{"id":"W7164321798","doi":"10.1109/iciics67880.2026.11483469","title":"HG-SSA-ChurnNet: A Hybrid Gradient-Guided Salp Swarm Optimized Deep Learning Framework for Telecom Analytics","year":2005,"lang":"","type":"article","venue":"","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Analytics; Artificial neural network; Deep neural networks; Big data; Key (lock)","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":[0.0007367739,0.001202423,0.0008414713,0.0005537022,0.0004097127,0.0007197422,0.001536352,0.001211122,0.001451622],"category_scores_gemma":[0.001565399,0.0005011669,0.0005813617,0.0003398406,0.0006126489,0.000986494,0.001031422,0.001504631,0.0003789259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165703,"about_ca_system_score_gemma":0.001639147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01462083,"about_ca_topic_score_gemma":0.0217714,"domain_scores_codex":[0.9997823,0.00005605681,0.00001358242,0.00005173226,0.00005957198,0.00003677397],"domain_scores_gemma":[0.9996067,0.0001679344,0.00004543334,0.00003256324,0.0001087631,0.0000386114],"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":[0.00006732257,0.00006115461,0.001601725,0.00003800284,0.00004546879,0.00006941558,0.0000566808,0.9333739,0.001700408,0.003493424,0.001736594,0.05775594],"study_design_scores_gemma":[0.000002420699,0.000009507257,0.00003461252,0.000001953266,0.000001854999,0.000002933502,0.000002306136,0.9991741,0.0001600516,0.0004832111,0.0001258439,0.000001227467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05023714,0.0006120925,0.9425473,0.0004371637,0.00009004988,0.00008666787,0.0001142177,0.002133883,0.003741455],"genre_scores_gemma":[0.7671245,0.0003441218,0.2244536,0.0005763334,0.00007292395,0.0002541141,0.0005427753,0.0003144278,0.006317235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01462083,"threshold_uncertainty_score":0.02907145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218579458083682,"score_gpt":0.275322777750211,"score_spread":0.2534648319418428,"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."}}