S
Sebelan Danishvar
Researcher at Brunel University London
Publications - 23
Citations - 115
Sebelan Danishvar is an academic researcher from Brunel University London. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 2, co-authored 6 publications receiving 8 citations. Previous affiliations of Sebelan Danishvar include University of Tabriz.
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Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG
TL;DR: A deep learning model using a convolutional neural network that have had significant performance in image processing fields is developed to develop an assistive tool for physicians to recognize ADHD children from healthy children using electroencephalography based on a continuous mental task.
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Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network
Sobhan Sheykhivand,Tohid Yousefi Rezaii,Zohreh Mousavi,Saeed Meshgini,S. Makouei,Ali Farzamnia,Sebelan Danishvar,Kenneth Teo Tze Kin +7 more
TL;DR: The study’s findings reveal that the suggested deep CNN–LSTM network could learn features hierarchically from raw EEG data and attain a greater precision rate than previous comparative approaches for two-stage driver fatigue categorization.
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Automatically Identified EEG Signals of Movement Intention Based on CNN Network (End-To-End)
Nahal Shahini,Zeinab Bahrami,Sobhan Sheykhivand,Saba Marandi,Morad Danishvar,Sebelan Danishvar,Yousef Roosta +6 more
TL;DR: This research introduces a novel method for automatically categorizing two-class and three-class movement-intention situations utilizing EEG data by applied directly to a convolutional neural network (CNN) without feature extraction or selection.
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An Improved Capsule Network (WaferCaps) for Wafer Bin Map Classification Based on DCGAN Data Upsampling
TL;DR: In this article , a deep learning approach based on deep convolutional generative adversarial network (DCGAN) and a new Capsule Network (WaferCaps) was proposed to upsample the original dataset and therefore increase the data used for training and balance the classes at the same time.
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A Customized Efficient Deep Learning Model for the Diagnosis of Acute Leukemia Cells Based on Lymphocyte and Monocyte Images
Sanam Ansari,Ahmad Habibizad Navin,Amin Babazadeh Sangar,Jalil Vaez Gharamaleki,Sebelan Danishvar +4 more
TL;DR: In this article , a deep learning model with a customized architecture for detecting acute leukemia using images of lymphocytes and monocytes was designed, which achieved a 99% accuracy rate in diagnosing acute leukemia types, including ALL and AML.