{"id":"W3035183237","doi":"10.1109/access.2020.3001426","title":"Multi-Objective Optimization of Wavelet-Packet-Based Features in Pathological Diagnosis of Alzheimer Using Spontaneous Speech Signals","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Wavelet packet decomposition; Computer science; Wavelet; Pattern recognition (psychology); Entropy (arrow of time); Feature selection; Artificial intelligence; Speech recognition; Network packet; Frequency band; Wavelet transform; Feature extraction; Bandwidth (computing)","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":[0.001040378,0.0005480949,0.0004293212,0.0004974541,0.0001737789,0.0004928398,0.0003914725,0.0004830601,0.00038333],"category_scores_gemma":[0.001521336,0.0002345362,0.0004454419,0.0003172038,0.0002715833,0.000272194,0.0002728277,0.0003670699,0.00006024875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003473618,"about_ca_system_score_gemma":0.0006746906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002265688,"about_ca_topic_score_gemma":0.001487706,"domain_scores_codex":[0.9998136,0.00007224664,0.0000140753,0.00003042258,0.00003723242,0.00003243363],"domain_scores_gemma":[0.9995281,0.0003077667,0.0000482977,0.00001168026,0.00008668609,0.00001750877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001445532,0.0001824594,0.002025776,0.00007406928,0.00006107573,0.00006396923,0.00006030675,0.9336185,0.01124337,0.0007397819,0.0002044027,0.05158178],"study_design_scores_gemma":[0.000006103022,0.00005007287,0.0004750906,0.000002551636,0.000008426709,0.000006821424,0.00001118216,0.9980838,0.001188351,0.0001288164,0.00003678397,0.000001948546],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4426993,0.0002211823,0.5556082,0.0001364697,0.00002369563,0.00008823151,0.00005684721,0.0002116055,0.0009544957],"genre_scores_gemma":[0.9162939,0.00008975226,0.08279745,0.00003083775,0.000008996981,0.0001007031,0.00009717944,0.00001829148,0.0005629896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002265688,"threshold_uncertainty_score":0.005502164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1026722833410575,"score_gpt":0.3261930617415912,"score_spread":0.2235207784005336,"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."}}