title: Deep Extraction (FBI Task Force)
author:  DiAnn Mills 
description: <b>4  Star Review, Top Pick</b><br />Few characters in Mills latest novel are who they appear to be at first glance, not even the good guys. Combined with intense action and stunning twists, this search for the truth keeps readers on the edges of their favorite reading chairs. Coles easygoing sense of humor and strong faith is the ideal balance for Toris more reticent personality, and the sparks their partnership generates adds a sweet touch of romance to the high-tension suspense. The crime is tightly plotted, and the message of faith is authentic and sincere. (Romantic Times)<br /><br />In Millss (<i>Deadly Encounter</i>) harrowing police procedural, a petroleum magnates wife is suspected of killing her husband by tampering with his pacemaker. FBI special agent Tory Templeton is brought in on the case, but its tough to be impartial as the wife in question is her best friend. Millss many fans will devour. (Library Journal)
zlib_text: Classification of COVID-19 in chest X-ray images using
  DeTraC deep convolutional neural network
  Asmaa Abbas1, Mohammed M. Abdelsamea1,2*, Mohamed Medhat Gaber2
  1 Mathematics Department, Faculty of Science, Assiut University, Assiut, Egypt
  2 School of Computing and Digital Technology, Birmingham City University,
  Birmingham, UK
  * mohammed.abdelsamea@bcu.ac.uk
  Abstract
  Chest X-ray is the first imaging technique that plays an important role in the diagnosis
  of COVID-19 disease. Due to the high availability of large-scale annotated image
  datasets, great success has been achieved using convolutional neural networks (CNN s)
  for image recognition and classification. However, due to the limited availability of
  annotated medical images, the classification of medical images remains the biggest
  challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that
  can provide a promising solution by transferring knowledge from generic object
  recognition tasks to domain-specific t