Enhancing COVID-19 Detection: An Xception-Based Model with Advanced Transfer Learning from X-ray Thorax Images.

Rapid and precise identification of Coronavirus Disease 2019 (COVID-19) is pivotal for effective patient care, comprehending the pandemic's trajectory, and enhancing long-term patient survival rates. Despite numerous recent endeavors in medical imaging, many convolutional neural network-based models grapple with the expressiveness problem and overfitting, and the training process of these models is always resource-intensive. This paper presents an innovative approach employing Xception, augmented with cutting-edge transfer learning techniques to forecast COVID-19 from X-ray thorax images.

Seismic Waveform Inversion Capability on Resource-Constrained Edge Devices.

Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it's use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities.