{"id":"W3091178504","doi":"10.1109/icip40778.2020.9191078","title":"Optimization Using Artificial Immune Systems Applied To Object Tracking And Segmentation","year":2020,"lang":"en","type":"article","venue":"","topic":"Artificial Immune Systems Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Artificial intelligence; Computer science; Pattern recognition (psychology); Segmentation; Image segmentation; Weighting; Computer vision; Object detection; Artificial immune system; Video tracking; Kernel (algebra); Graph; Object (grammar); Mathematics","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":[0.001139807,0.0005709787,0.0007183689,0.000692221,0.0003700111,0.0007915866,0.0006603553,0.001041383,0.0008396127],"category_scores_gemma":[0.002782385,0.0003319242,0.0005796573,0.0004207587,0.000685588,0.0007224108,0.0007703273,0.0007169808,0.0001646194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006764251,"about_ca_system_score_gemma":0.0006255297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001456816,"about_ca_topic_score_gemma":0.0009728294,"domain_scores_codex":[0.9995152,0.0001856779,0.00002573705,0.00009674964,0.0001300995,0.00004656198],"domain_scores_gemma":[0.9991775,0.0004646149,0.0001099552,0.00004544625,0.0001749421,0.0000274741],"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":[0.00004280113,0.00004693636,0.0009292256,0.00006571806,0.00008019911,0.00006284763,0.00008040427,0.9098592,0.007509351,0.01169274,0.0004607223,0.06916995],"study_design_scores_gemma":[0.000002851828,0.00001879403,0.00006783415,0.000002994296,0.000004442022,0.000009631062,0.000004059787,0.997245,0.0007443107,0.001677505,0.0002196096,0.000003072612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0210265,0.0002950686,0.9767223,0.0001481165,0.00004548673,0.00003003835,0.000006071883,0.0002130041,0.001513394],"genre_scores_gemma":[0.7408012,0.0003086851,0.2557431,0.0002719026,0.0000737052,0.0001429286,0.00003503083,0.0000655034,0.002557785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001456816,"threshold_uncertainty_score":0.006027937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03742307638104631,"score_gpt":0.2456497460686963,"score_spread":0.20822666968765,"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."}}