Parham M. Kebria

TL;DR: A comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis and the impacts of various factors, such as dataset characteristics, sample size, features, and the stenosis of each coronary artery are investigated in detail.

TL;DR: This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance, and proposes a new ensemble approach to calculate and update weights for the models regarding their mean squared error values.

TL;DR: The Simmechanics model is developed based on these models to provide high quality visualisation of this robot for simulation of it in Matlab environment and to demonstrate the accuracy of the developed mathematical models.

TL;DR: This paper reviews the recent control methodologies used for teleoperation systems with model uncertainty, unknown time-varying delay, and Internet-based communication and focuses on control algorithms that are suitable for nonlinear uncertain systems to decrease restrictions and increase application scope.

TL;DR: Wang et al. as discussed by the authors proposed a deep uncertainty-aware transfer learning framework for COVID-19 detection using medical images, and four popular convolutional neural networks (CNNs), including VGG16, ResNet50, DenseNet121, and InceptionResNetV2, were first applied to extract deep features from chest X-ray and computed tomography (CT) images.