{"id":"W3119320318","doi":"10.18280/ts.370609","title":"Multiple Linear Regression of Multi-class Images in Devices of Internet of Things","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province","keywords":"Disjoint sets; Computer science; MNIST database; Convolutional neural network; Robustness (evolution); Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Benchmark (surveying); Contextual image classification; Data mining; Artificial neural network; Machine learning; Image (mathematics); 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":[0.0001355176,0.00007397404,0.0001673123,0.0000669447,0.00001155927,0.000006033901,0.0003961454,0.00003281846,0.00002875438],"category_scores_gemma":[0.00001433341,0.000060407,0.00005694886,0.0002420159,0.0000455787,0.0001777475,0.0001222256,0.00006703843,0.000001490426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008284647,"about_ca_system_score_gemma":0.00001600875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001063984,"about_ca_topic_score_gemma":0.000005221224,"domain_scores_codex":[0.9992002,0.00002871045,0.0003774759,0.0001668644,0.0001507241,0.00007597481],"domain_scores_gemma":[0.999474,0.00004967783,0.0002480464,0.0001254073,0.00006951269,0.00003334213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007435859,0.0006926022,0.01816627,0.0003726237,0.00003213023,0.000002981664,0.006649221,0.0006222868,0.9159967,0.004636193,0.0005627368,0.05219192],"study_design_scores_gemma":[0.0003638878,0.0001678736,0.003802011,0.00007734641,0.000003006487,3.964562e-7,0.00006743504,0.3553949,0.6395714,0.00005322652,0.0004434245,0.00005516587],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1804292,0.0000509168,0.8187782,0.0004010492,0.00001050104,0.0001848071,0.000005566276,0.0000465747,0.00009320029],"genre_scores_gemma":[0.9305571,0.000007936539,0.06929816,0.000097684,0.000008685053,0.00001316515,0.000001763826,0.000003686507,0.00001187628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7501279,"threshold_uncertainty_score":0.2463326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02957901046850868,"score_gpt":0.2687790163954437,"score_spread":0.239200005926935,"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."}}