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".

Example Input: 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
Example Output: False

Example Input: Paragraph: Background: Infrared thermography (IRT) is used to screen febrile passengers at international airports, but it suffers from low sensitivity. This study explored the application of a combined visible and thermal image processing approach that uses a CMOS camera equipped with IRT to remotely sense multiple vital signs and screen patients with suspected infectious diseases. Methods: An IRT system that produced visible and thermal images was used for image acquisition. The subjects' respiration rates were measured by monitoring temperature changes around the nasal areas on thermal images; facial skin temperatures were measured simultaneously. Facial blood circulation causes tiny color changes in visible facial images that enable the determination of the heart rate. A logistic regression discriminant function predicted the likelihood of infection within 10 s, based on the measured vital signs. Sixteen patients with an influenza-like illness and 22 control subjects participated in a clinical test at a clinic in Fukushima, Japan. Results: The vital-sign-based IRT screening system had a sensitivity of 87.5% and a negative predictive value of 91.7%; these values are higher than those of conventional fever-based screening approaches. Conclusions: Multiple vital-sign-based screening efficiently detected patients with suspected infectious diseases. It offers a promising alternative to conventional fever-based screening. 
 Title: Remote sensing of multiple vital signs using a CMOS camera-equipped infrared thermography system and its clinical application in rapidly screening patients with suspected infectious diseases
Example Output: True

Example Input: Paragraph: Picornaviruses form replication complexes in association with membranes in structures called replication organelles. Common themes to emerge from studies of picornavirus replication are the need for cholesterol and phosphatidylinositol 4-phosphate (PI4P). In infected cells, type III phosphatidylinositol 4-kinases (PI4KIIIs) generate elevated levels of PI4P, which is then exchanged for cholesterol at replication organelles. For the enteroviruses, replication organelles form at Golgi membranes in a process that utilizes PI4KIIIb. Other picornaviruses, for example the cardioviruses, are believed to initiate replication at the endoplasmic reticulum and subvert PI4KIIIa to generate PI4P. Here we investigated the role of PI4KIII in foot-and-mouth disease virus (FMDV) replication. Our results showed that, in contrast to the enteroviruses and the cardioviruses, FMDV replication does not require PI4KIII (PI4KIIIa and PI4KIIIb), and PI4P levels do not increase in FMDV-infected cells and PI4P is not seen at replication organelles. These results point to a unique requirement towards lipids at the FMDV replication membranes. 
 Title: Foot-and-mouth disease virus replicates independently of phosphatidylinositol 4-phosphate and type III phosphatidylinositol 4-kinases
Example Output:
False