183 citations · 387 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Optimal Uncertainty-guided Neural Network Training
H M Dipu Kabir, Abbas Khosravi, Abdollah Kavousi-Fard +2
The neural network (NN)-based direct uncertainty quantification (UQ) methods have achieved the state of the art performance since the first inauguration, known as the lower-upper-b…
Enhanced Multiobjective Evolutionary Algorithm based on Decomposition for Solving the Unit Commitment Problem
Anupam Trivedi, Kunal Pal, Chiranjib Saha +1
The unit commitment (UC) problem is a nonlinear, high-dimensional, highly constrained, mixed-integer power system optimization problem and is generally solved in the literature con…