{"id":"W6892886310","doi":"10.5281/zenodo.12783618","title":"PREDICTING CHURN IN TELECOM SECTOR USING A POPULATION-BASED INCREMENTAL ANN LEARNING ALGORITHM","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Churning; Artificial neural network; Customer retention; Profit (economics); Customer satisfaction; Probabilistic logic; Inefficiency; Customer intelligence; Simulated annealing","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":[0.0007585186,0.000541945,0.0006282401,0.0007524976,0.0003405621,0.0006163421,0.000748136,0.0009524277,0.0009729221],"category_scores_gemma":[0.001893973,0.0003784739,0.0004659631,0.0005320667,0.0003118615,0.0004252884,0.0003712998,0.0007241316,0.0001174611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000946327,"about_ca_system_score_gemma":0.0006525879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01579857,"about_ca_topic_score_gemma":0.01032924,"domain_scores_codex":[0.9997973,0.00005869695,0.00001311757,0.00005229141,0.00003707546,0.00004159174],"domain_scores_gemma":[0.9989693,0.0006795439,0.000085103,0.00003152769,0.0001996551,0.00003473292],"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":[0.00003221797,0.00004327251,0.002646055,0.00001067752,0.00002085014,0.0000294804,0.00001482064,0.9847471,0.000283825,0.0002391476,0.0001791539,0.01175347],"study_design_scores_gemma":[6.631665e-7,0.00000423761,0.0001179654,7.557668e-7,0.000001184592,0.000001353671,0.000001002539,0.9997717,0.00003835483,0.00005161177,0.00001062271,5.862535e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6777622,0.0008210567,0.3141585,0.0005215406,0.00009671252,0.00008990002,0.0001616081,0.0006325982,0.005755955],"genre_scores_gemma":[0.9818853,0.00008925504,0.01676326,0.00005009558,0.00001193119,0.00004600958,0.00009183842,0.000008651263,0.001053679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01579857,"threshold_uncertainty_score":0.0314132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03312816622723147,"score_gpt":0.2463427079587965,"score_spread":0.2132145417315651,"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."}}