{"id":"W4402307001","doi":"10.18280/ts.410415","title":"Optimized Hybrid Convolution Neural Network with Machine Learning for Arabic Sign Language Recognition","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Arabic; Convolution (computer science); Convolutional neural network; Artificial intelligence; Sign (mathematics); Artificial neural network; Sign language; Speech recognition; Natural language processing; Pattern recognition (psychology); Linguistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004274388,0.0004404215,0.0005774766,0.0003035108,0.0002863756,0.0004794585,0.0007232401,0.0006783126,0.00316177],"category_scores_gemma":[0.0006011434,0.0002275271,0.0003891543,0.0003710195,0.0001873126,0.0006101022,0.0005094932,0.000512336,0.0007729406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004921509,"about_ca_system_score_gemma":0.001080621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007313363,"about_ca_topic_score_gemma":0.009006374,"domain_scores_codex":[0.9997676,0.0000368339,0.0000162259,0.00004519018,0.00008538189,0.00004885114],"domain_scores_gemma":[0.999791,0.00005352449,0.00001344603,0.00002547513,0.0001018695,0.00001464793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004871527,0.000182124,0.001452278,0.00009494636,0.0001289131,0.0001173135,0.00003164548,0.369597,0.05771343,0.003742991,0.003291401,0.5631608],"study_design_scores_gemma":[0.00000464403,0.00002885735,0.0002504165,0.000002446887,0.000009600417,0.0000222389,0.000002602241,0.9934065,0.005610675,0.0002557904,0.0004023236,0.000003948911],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09445752,0.0008832022,0.8979757,0.0001729896,0.0001612098,0.00004960066,0.0001016314,0.001702602,0.004495608],"genre_scores_gemma":[0.7438918,0.0002559747,0.2460177,0.0001282272,0.00005158997,0.00009599729,0.0002794265,0.0001054577,0.009173725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007313363,"threshold_uncertainty_score":0.01454163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01959805431270501,"score_gpt":0.2354079803135141,"score_spread":0.2158099260008091,"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."}}