{"id":"W4399049452","doi":"10.62110/sciencein.jist.2024.v12.805","title":"Securing the patient healthcare data using Deep Inception-ResNet based CPABPP model in Internet of Things","year":2024,"lang":"en","type":"article","venue":"Journal of Integrated Science and Technology","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Internet of Things; Health care; Internet privacy; The Internet; Computer science; Computer security; Residual neural network; Business; Data science; Psychology; Artificial intelligence; World Wide Web; Political science; Deep learning","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":[0.0004805422,0.0004402609,0.0003375064,0.0002975905,0.0002343049,0.0006346064,0.001092801,0.0007993985,0.001485391],"category_scores_gemma":[0.0008055388,0.0001652201,0.0004064146,0.0002265227,0.0005333981,0.001141666,0.0006574424,0.0007061111,0.0004730451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006336029,"about_ca_system_score_gemma":0.0006126291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004434186,"about_ca_topic_score_gemma":0.004749866,"domain_scores_codex":[0.9998001,0.00004363057,0.00001037552,0.00005134327,0.00005261867,0.00004197603],"domain_scores_gemma":[0.9997955,0.00005585195,0.0000253057,0.00003204565,0.00007536537,0.00001607108],"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":[0.0006970183,0.0001986559,0.002802161,0.0001945306,0.0001228845,0.0007336994,0.0001404805,0.7174984,0.02235363,0.04686118,0.009573434,0.1988239],"study_design_scores_gemma":[0.000005624006,0.00007550428,0.0002076515,0.000007323016,0.00001085167,0.00006946327,0.000008920685,0.9909247,0.003049378,0.004581859,0.001050598,0.00000800734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0965497,0.001141146,0.8884221,0.001411274,0.0002182012,0.00009653639,0.0002976904,0.001978155,0.009885189],"genre_scores_gemma":[0.9303591,0.0005413629,0.0606943,0.0003740454,0.00004508491,0.00008805858,0.0003056907,0.00005215925,0.007540175],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004434186,"threshold_uncertainty_score":0.008816779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02265029779342298,"score_gpt":0.2819839501006503,"score_spread":0.2593336523072273,"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."}}