{"id":"W4200240718","doi":"10.1155/2021/9023010","title":"Intelligent Malaysian Sign Language Translation System Using Convolutional‐Based Attention Module with Residual Network","year":2021,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centennial College","funders":"Research Institute of Electrical Communication, Tohoku University; Universiti Malaysia Sarawak; King Saud University","keywords":"Computer science; Residual; Classifier (UML); Confusion; Convolutional neural network; Sign language; Confusion matrix; Artificial intelligence; Speech recognition; Residual neural network; Algorithm; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003591374,0.0001849025,0.0001883245,0.0001250634,0.0003788391,0.0003289338,0.0003176926,0.00005751119,0.000005943819],"category_scores_gemma":[0.00003132469,0.0001695075,0.00004696727,0.00103507,0.0001595679,0.0005559021,0.00007097868,0.0001390301,0.00001591356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005972521,"about_ca_system_score_gemma":0.000240953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001175058,"about_ca_topic_score_gemma":0.00000843905,"domain_scores_codex":[0.9977938,0.0002229396,0.0003682133,0.0006917819,0.0006148082,0.0003084535],"domain_scores_gemma":[0.99888,0.000249077,0.0001547693,0.0002405979,0.0003356687,0.0001398981],"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":[0.00001199716,0.00005163882,0.0009227897,0.00005646014,0.000005889578,0.0000975563,0.0002266092,0.9686431,0.002203644,0.01920767,0.00001736516,0.00855525],"study_design_scores_gemma":[0.0001186989,0.00009033855,0.003659849,0.0002038664,0.00001125821,0.0004199115,0.0002199051,0.9892817,0.004870574,0.0008030775,0.00009394902,0.0002268428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03459967,0.0004169529,0.963703,0.0003690602,0.0004079047,0.0002089179,0.000009020233,0.0001211649,0.0001643195],"genre_scores_gemma":[0.9447721,0.000009091375,0.05464598,0.0003829662,0.0001080616,0.00001308756,0.00002014052,0.000009757654,0.00003878666],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9101725,"threshold_uncertainty_score":0.6912317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06583588349893778,"score_gpt":0.2880973675296436,"score_spread":0.2222614840307058,"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."}}