{"id":"W4212958975","doi":"10.1109/access.2022.3151660","title":"Convolution Optimization in Fire Classification","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Concordia University","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Convolution (computer science); Block (permutation group theory); Artificial intelligence; Deep learning; Computer engineering; FLOPS; Computation; Residual; Machine learning; Algorithm; Parallel computing; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006175293,0.0005789216,0.0007909833,0.0004481018,0.0002777838,0.0007874648,0.0007499694,0.000979914,0.002677795],"category_scores_gemma":[0.001401053,0.0003581711,0.0007048714,0.0005185434,0.0005084913,0.001017071,0.000615843,0.0009949058,0.0006460989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009316119,"about_ca_system_score_gemma":0.0008994181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008127646,"about_ca_topic_score_gemma":0.007707491,"domain_scores_codex":[0.9997948,0.00003754318,0.00001224734,0.00005612529,0.00004329331,0.00005607751],"domain_scores_gemma":[0.9997625,0.000105023,0.00002521811,0.00003033695,0.00005865233,0.00001827452],"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":[0.0002227803,0.00005559086,0.001838029,0.00007838955,0.00007070972,0.00007480582,0.00002862814,0.8119663,0.005383909,0.009154106,0.003472917,0.1676539],"study_design_scores_gemma":[0.000002512397,0.000006562338,0.0001675621,0.000002863012,0.00000421598,0.00001128128,0.000002293356,0.9972425,0.0006987371,0.001566409,0.0002933239,0.000001904972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09588823,0.002082681,0.8913389,0.0008652401,0.0001884531,0.00004041727,0.0003108172,0.001807379,0.007477896],"genre_scores_gemma":[0.8639945,0.0009744347,0.1235165,0.0003815144,0.0001442687,0.00006377076,0.0007262037,0.0002056081,0.009993271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008127646,"threshold_uncertainty_score":0.01616073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834709714939807,"score_gpt":0.2493525475481674,"score_spread":0.2210054503987694,"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."}}