{"id":"W4281884786","doi":"10.3390/math10111878","title":"A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Electricity Theft Detection Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Autoregressive integrated moving average; Computer science; Artificial intelligence; Process (computing); Autoregressive model; Random forest; Machine learning; Set (abstract data type); Time series; Series (stratigraphy); Transformation (genetics); Data mining; Pattern recognition (psychology); Econometrics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005990326,0.0001141924,0.0001843174,0.0001906656,0.0001443507,0.00002426616,0.00006703175,0.00004638418,0.00003709244],"category_scores_gemma":[0.0000661668,0.0001280069,0.00004061862,0.0002486477,0.00001345707,0.00009956952,0.00001343901,0.0002430632,0.000001190523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009539782,"about_ca_system_score_gemma":0.000006992191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002191433,"about_ca_topic_score_gemma":0.00006435793,"domain_scores_codex":[0.999357,0.00004415529,0.0002482048,0.00009400991,0.0001198923,0.0001367104],"domain_scores_gemma":[0.999631,0.000185975,0.00004675244,0.00009548885,0.00002099073,0.00001975098],"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":[0.0003701705,0.0007532567,0.0001512726,0.005396523,0.0001587482,0.000002257292,0.02200319,0.4013512,0.4274822,0.00234129,0.0001871357,0.1398027],"study_design_scores_gemma":[0.0002686083,0.0001326835,0.00003554373,0.000008495358,0.00001131724,0.00001437575,0.0001541141,0.8468987,0.148188,0.002750159,0.001427785,0.0001102519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03464475,0.00004794581,0.9639299,0.00003736264,0.00003090336,0.0004622469,0.00002763845,0.0005943339,0.0002249602],"genre_scores_gemma":[0.8655918,0.000005654536,0.1339876,0.00001716528,0.000007982573,0.0003014008,0.00001296285,0.00003414524,0.00004134996],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.830947,"threshold_uncertainty_score":0.5219969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564539029125576,"score_gpt":0.2385621755276151,"score_spread":0.2229167852363594,"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."}}