascaded model reaches accuracies of 99.38%, 98.96% and 97.66% on the three datasets.An application is developed to automatically recognize individual pigs.Abstract. The identification and tracking of livestock using artificial intelligence technology have been a research hotspot in recent years. Automatic individual recognition is the key to realizing intelligent feeding. Although RFID can achieve identification tasks, it is expensive and easily fails. In this article, a pig face recognition model that cascades a pig face detection network and a pig face recognition network is proposed. First, the pig face detection network is utilized to crop the pig face images from videos and eliminate the complex background of the pig shed. Second, batch normalization, dropout, skip connection, and residual modules are exploited to design a pig face recognition network for individual identification. Finally, the cascaded network model based on the pig face detection and recognition network is deployed on a GPU server, and an application is developed to automatically recognize individual pigs. Additionally, class activation maps generated by grad-CAM are used to analyze the performance of features of pig faces learned by the model. Under free and unconstrained conditions, 46 pigs are selected to make a positive pig face dataset, original multiangle pig face dataset and enhanced multiangle pig face dataset to verify the pig face recognition cascaded model. The proposed cascaded model reaches accuracies of 99.38%, 98.96%, and 97.66% on the three datasets, which are higher than those of other pig face recognition models. The results of this study improved the recognition performance of pig faces under multiangle and multi-environment conditions. Keywords: CNN, Deep learning, Pig face detection, Pig face recognition.

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