{"id":"W3186960229","doi":"10.18280/ts.380336","title":"Extraction and Classification of Image Features for Fire Recognition Based on Convolutional Neural Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Fire detection; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Image (mathematics); Artificial neural network; Process (computing); Contextual image classification; Set (abstract data type); Computer vision; Engineering","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":[0.0003185749,0.0007294341,0.0004325587,0.001013799,0.0002372809,0.0004288541,0.0005480768,0.0005081464,0.0007700007],"category_scores_gemma":[0.000553968,0.0002895501,0.0006368121,0.0006801692,0.000263553,0.0006789015,0.0003399422,0.000489219,0.0002958683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006001925,"about_ca_system_score_gemma":0.0005725409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0108512,"about_ca_topic_score_gemma":0.01090438,"domain_scores_codex":[0.9997904,0.00001400797,0.00001525605,0.0000570383,0.00007074897,0.00005251899],"domain_scores_gemma":[0.9998542,0.00002860829,0.00002451814,0.00002222971,0.00006049881,0.000009875963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003346125,0.0002976476,0.008233041,0.0001440037,0.0001274363,0.0002281114,0.00006960707,0.1068873,0.1578297,0.00245585,0.002158845,0.7212338],"study_design_scores_gemma":[0.000007924968,0.00008416578,0.005942574,0.00001255485,0.00005029681,0.0001016431,0.00001596116,0.9337475,0.05815183,0.0006485863,0.001219809,0.00001711648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1810478,0.0007652922,0.8126913,0.0001513704,0.0001152738,0.0001468683,0.0002525739,0.001813242,0.003016329],"genre_scores_gemma":[0.7955477,0.0007153355,0.1977938,0.00008895437,0.00004387577,0.0001014815,0.0007102322,0.00006233947,0.00493621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0108512,"threshold_uncertainty_score":0.02157611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02181327368013957,"score_gpt":0.2319711489677373,"score_spread":0.2101578752875977,"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."}}