{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003029463,0.0001643182,0.0001246559,0.00006504255,0.0001578898,0.00003169562,0.0002166402,0.00001785041,0.000280786],"category_scores_gemma":[0.000009156319,0.0001619808,0.00003689648,0.0001872019,0.00007034657,0.0001479233,0.00004251744,0.0001772276,0.000009324402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000268271,"about_ca_system_score_gemma":0.000004936965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003068171,"about_ca_topic_score_gemma":3.157026e-7,"domain_scores_codex":[0.9989508,0.00002269706,0.0002402715,0.0002508786,0.0003032931,0.0002320769],"domain_scores_gemma":[0.9995907,0.00003600153,0.00007406976,0.0002253904,0.00002900168,0.00004482151],"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":[0.00001945874,0.00005461871,0.0001528306,0.00002401608,0.00002739713,0.00001687464,0.00002507173,0.1414291,0.8270879,0.00002933021,0.00008454662,0.03104885],"study_design_scores_gemma":[0.0004208437,0.00006323943,0.008108735,0.000009588428,0.00005209727,0.0000181322,0.00009752155,0.7479329,0.2420871,0.0001835031,0.0007419552,0.0002843725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7598839,0.0004575875,0.2383684,0.0000638335,0.00006937519,0.0002681246,0.0002136783,0.0006397121,0.0000352769],"genre_scores_gemma":[0.9826611,0.00002251391,0.01694378,0.00001693508,0.00006055111,0.000128637,0.000103265,0.00004407064,0.00001915963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6065038,"threshold_uncertainty_score":0.6605384,"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."}}