2.7k citations · 7k across the 44 of their papers we have counts for
45 papers
Comparison Study of Inertial Sensor Signal Combination for Human Activity Recognition based on Convolutional Neural Networks
Farhad Nazari, Navid Mohajer, Darius Nahavandi +2
Human Activity Recognition (HAR) is one of the essential building blocks of so many applications like security, monitoring, the internet of things and human-robot interaction. The…
Controlled Dropout for Uncertainty Estimation
Mehedi Hasan, Abbas Khosravi, Ibrahim Hossain +2
Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art per…
Applied Exoskeleton Technology: A Comprehensive Review of Physical and Cognitive Human-Robot Interaction
Farhad Nazari, Navid Mohajer, Darius Nahavandi +2
Exoskeletons and orthoses are wearable mobile systems providing mechanical benefits to the users. Despite significant improvements in the last decades, the technology is not fully…
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification
Feras Albardi, H M Dipu Kabir, Md Mahbub Islam Bhuiyan +3
This study aims to explore different pre-trained models offered in the Torchvision package which is available in the PyTorch library. And investigate their effectiveness on fine-gr…
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors
Mohamad Roshanzamir, Roohallah Alizadehsani, Mahdi Roshanzamir +4
One of the most common problems encountered in human-computer interaction is automatic facial expression recognition. Although it is easy for human observer to recognize facial exp…
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
Afshin Shoeibi, Delaram Sadeghi, Parisa Moridian +8
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack o…
MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
Zakaria Senousy, Mohammed M. Abdelsamea, Mohamed Medhat Gaber +4
Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonst…
Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning
Maryam Habibpour, Hassan Gharoun, Mohammadreza Mehdipour +7
Countless research works of deep neural networks (DNNs) in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwant…
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products
Maryam Habibpour, Hassan Gharoun, AmirReza Tajally +4
Defects are unavoidable in casting production owing to the complexity of the casting process. While conventional human-visual inspection of casting products is slow and unproductiv…
Confidence Aware Neural Networks for Skin Cancer Detection
Donya Khaledyan, AmirReza Tajally, Ali Sarkhosh +4
Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities. However, Deep Neural Networks (DNNs) requir…
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
Afshin Shoeibi, Parisa Moridian, Marjane Khodatars +9
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements.…
Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari +9
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. I…
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods
Nooshin Ayoobi, Danial Sharifrazi, Roohallah Alizadehsani +9
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs…
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients
Fahime Khozeimeh, Danial Sharifrazi, Navid Hoseini Izadi +10
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classificati…
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Delaram Sadeghi, Afshin Shoeibi, Navid Ghassemi +9
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, pe…
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
Danial Sharifrazi, Roohallah Alizadehsani, Mohamad Roshanzamir +13
The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to pre…
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
Roohallah Alizadehsani, Danial Sharifrazi, Navid Hoseini Izadi +11
The new coronavirus has caused more than one million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe.…
Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection
Hamzeh Asgharnezhad, Afshar Shamsi, Roohallah Alizadehsani +4
Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strateg…
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain +9
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of r…
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
Roohallah Alizadehsani, Mohamad Roshanzamir, Sadiq Hussain +12
Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread applic…
Development of novel algorithm to visualize blood vessels on 3D ultrasound images during liver surgery
Fatemeh Salehihafshejani, Alireza Ahmadian, Afshin Shoeibi +5
Volume visualization is a method that displays three-dimensional (3D) data in two-dimensional (2D) space. Using 3D datasets instead of 2D traditional images improves the visualizat…
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis
Afshar Shamsi Jokandan, Hamzeh Asgharnezhad, Shirin Shamsi Jokandan +5
The early and reliable detection of COVID-19 infected patients is essential to prevent and limit its outbreak. The PCR tests for COVID-19 detection are not available in many countr…
SpinalNet: Deep Neural Network with Gradual Input
H M Dipu Kabir, Moloud Abdar, Seyed Mohammad Jafar Jalali +4
Deep neural networks (DNNs) have achieved the state of the art performance in numerous fields. However, DNNs need high computation times, and people always expect better performanc…
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Marjane Khodatars, Afshin Shoeibi, Delaram Sadeghi +12
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques…