{"id":"W4401830400","doi":"10.18280/ria.380420","title":"BioSwarmNet: A Revolutionary Approach to Brain Tumour Detection Using Fractional Order Differential Particle Swarm Optimisation and Recurrent Neural Networks","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Artificial neural network; Differential (mechanical device); Order (exchange); Computer science; Differential evolution; Swarm behaviour; Artificial intelligence; Machine learning; Engineering; Economics; Aerospace 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002713894,0.0001972827,0.0001445401,0.0001751445,0.0004028725,0.0001857127,0.0001343468,0.00009446059,0.0001098525],"category_scores_gemma":[0.000350825,0.0001999519,0.00007461504,0.001118884,0.0001095072,0.0003381662,0.00006844071,0.0003342375,0.00008626206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000149017,"about_ca_system_score_gemma":0.00003141681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000206534,"about_ca_topic_score_gemma":0.00000449395,"domain_scores_codex":[0.9980724,0.0001862097,0.0004104354,0.0007590878,0.0002405966,0.0003312954],"domain_scores_gemma":[0.9992067,0.0002577854,0.00008093422,0.0002204863,0.00006879131,0.0001653163],"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":[0.00008309399,0.0001927706,0.00002708409,0.00006104918,0.000007204859,0.000005408471,0.0006048658,0.3931212,0.5432154,0.002838557,0.0002078891,0.0596355],"study_design_scores_gemma":[0.00003437897,0.00007851674,0.0002023441,0.00003935941,0.00001374517,0.0001701637,0.0002318812,0.8446257,0.1530592,0.0002044276,0.001148925,0.000191326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3709581,0.0001691022,0.6261475,0.001087354,0.0009168124,0.0003405861,0.000005855731,0.0001870946,0.0001876339],"genre_scores_gemma":[0.9982291,0.00005352752,0.0005591171,0.000290849,0.0003590125,0.00007378302,0.000008443885,0.00003145189,0.0003946577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6272711,"threshold_uncertainty_score":0.8153802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07544404981148338,"score_gpt":0.2986452792471886,"score_spread":0.2232012294357052,"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."}}