{"id":"W4389341525","doi":"10.17762/ijritcc.v11i10.8525","title":"Convolutional Neural Network – Based Algorithm for Currency Exchange Rate Prediction","year":2023,"lang":"en","type":"article","venue":"International Journal on Recent and Innovation Trends in Computing and Communication","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Liberian dollar; Currency; Foreign exchange market; Computer science; Random forest; Convolutional neural network; Artificial intelligence; Feature selection; Artificial neural network; Exchange rate; Machine learning; Economics; Finance; Monetary economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009483985,0.0001327558,0.0001773053,0.001995591,0.0003956652,0.0003603738,0.0004586956,0.00007551707,0.00008082909],"category_scores_gemma":[0.001533551,0.0001179787,0.00004484528,0.002705693,0.00006297725,0.0002516244,0.0001550527,0.0003387483,0.000002995686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001156944,"about_ca_system_score_gemma":0.00004290473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003845849,"about_ca_topic_score_gemma":0.000004043482,"domain_scores_codex":[0.9973499,0.0006192247,0.00090366,0.0002778772,0.000646879,0.0002024475],"domain_scores_gemma":[0.9952211,0.002732852,0.0006023804,0.0002000745,0.001198139,0.00004539842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007324833,0.00003396431,0.008213349,0.000001297615,0.00001377361,8.760945e-7,0.00009472296,0.003048498,0.000009258581,0.003427369,0.006730285,0.9783534],"study_design_scores_gemma":[0.001080429,0.00009749095,0.1189577,0.00007769732,0.000004066334,0.00001807748,0.0000538916,0.8171571,0.000008574504,0.02145353,0.04099279,0.00009871083],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4519301,0.001117563,0.4858822,0.04561934,0.01007308,0.0005266465,0.0001420282,0.0002886508,0.004420409],"genre_scores_gemma":[0.9115474,0.001416918,0.08216844,0.001606579,0.001428732,0.00004377387,0.0007807509,0.00003290829,0.0009745211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9782547,"threshold_uncertainty_score":0.4811032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1929478861291196,"score_gpt":0.4532814949866392,"score_spread":0.2603336088575196,"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."}}