WITHDRAWN: An advanced machine learning technique for the analysis of retina fundus images

Bandyopadhyay, Abhijit; Shankar, D. Ravi; Tigadi, Arun; Jamshed, Aatif

doi:10.1016/j.matpr.2020.10.278

Cited by 3 publications

(3 citation statements)

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“…This training algorithm is comprised of the following steps: Initialize the model weights with some starting values. Specifically, the initial learning rate of 0.0001 is adopted, and the Kaiming initialization method is used to initialize the network 25 . For each training iteration: Calculate the gradient of the loss function with respect to the model weights.…”

Section: Methodsmentioning

confidence: 99%

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Air traffic control forgetting prediction based on eye movement information and hybrid neural network

Jin

Gao

et al. 2023

Sci Rep

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Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.

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Section: Methodsmentioning

confidence: 99%

“…Initialize the model weights with some starting values. Specifically, the initial learning rate of 0.0001 is adopted, and the Kaiming initialization method is used to initialize the network 25 .…”

Section: Methodsmentioning

confidence: 99%

Air traffic control forgetting prediction based on eye movement information and hybrid neural network

Jin

Gao

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The loss function is: where y represents the category probability predicted by the model, class represents the correct category, ] [i y represents the predicted probability of the i-th category. In this paper, Adam is used as the optimizer when training the model, the initial learning rate is 0.0001 and the Kaiming initialization method is used to initialize the network [23].…”

Section: Final Outputmentioning

confidence: 99%

Air traffic control forgetting prediction based on eye movement information and hybrid neural network

Jin

Gao

et al. 2022

Preprint

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Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting to eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that this method, using eye movement data, can provide control forgetting prediction with remarkably high accuracy. This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.

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An Analysis of Sequential Pattern Mining Approach for Progressive Database by Deep Learning Technique

Jamshed

Mallick²,

Bharti³

2022

2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)

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WITHDRAWN: An advanced machine learning technique for the analysis of retina fundus images

Cited by 3 publications

References 1 publication

Air traffic control forgetting prediction based on eye movement information and hybrid neural network

Air traffic control forgetting prediction based on eye movement information and hybrid neural network

Air traffic control forgetting prediction based on eye movement information and hybrid neural network

An Analysis of Sequential Pattern Mining Approach for Progressive Database by Deep Learning Technique

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