{"id":"W3155879942","doi":"10.1088/1757-899x/1131/1/012007","title":"Impact of Hidden Dense Layers in Convolutional Neural Network to enhance Performance of Classification Model","year":2021,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Convolutional neural network; Computer science; Deep learning; Artificial intelligence; Convolution (computer science); Artificial neural network; Machine learning; Layer (electronics); Pattern recognition (psychology); Materials science","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":[0.0007949444,0.0001143818,0.0002757239,0.000112581,0.000168899,0.00001786526,0.000182293,0.00007751936,0.00008784715],"category_scores_gemma":[0.0004354184,0.0001091987,0.00001689228,0.0005624836,0.0001827946,0.0004481965,0.0001275703,0.0001320118,0.000005670175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001499529,"about_ca_system_score_gemma":0.001209322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003559575,"about_ca_topic_score_gemma":0.000161021,"domain_scores_codex":[0.998405,0.00005449816,0.0005796303,0.0002545243,0.0002587115,0.0004475731],"domain_scores_gemma":[0.9986751,0.00008562235,0.0001435579,0.0002066697,0.0007750123,0.0001140409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007731373,0.000008308517,0.02032488,0.0002236822,0.000002338654,8.803185e-7,0.00192424,0.06416114,0.909881,0.00281748,0.00001130173,0.0005674171],"study_design_scores_gemma":[0.00006334982,0.0001710613,0.3203828,0.0006851,0.000004371964,0.000004244123,0.001373618,0.3609053,0.3159809,0.0002201272,0.000008684684,0.0002005093],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986823,0.00003788306,0.0003224722,0.0002037424,0.0003966784,0.0002338928,0.00002724001,0.00001937046,0.00007642324],"genre_scores_gemma":[0.9980787,0.00009674129,0.001645895,0.00003482538,0.00005660902,0.00004173159,0.000004351875,0.00000807118,0.00003306355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5939001,"threshold_uncertainty_score":0.4452994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002050373235697,"score_gpt":0.4028112147850816,"score_spread":0.3026061774615119,"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."}}