{"id":"W4320925818","doi":"10.18280/rces.090402","title":"PYNQ Framework Based Object Recognition Implementation Using Convolution Neural Network (CNN)","year":2022,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Convolution (computer science); Object (grammar); Artificial intelligence; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Artificial neural network","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":[0.0003410254,0.0005175676,0.0003806642,0.000524071,0.0002125099,0.0006304053,0.001199583,0.0004035958,0.005689159],"category_scores_gemma":[0.0005124938,0.0002754797,0.0004802123,0.0004434501,0.0002405633,0.0009429542,0.0005013281,0.0004704686,0.001507317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006945828,"about_ca_system_score_gemma":0.0007399551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005600296,"about_ca_topic_score_gemma":0.004863049,"domain_scores_codex":[0.9997297,0.00001881893,0.00001784012,0.00006967744,0.0001223918,0.00004156871],"domain_scores_gemma":[0.9998307,0.0000243919,0.00001343699,0.0000294025,0.00009103057,0.00001095906],"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":[0.0005287526,0.0001130792,0.003919369,0.0008356939,0.0001769369,0.0006278554,0.0001547519,0.05673176,0.1483522,0.01814938,0.02039283,0.7500174],"study_design_scores_gemma":[0.00008340691,0.0004032466,0.004997984,0.0000875052,0.0001164716,0.001348453,0.00006264316,0.7493931,0.1734435,0.005116753,0.06486296,0.00008398776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02307823,0.001099845,0.9588692,0.0001383369,0.0001332777,0.0001273433,0.0003254272,0.009462606,0.006765674],"genre_scores_gemma":[0.3598008,0.001514781,0.6241799,0.0003307833,0.00006426388,0.0003186863,0.001667125,0.0005878419,0.01153593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005689159,"threshold_uncertainty_score":0.01903218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08580690696831425,"score_gpt":0.3393710628205067,"score_spread":0.2535641558521924,"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."}}