Výsledky vyhledávání - "Qazani, Mohammad Reza Chalak"

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Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model

Autor: Khan, Mehshan Ahmed, Asadi, Houshyar, Qazani, Mohammad Reza Chalak, Arogbonlo, Adetokunbo, Nahavandi, Saeid, Lim, Chee Peng

One debatable issue in traffic safety research is that cognitive load from sec-ondary tasks reduces primary task performance, such as driving. Although physiological signals have been extensively used in driving-related research to assess cognitive l

Externí odkaz: http://arxiv.org/abs/2408.06350

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Functional near-infrared spectroscopy (fNIRS) and Eye tracking for Cognitive Load classification in a Driving Simulator Using Deep Learning

Autor: Khan, Mehshan Ahmed, Asadi, Houshyar, Qazani, Mohammad Reza Chalak, Lim, Chee Peng, Nahavandi, Saied

Motion simulators allow researchers to safely investigate the interaction of drivers with a vehicle. However, many studies that use driving simulator data to predict cognitive load only employ two levels of workload, leaving a gap in research on empl

Externí odkaz: http://arxiv.org/abs/2408.06349

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Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis

Autor: Khan, Mehshan Ahmed, Asadi, Houshyar, Qazani, Mohammad Reza Chalak, Arogbonlo, Adetokunbo, Pedrammehr, Siamak, Anwar, Adnan, Bhatti, Asim, Nahavandi, Saeid, Lim, Chee Peng

Functional near-infrared spectroscopy (fNIRS) is employed as a non-invasive method to monitor functional brain activation by capturing changes in the concentrations of oxygenated haemoglobin (HbO) and deoxygenated haemo-globin (HbR). Various machine

Externí odkaz: http://arxiv.org/abs/2407.15901

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Uncertainty Aware Neural Network from Similarity and Sensitivity

Autor: Kabir, H M Dipu, Mondal, Subrota Kumar, Khanam, Sadia, Khosravi, Abbas, Rahman, Shafin, Qazani, Mohammad Reza Chalak, Alizadehsani, Roohallah, Asadi, Houshyar, Mohamed, Shady, Nahavandi, Saeid, Acharya, U Rajendra

Publikováno v: Applied Soft Computing, 2023

Researchers have proposed several approaches for neural network (NN) based uncertainty quantification (UQ). However, most of the approaches are developed considering strong assumptions. Uncertainty quantification algorithms often perform poorly in an

Externí odkaz: http://arxiv.org/abs/2304.14925

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Akademický článek

Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization.

Autor: Moayyedian, Mehdi¹ (AUTHOR), Qazani, Mohammad Reza Chalak² (AUTHOR), Amirkhizi, Parisa Jourabchi³ (AUTHOR), Asadi, Houshyar⁴ (AUTHOR), Hedayati-Dezfooli, Mohsen⁵ (AUTHOR) mohsen.hedayati@udst.edu.qa

Publikováno v: Scientific Reports. 10/10/2024, Vol. 14 Issue 1, p1-15. 15p.

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CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis

Autor: Khanam, Sadia, Qazani, Mohammad Reza Chalak, Mondal, Subrota Kumar, Kabir, H M Dipu, Sabyasachi, Abadhan S., Asadi, Houshyar, Kumar, Keshav, Tabarsinezhad, Farzin, Mohamed, Shady, Khorsavi, Abbas, Nahavandi, Saeid

This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very

Externí odkaz: http://arxiv.org/abs/2209.09556

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