Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine

This study proposes an effective intelligent predictive model for prediction of cervical hyperextension injury. The prediction model is constructed by combing an improved sine cosine algorithm (SCA) with support vector machines (SVM), which is named COSCA-SVM. The core of the developed model is the...

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Bibliographic Details
Main Authors: Guomin Liu, Wenyuan Jia, Mingjing Wang, Ali Asghar Heidari, Huiling Chen, Yungang Luo, Chengye Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Support vector machine
sine cosine algorithm
covariance
cervical hyperextension injury
opposition-based learning
Online Access:https://ieeexplore.ieee.org/document/9022895/

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