scholarly journals Nomogram of Naive Bayesian Model for Recurrence Prediction of Breast Cancer

2016 ◽  
Vol 22 (2) ◽  
pp. 89 ◽  
Author(s):  
Woojae Kim ◽  
Ku Sang Kim ◽  
Rae Woong Park
Keyword(s):  
Breast Cancer ◽  
Bayesian Model ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Recurrence Prediction

scholarly journals Crash Risk Assessment of Off-Ramps, Based on the Gaussian Mixture Model Using Video Trajectories

2020 ◽  
Vol 12 (8) ◽  
pp. 3076
Author(s):  
Ting Xu ◽  
Yanjun Hao ◽  
Shichao Cui ◽  
Xingqi Wu ◽  
Zhishun Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Bayesian Model ◽  
Logistic Regression Model ◽  
Gaussian Mixture ◽  
Ordinal Logistic Regression ◽  
Crash Risk ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Ordinal Logistic Regression Model

The focus of this paper is the crash risk assessment of off-ramps in Xi’an. The time-to-collision (TTC) is used for the measurement and cross-comparison of the crash risk of each location. Five sites from the urban expressway in Xi’an were selected to explore the TTC distribution. An unmanned aerial vehicle and a camera were used to collect traffic flow data for 20 min at each site. The parameters, including speed, deceleration rate, truck percentage, traffic volume, and vehicle trajectories, were extracted from video images. The TTCs were calculated for each vehicle. The Gaussian mixture model (GMM) was proposed to predict the TTC probability density functions (PDFs) and cumulative density functions (CDFs) for five sites. The Kolmogorov–Smirnov (K-S) test indicated that the samples followed the estimated GMM distribution. The relationship between the crash risk level and influencing factors was studied by an ordinal logistic regression model and a naive Bayesian model. The results showed that the naive Bayesian model had an accuracy of 86.71%, while the ordinal logistic regression model had an accuracy of 84.81%. The naive Bayesian model outperformed the ordinal logistic regression model, and it could be applied to the real-time collision warning system.

scholarly journals Detection of fake-video uploaders on social media using Naive Bayesian model with social cues

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaojun Li ◽  
Shaochen Li ◽  
Jia Li ◽  
Junping Yao ◽  
Xvhao Xiao
Keyword(s):  
Bayesian Model ◽  
Rapid Development ◽  
Social Cues ◽  
The Internet ◽  
Feature Combination ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Proposed Model ◽  
Optimal Feature ◽  
Search And Recognition

AbstractWith the rapid development of the Internet, the wide circulation of disinformation has considerably disrupted the search and recognition of information. Despite intensive research devoted to fake text detection, studies on fake short videos that inundate the Internet are rare. Fake videos, because of their quick transmission and broad reach, can increase misunderstanding, impact decision-making, and lead to irrevocable losses. Therefore, it is important to detect fake videos that mislead users on the Internet. Since it is difficult to detect fake videos directly, we probed the detection of fake video uploaders in this study with a vision to provide a basis for the detection of fake videos. Specifically, a dataset consisting of 450 uploaders of videos on diabetes and traditional Chinese medicine was constructed, five features of the fake video uploaders were proposed, and a Naive Bayesian model was built. Through experiments, the optimal feature combination was identified, and the proposed model reached a maximum accuracy of 70.7%.

scholarly journals Prediction of algal bloom occurrence based on the naive Bayesian model considering satellite image pixel differences

2021 ◽  
Vol 124 ◽  
pp. 107416
Author(s):  
Meng Mu ◽  
Yunmei Li ◽  
Shun Bi ◽  
Heng Lyu ◽  
Jie Xu ◽  
...  
Keyword(s):  
Bayesian Model ◽  
Algal Bloom ◽  
Satellite Image ◽  
Naive Bayesian ◽  
Naïve Bayesian

scholarly journals Naive Bayesian Machine Learning to Diagnose Breast Cancer

2020 ◽  
Author(s):  
Arash Hooshmand
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Tissue Expression ◽  
Learning System ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Diagnose Breast Cancer ◽  
Tissue Samples ◽  
Naive Bayesian ◽  
Naïve Bayesian

Abstract A novel MLAC (Machine Learning Against Cancer) method to distinguish between cancerous and noncancerous RNA genomic data is developed and tested with 100\% accuracy on all healthy and cancerous Breast tissue samples. A Naive Bayesian ML (Machine Learning) system is trained using WES (Whole Exome Sequencing) data in a high-level i.e. normalized quantification of RNAs obtained from 1091 breast cancer samples’ WES files from the TCGA (The Cancer Genome Atlas) and 179 healthy samples’ WES data from the GTEx (Genotype-Tissue Expression) project. We could show that both sensitivity and specificity of the method in classification of cancerous and noncancerous cells is perfectly 100%.

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