In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

[EX Q]: Paragraph: Several French regions where COVID19 has been reported currently show a renewed increase in ILI cases in the general practice based Sentinelles network. Here we computed the number of excess cases by region and found correlation with the number of reported COVID19 cases so far. These data suggest larger circulation of SARS-CoV-2 in the French population than apparent from confirmed cases. 
 Title: Excess cases of Influenza like illnesses in France synchronous with COVID19 invasion. Pierre-Yves Boëlle 1 and the Sentinelles syndromic and viral surveillance group
[EX A]: True

[EX Q]: Paragraph: Purpose: COVID-19 has become global threaten. CT acts as an important method of 60 diagnosis. However, human-based interpretation of CT imaging is time consuming. 61 More than that, substantial inter-observer-variation cannot be ignored. We aim at 62 developing a diagnostic tool for artificial intelligence (AI)-based classification of CT 63 images for recognizing COVID-19 and other common infectious diseases of the lung. 64 Experimental Design: In this study, images were retrospectively collected and 65 prospectively analyzed using machine learning. CT scan images of the lung that show 66 or do not show COVID-19 were used to train and validate a classification framework 67 based on convolutional neural network. Five conditions including COVID-19 68 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary 69 tuberculosis, and normal lung were evaluated. Training and validation set of images 70 were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were 71 collected from Zhongshan Hospital Xiamen University and the fifth Hospital of 72 Wuhan. 73 Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For 74 test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, 75 non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis 76 were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, 77 accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 78 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. 79 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 
 Title: Deep Learning-Based Recognizing COVID-19 Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT 2 Scan Images
[EX A]: False

[EX Q]: Paragraph: The transmission modes of SARS-coronavirus appear to be through droplet spread, close contact and fomites although air borne transmission has not been ruled out. This clearly places dental personnel at risks as they work in close proximity to their patients employing droplet and aerosol generating procedures. Although the principle of universal precautions is widely advocated and followed throughout the dental community, additional precautionary measures -termed standard precaution may be necessary to help control the spread of this highly contagious disease. Patient assessment should include questions on recent travel to SARS infected areas and, contacts of patients, fever and symptoms of respiratory infections. Special management protocols and modified measures that regulate droplet and aerosol contamination in a dental setting have to be introduced and may include the reduction or avoidance of droplet/aerosol generation, the disinfection of the treatment field, application of rubber dam, pre-procedural antiseptic mouthrinse and the dilution and efficient removal of contaminated ambient air. The gag, cough or vomiting reflexes that lead to the generation of aerosols should also be prevented. 
 Title: PRACTICE Severe Acute Respiratory Syndrome (SARS) and the GDP. Part II: Implications for GDPs
[EX A]:
False