{"id":"W4388479292","doi":"10.18280/ria.370508","title":"COVID-19 Diagnosis Using Chaotic Logistic Map Based Modified Whale Optimization: A Robust Feature and Parameter Selection Approach","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Feature selection; Chaotic; Selection (genetic algorithm); Whale; Artificial intelligence; Computer science; Feature (linguistics); Pattern recognition (psychology); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Logistic regression; Machine learning; Biology; Virology; Fishery; Medicine; Pathology","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":[0.0007183651,0.0009565997,0.0009700472,0.0009567842,0.0003243802,0.0008383652,0.0007607612,0.0009034325,0.0009599595],"category_scores_gemma":[0.002634304,0.0003728838,0.0008237502,0.0004006705,0.0003820682,0.000463075,0.0007542069,0.0005668266,0.0001831632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003515462,"about_ca_system_score_gemma":0.0008536226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004246278,"about_ca_topic_score_gemma":0.002422757,"domain_scores_codex":[0.9997206,0.00006898732,0.00002727823,0.00007355338,0.00006254509,0.00004690894],"domain_scores_gemma":[0.9994467,0.0002918899,0.00008989878,0.00002882612,0.0001122138,0.00003045098],"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":[0.0002686904,0.0001006832,0.008200063,0.0001351681,0.0001524086,0.0004661389,0.00008669426,0.8558609,0.009115793,0.001946524,0.001503971,0.1221629],"study_design_scores_gemma":[0.000006060011,0.00002724823,0.0005099721,0.000004622312,0.00001024694,0.000035021,0.00001211282,0.9982496,0.0005976111,0.0003905448,0.0001508088,0.000006178533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1209998,0.0007471105,0.8743296,0.0005271051,0.00006028609,0.0001276505,0.000115569,0.0006404688,0.002452307],"genre_scores_gemma":[0.9118997,0.0002177472,0.08594055,0.000117475,0.00004316938,0.0001625978,0.0002202332,0.00005040244,0.001348187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004246278,"threshold_uncertainty_score":0.008443117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.476017418269832,"score_gpt":0.4142289528151324,"score_spread":0.06178846545469963,"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."}}