{"id":"W3033212464","doi":"10.1016/j.procs.2020.04.165","title":"Comparative Analysis of Convolution Neural Network Models for Continuous Indian Sign Language Classification","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Science and Engineering Research Board","keywords":"Computer science; Classifier (UML); Convolutional neural network; Artificial intelligence; Sign language; Sign (mathematics); Pattern recognition (psychology); Speech recognition; Natural language processing; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001664025,0.0009155937,0.0006635351,0.000811197,0.0003525239,0.0008221113,0.0008471652,0.0008033641,0.001562262],"category_scores_gemma":[0.003382986,0.0001989633,0.00063121,0.0007089596,0.0002801773,0.001074534,0.0004562974,0.0008185816,0.0004188057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001413051,"about_ca_system_score_gemma":0.001133185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02576473,"about_ca_topic_score_gemma":0.01593443,"domain_scores_codex":[0.9995446,0.00009921585,0.00004928314,0.00008992096,0.0001271187,0.00008985623],"domain_scores_gemma":[0.9987835,0.00060602,0.00007386744,0.00008946106,0.0004002436,0.00004684236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001080237,0.0002597893,0.01008668,0.0002491281,0.0002549694,0.0001726803,0.00009358794,0.6492115,0.006859563,0.00336478,0.002514249,0.3258529],"study_design_scores_gemma":[0.000004235947,0.00006831974,0.001304455,0.00001264849,0.00002708503,0.00002064409,0.00001723422,0.9965239,0.001449333,0.0003308963,0.000233189,0.000008030574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7658236,0.00999727,0.2077339,0.001000316,0.0003401776,0.00009144647,0.0005744517,0.001823857,0.01261497],"genre_scores_gemma":[0.9719887,0.001384625,0.0225588,0.00007807034,0.00003107687,0.00004194728,0.0005790325,0.0000509194,0.003286728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02576473,"threshold_uncertainty_score":0.05122954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05605353777993627,"score_gpt":0.288091800585051,"score_spread":0.2320382628051147,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}