{"id":"W4310801051","doi":"10.18280/ts.390530","title":"Thermal Image Diseases Identification Using Hybrid Genetic Algorithm with Relevance Vector Machine Classification","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Artificial intelligence; Algorithm; Pattern recognition (psychology); Computer science; Gaussian filter; Principal component analysis; Classifier (UML); Genetic algorithm; Filter (signal processing); Precision and recall; Computer vision; Machine learning; Image (mathematics)","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":[0.001487517,0.001010854,0.001558604,0.002277269,0.0004791047,0.00104568,0.001422817,0.001501505,0.0008469724],"category_scores_gemma":[0.003707235,0.0003500977,0.001064276,0.001265021,0.0003951813,0.0007506022,0.0006245985,0.000698096,0.0003524504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008594532,"about_ca_system_score_gemma":0.001087512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006554738,"about_ca_topic_score_gemma":0.003390749,"domain_scores_codex":[0.9987546,0.0003014591,0.00009236104,0.0003811269,0.0003241097,0.0001462994],"domain_scores_gemma":[0.998838,0.0005452913,0.0001335004,0.00007585297,0.0003677034,0.00003968846],"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":[0.0002522604,0.0003888096,0.006237861,0.0001153177,0.0002187982,0.0001648281,0.0001228157,0.3361778,0.0135592,0.001712876,0.001919181,0.6391302],"study_design_scores_gemma":[0.00001619913,0.00008869618,0.0009102537,0.000006208456,0.00001959656,0.00005686292,0.00001213328,0.9959732,0.001973473,0.0006469326,0.0002844239,0.00001190616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1323267,0.000923845,0.8615993,0.0003718743,0.0001375293,0.0002781785,0.0001101752,0.00203387,0.002218448],"genre_scores_gemma":[0.6797919,0.0002029892,0.3167574,0.0002160087,0.00008470276,0.0003289571,0.0002725045,0.00006869344,0.002276937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006554738,"threshold_uncertainty_score":0.01303315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01018769424094316,"score_gpt":0.2106578202281495,"score_spread":0.2004701259872064,"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."}}