Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm

Zhao, Mingjing; Ji, Shouwen; Wei, Zhenlin

doi:10.1371/journal.pone.0238443

Cited by 9 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This classifier has good nonlinear mapping ability and learning speed. Consequently, the use of GRNN to solve classification problems showed beneficial prediction effects with only a few training samples available [Zhao et al, 2020]. However, it is worth noting the limitations in the interpretation of these models .…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

We performed a systematic review to evaluate the success of machine learning algorithms in the diagnosis and prognostic prediction of dental caries. The review protocol was a priori registered in the PROSPERO, CRD42020183447. The search involved electronic bibliographic databases: PubMed/Medline, Scopus, EMBASE, and Web of Science, and grey literature until December 2020. We excluded review articles, case series, case reports, editorials, letters, comments, educational methodologies or assessments of robotic devices and articles with less than 10 participants or specimens. Two independent reviewers selected the studies and performed the assessment of the methodological quality based on standardized scales. We summarize data on the machine learning algorithms used, software, performance outcomes such as accuracy/precision sensitivity/recall, specificity, area under the receiver operating characteristic curve (AUC), and positive/negative predictive values related to dental caries. Meta-analyses were not performed due to methodological differences. Our review included 15 studies (10 diagnostic studies and 5 prognostic prediction studies). Cross-sectional design studies were predominant (12). The most frequently used statistical measure of performance reported in diagnostic studies was AUC value, which ranged from 0.745 to 0.987. For most diagnostic studies, data from contingency tables were not available. Reported sensitivities were higher in low risk of bias prognostic prediction studies (median [IQR] of 0.996 [0.971-1.000] versus unclear/high risk of bias studies 0.189 [0-0.340]; p-value 0.025). While there were no significant differences in the specificity between these subgroups. We concluded that the use of these technologies for the diagnosis and prognostic prediction of dental caries, although promising, is at an early stage. The general applicability of the evidence was limited given that most models were developed outside the real clinical setting with a prevalence of unclear/high risk of bias. Researchers must increase the overall quality of their research protocols by providing a comprehensive report on the methods implemented.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…When spread approaches zero, the fitting performance can reach a good level, but it is easy to lead the over-fitting. Some researchers proposed to utilize metaheuristic algorithms to obtain the appropriate spread value of GRNN, such as Particle Swarm Optimization (PSO) [33,34]. The main advantage of PSO is its parallel and random optimization process, which contribute to a high degree of stability and generalization.…”

Section: Measured Roughnessmentioning

confidence: 99%

Surface roughness prediction for turning based on the corrected subsection theoretical model

Lü

Wang

Chen

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

In order to obtain the accurate prediction model of surface roughness in precision machining processes, this paper researches the subsection theoretical model for surface roughness in turning built by our research group, which considers feed rate, nose radius and tool minor cutting edge angle, and proposes two error correction models of the subsection theoretical model for improving theoretical model accuracy. The prediction performance of two error correction models is evaluated by 25 groups of turning data. The experimental results show that two error correction models have excellent prediction performance and significantly improve the prediction accuracy and stability of the subsection theoretical model. Moreover, the influence of turning parameters and tool geometry on surface roughness based on the three models, and the advantages and disadvantages of three models in prediction performance and test cost are analyzed, which provide an effective guidance for selection of parameters and the prediction model of surface roughness in the actual turning.

show abstract

“…Gao and Chen 17 used PSO-GRNN to predict sea clutter. Zhao et al 18 used PSO-GRNN to predict the occurrence of urban logistics public accidents. Zhang 19 used PSO-GRNN to predict air quality.…”

Section: Introductionmentioning

confidence: 99%

Yarn unevenness prediction using generalized regression neural network under various optimization algorithms

Jiang

Song

Zhang

et al. 2022

Journal of Engineered Fibers and Fabrics

View full text Add to dashboard Cite

Unevenness is one of the important parameters for evaluating yarn quality, but the current prediction accuracy of yarn unevenness is low. One of the important reasons is that there are few sample dataset for yarn unevenness prediction. For this problem, this paper applies generalized regression neural network to predict the unevenness of the yarn. Then, the generalized regression neural network is optimized by using particle swarm optimization, fruit fly optimization algorithm, and gray wolf optimizer, respectively. Finally, the optimized models were experimentally validated for their effectiveness. The experimental results show that the generalized regression neural network optimized by gray wolf optimizer has the best effect and the fastest optimization speed; the generalized regression neural network optimized by particle swarm optimization algorithm has the middle optimization speed; the generalized regression neural network optimized by fruit fly optimization algorithm has the worst effect and the slowest optimization speed.

show abstract

Risk prediction and risk factor analysis of urban logistics to public security based on PSO-GRNN algorithm

Cited by 9 publications

References 22 publications

Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review

Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review

Surface roughness prediction for turning based on the corrected subsection theoretical model

Yarn unevenness prediction using generalized regression neural network under various optimization algorithms

Contact Info

Product

Resources

About