Latent Semantic Index Based Feature Reduction for Enhanced Severity Prediction of Road Accidents

被引：0

作者：

Jaglan, Saurabh ^{[1
]}

Kumari, Sunita ^{[1
]}

Aggarwal, Praveen ^{[2
]}

机构：

[1] Deenbandhu Chhotu Ram Univ Sci & Technol, Civil Engn Dept, Murthal 131039, Haryana, India

[2] Natl Inst Technol, Civil Engn Dept, Kurukshetra 136119, Haryana, India

来源：

OPTICAL MEMORY AND NEURAL NETWORKS | 2024年 / 33卷 / 02期

关键词：

adaptive data cleaning; min-max normalization; Pearson correlation coefficient; ANN;

D O I：

10.3103/S1060992X24700103

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Traditional approaches do not have the capability to analyse the road accident severity with different road characteristics, area and type of injury. Hence, the road accident severity prediction model with variable factors is designed using the ANN algorithm. In this designed model, the past accident records with road characteristics are obtained and pre-processed utilizing adaptive data cleaning as well as the min-max normalization technique. These techniques are used to remove and separate the collected data according to their relation. The Pearson correlation coefficient is utilized to separate the features from the pre-processed data. The ANN algorithm is used to train and validate these retrieved features. The proposed model's performance values are 99, 98, 99 and 98% for accuracy, precision, specificity and recall. Thus, the resultant values of the designed road accident severity prediction model with variable factors using the ANN algorithm perform better compared to the existing techniques.

引用

页码：221 / 235

页数：15

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