A Risk Factor Analysis of West Nile Virus: Extraction of Relationships from a Neural-Network Model

2010 ◽  
pp. 208-217 ◽  
Author(s):  
Debarchana Ghosh ◽  
Rajarshi Guha
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Risk Factor ◽  
West Nile Virus ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Factor Analysis ◽  
West Nile

A Neural Network-Based Method for Risk Factor Analysis of West Nile Virus

Risk Analysis ◽  
2008 ◽  
Vol 28 (2) ◽  
pp. 487-496 ◽  
Author(s):  
Leilei Pan ◽  
Lixu Qin ◽  
Simon X. Yang ◽  
Jiangping Shuai
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Risk Factor ◽  
West Nile Virus ◽  
Risk Factor Analysis ◽  
West Nile

Insole System-Based Neural Network Model to Evaluate Force Risk in Cube Method: Application to Pepper Farming Tasks

2020 ◽  
Vol 10 (6) ◽  
pp. 1444-1451
Author(s):  
Hyunwoo Jung ◽  
Ahnryul Choi ◽  
Jose Moon ◽  
Seung Heon Chae ◽  
Kyungsuk Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Risk Factor ◽  
Musculoskeletal Disorders ◽  
Network Model ◽  
Neural Network Model ◽  
Observational Methods ◽  
Adult Males ◽  
Agricultural Field ◽  
Risk Levels

Most agricultural workers are exposed to musculoskeletal disorders due to the characteristics of agricultural work performed manually. As observational methods to prevent musculoskeletal disorders, a cube method has been proposed that considers the risk factors of posture, time and force workload simultaneously. However, force workload could evaluate using the weight of an object or qualitative measurement to prevent interfering with a worker’s occupation. The purpose of this study is to propose a novel method for evaluating quantitatively the risk factor of force in agricultural field using insole system and artificial neural network model. Agricultural simulated experiments were performed on ten healthy adult males and six observers were recruited to evaluate the risk factors of force for the experiments. The model was constructed using the signals measured in the insole system and the consensus among observers about evaluation results. To verify the performance of the model, the performance measurement was calculated using 10-fold cross-validation. The results of the proposed method are compared with those of the observers to verify reproducibility and usefulness. The model showed more than 97% prediction accuracy in all risk levels, and the proposed method showed 1.59%, 0.99 and 0.98 in the coefficient of variation, proportion agreement index, Cohen’s kappa coefficient, and high reproducibility and usefulness when compared with the observers’ evaluation. The method of quantitatively evaluating the risk factor of force proposed in this study is possible to be applied to various agricultural works using observational methods.

A neural network approach to risk factor analysis in osteoporosis

1996 ◽  
Vol 6 (S1) ◽  
pp. 139-139
Author(s):  
S. A. Jackson ◽  
L. Robertson ◽  
A. Tenenhouse ◽  
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Risk Factor ◽  
Risk Factor Analysis ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals Valve Internal Leakage Rate Quantification Based on Factor Analysis and Wavelet-BP Neural Network Using Acoustic Emission

2020 ◽  
Vol 10 (16) ◽  
pp. 5544
Author(s):  
Hanxue Zhao ◽  
Zhenlin Li ◽  
Shenbin Zhu ◽  
Ying Yu
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Factor Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Leakage Rate ◽  
In Situ Monitoring ◽  
Safe Operation ◽  
Bp Neural Network Model

Valve internal leakage is easily found because of various defects resulting from environmental factors and load fluctuation. The timely detection of valve internal leakage is of great significance to the safe operation of pipelines. As an effective means for detecting valve internal leakage, the acoustic emission technique is characterized by nonintrusive and strong anti-interference ability, which can realize the in situ monitoring of the valve running status in real time. In this paper, acoustic emission signals from an internal leaking valve were obtained experimentally. Then, the dimensionality reduction technology based on factor analysis was introduced to the processing of valve internal leakage detection data. Next, the wavelet decomposition was carried out to decompose the sample feature set into four subsets. Finally, the decomposed sample feature sets were inputted into the error backpropagation (BP) neural network quantitative model, respectively. The optimized results show that the predicted internal leakage rate by the wavelet-BP neural network model has good precision with an error of less than 10%. The wavelet-BP neural network model can realize the analysis of the valve internal leakage rate quantitatively and has good robustness, which provides technical support and guarantees the safe operation of natural gas pipeline valves.

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