scholarly journals Effect of Eye Movement Desensitization and Reprocessing according to the AIP Model based on Seven Factors of Parenting Stress among Mothers of Deaf Children

2021 ◽  
Vol 29 (2) ◽  
pp. 64-74
Author(s):  
Forough Ahame ◽  
Maryam Gholamzadeh jofre ◽  
Amin Koraei ◽  
◽  
◽  
...  
Keyword(s):  
Parenting Stress ◽  
Eye Movement ◽  
Deaf Children ◽  
Model Based

scholarly journals Computational Eye Movement Model based on Adaptive Saliency Map

2010 ◽  
Vol 7 (15) ◽  
pp. 56-56
Author(s):  
I. Chakraborty ◽  
X. Zheng ◽  
S. Lin ◽  
R. Rauschenberger
Keyword(s):  
Eye Movement ◽  
Saliency Map ◽  
Movement Model ◽  
Model Based

A Model-Based Objective Evaluation of Eye Movement Correction in EEG Recordings

2006 ◽  
Vol 53 (2) ◽  
pp. 246-253 ◽  
Author(s):  
J.J.M. Kierkels ◽  
G.J.M. vanBoxtel ◽  
L.L.M. Vogten
Keyword(s):  
Eye Movement ◽  
Objective Evaluation ◽  
Model Based ◽  
Movement Correction ◽  
Eeg Recordings

Depression Classification Model Based on Emotionally Related Eye-Movement Data and Kernel Extreme Learning Machine

2020 ◽  
Vol 10 (11) ◽  
pp. 2668-2674
Author(s):  
Shengfu Lu ◽  
Sa Liu ◽  
Mi Li ◽  
Xin Shi ◽  
Richeng Li
Keyword(s):  
Eye Movement ◽  
Classification Model ◽  
Model Parameters ◽  
Local Optimum ◽  
Projection Model ◽  
Model Based ◽  
Movement Data ◽  
Mutation Strategy ◽  
Learning Machine ◽  
Searching Ability

The paper constructed a depression classification model based on emotionally related eye-movement data and kernel extreme learn machine (ELM). In order to improve the classification ability of the model, we use particle swarm optimization (PSO) to optimize the model parameters (regularization coefficient C and the parameter σ in the kernel function). At the same time, in order to avoid to be caught in the local optimum and improve PSO's searching ability, we use improved chaotic PSO optimization algorithm and Gauss mutation strategy to increase PSO's particle diversity. The classification results show that the accuracy, sensitivity and specificity of classification models without parameter optimization and Gauss mutation strategy are 80.23%, 80.31% and 79.43%, respectively, while those results of classification model using improved chaotic projection model and Gauss mutation strategy are improved to 88.55%, 87.71% and 89.42%, respectively. Compared with other classification methods of depression, the proposed classification method has better performance on depression recognition.

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