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: Bovine renal brush-border membrane vesicle aminopeptidase N at various stages of purity was treated with two bifunctional cross-linking agents. A pattern of emergence of higher molecular weight forms was observed. By using a cleavable cross-linker, aminopeptidase N was shown to cross-link both to itself and to its breakdown products as well as to dipeptidyl peptidase IV. Using this technique it was possible to identify three of the breakdown products as 45 kDa, 66 kDa and 95 kDa peptides. N-terminal amino acid sequence analysis was used to define the precise cleavage points for the bovine renal aminopeptidase N breakdown products. The short amino acid sequences obtained show strong sequence similarity with the human intestinal and rat kidney aminopeptidase N. 
 Title: The oligomeric structure of renal aminopeptidase N from bovine brush-border membrane vesicles
Example Output: False

Example Input: Paragraph: A number of sentences in the first paragraph of the introduction of [28] were copied verbatim from [21, 22, 25, 29] . Although [21, 22, 25] were cited in the text, [29] was omitted and it was not made sufficiently clear that direct quotations were used. The authors wish to apologize to the authors of [21, 22, 25, 29] and to the readers of the journal for any inconvenience. The authors wish to replace the introduction of [28] with the following: OPEN ACCESS 
 Title: Correction: Xie, H.; et al. 3D QSAR Studies, Pharmacophore Modeling and Virtual Screening on a Series of Steroidal Aromatase
Example Output:
True