Training and testing accuracy of the proposed stack and proposed fft methods are compared with conventional DL models.

<p>(a) CNN model value accuracy during test and train (b) LSTM model value accuracy during test and train. (c) Bi-LSTM model value accuracy during test and train. (d) Stack method value accuracy during test and train. (e) fft method value accuracy during test and train.</p>

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Jamila Akhter (21144252) (author)
مؤلفون آخرون:	Hammad Nazeer (21144255) (author), Noman Naseer (6909227) (author), Rehan Naeem (11851347) (author), Karam Dad Kallu (13429833) (author), Jiye Lee (1346235) (author), Seong Young Ko (21144258) (author)
منشور في:	2025
الموضوعات:	Cell Biology Neuroscience Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified twenty participants engaged thereby enabling individuals systems recognize patterns generate control commands convolutional neural networks gripping motor activity performed using nirslab stack method classifies classification accuracies respectively 16 %), lstm motor disabilities 46 %), features using classification using stack model classification stack xlink "> term memory significantly higher results show regain autonomy proposed stack one another lstm ), infrared spectroscopy improved performance functional near fft methods features extraction feature extraction extract features effectively used deep learning computer interface classification performance class problems class hand brain signals bidirectional long based brain
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Training and testing accuracy of the proposed stack and proposed fft methods are compared with conventional DL models.

مواد مشابهة