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PMC:7796058 / 53669-54536 JSONTXT

Annnotations TAB JSON ListView MergeView

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
395 431-437 Species denotes people Tax:9606
396 552-558 Species denotes people Tax:9606
397 25-33 Disease denotes coughing MESH:D003371

LitCovid-PD-HP

Id Subject Object Predicate Lexical cue hp_id
T7 25-33 Phenotype denotes coughing http://purl.obolibrary.org/obo/HP_0012735

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T385 0-210 Sentence denotes In total, 603 images for coughing, 634 images for hugging, 608 images for handshaking, and 623 images for door touching were used from COCO dataset [62] for transfer learning for the pre-trained model (YOLOv3).
T386 211-289 Sentence denotes These images were taken from free sources found through Google image searches.
T387 290-349 Sentence denotes For labelling objects, a semi-automatic method was applied.
T388 350-393 Sentence denotes Darknet library was also used for training.
T389 394-586 Sentence denotes For individual behaviors, all of the people in images were detected and labelled in a text file whilst the algorithm aggregated intersected bounding boxes of people into a single bounding box.
T390 587-694 Sentence denotes As wrong labels might be generated, the images should be manually checked to correct misclassified objects.
T391 695-788 Sentence denotes For this step 80 percent of the images were selected for training and 20 percent for testing.
T392 789-867 Sentence denotes To increase the accuracy of this model, the configuration in Table 3 was used.