{"id":"W3040483011","doi":"10.18280/mmep.070213","title":"Comparison of Regression Model with Multi-layer Perceptron Model While Optimising Cutting Force Using Genetic Algorithm","year":2020,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perceptron; Computer science; Genetic algorithm; Regression analysis; Layer (electronics); Regression; Algorithm; Artificial intelligence; Machine learning; Artificial neural network; Mathematics; Statistics; Materials science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007520658,0.0002975453,0.0004463094,0.00006400699,0.00008548836,0.00004558486,0.000104734,0.0001108625,0.000002256681],"category_scores_gemma":[0.0000128887,0.0002564494,0.0000434252,0.0001496328,0.0000265061,0.0001880161,0.00004353909,0.0002716905,6.449081e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002872334,"about_ca_system_score_gemma":0.00001301108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001258457,"about_ca_topic_score_gemma":4.684498e-8,"domain_scores_codex":[0.9987409,0.000005164427,0.0004543962,0.0002781144,0.0001990567,0.0003223492],"domain_scores_gemma":[0.9995329,0.00003595486,0.0000775309,0.0001387212,0.00005816371,0.0001567675],"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":[0.000006383988,0.00002443752,0.000005352053,0.001457583,0.00001999624,5.375196e-7,0.002768914,0.991681,0.002839646,0.0001020623,0.000001008024,0.001093095],"study_design_scores_gemma":[0.0003225574,0.00004411461,3.541787e-7,0.0007616384,0.00005535341,0.000006434801,0.00008774566,0.9969062,0.001133779,0.0003720431,0.000001200482,0.0003085842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03221998,0.0005755456,0.9666049,0.00001316417,0.00001508124,0.0001909533,0.000004438662,0.000321489,0.00005438374],"genre_scores_gemma":[0.4496746,0.00004430602,0.5501828,0.000004511061,0.00001391345,0.000007103454,0.000003023352,0.00006261808,0.000007124249],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4174546,"threshold_uncertainty_score":0.9999888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07759055388590058,"score_gpt":0.27990939658002,"score_spread":0.2023188426941194,"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."}}