{"id":"W2953896412","doi":"10.1007/978-3-030-23813-1_16","title":"Prediction of Transaction Confirmation Time in Ethereum Blockchain Using Machine Learning","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Database transaction; Softmax function; Cryptocurrency; Blockchain; Artificial intelligence; Machine learning; Naive Bayes classifier; Random forest; Perceptron; Data mining; Computer security; Database; Artificial neural network; Support vector machine","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":[0.001701621,0.0005684881,0.0007015296,0.0009908864,0.0004300753,0.0009099819,0.0008005369,0.0008324253,0.002103361],"category_scores_gemma":[0.00568322,0.0003002597,0.0003087181,0.0007971241,0.0004143195,0.001346855,0.0004606744,0.00109893,0.0004969969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075876,"about_ca_system_score_gemma":0.0009666263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007442692,"about_ca_topic_score_gemma":0.006436129,"domain_scores_codex":[0.9994387,0.0001270058,0.00003480154,0.0001540324,0.0001388238,0.0001067043],"domain_scores_gemma":[0.9931793,0.00532285,0.0003988005,0.0002485401,0.000655079,0.0001955015],"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":[0.001057065,0.0002641166,0.02874838,0.0001150855,0.00006006837,0.0002181257,0.00005953911,0.8565966,0.002310187,0.005402497,0.003025689,0.1021426],"study_design_scores_gemma":[0.000003649361,0.00001590494,0.0006419334,0.000002583201,0.000002613878,0.00000959082,0.0000038557,0.9980522,0.0004105113,0.000805623,0.00004894746,0.000002538013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8456486,0.001364313,0.1462669,0.000580156,0.0001241292,0.00006861757,0.0009013108,0.0009433877,0.004102668],"genre_scores_gemma":[0.9912605,0.0001678004,0.006376413,0.00001665436,0.00002652948,0.00001750429,0.0004226685,0.00001669278,0.001695225],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007442692,"threshold_uncertainty_score":0.0147987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865809895356647,"score_gpt":0.2426487917221821,"score_spread":0.2239906927686156,"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."}}