{"id":"W4283580437","doi":"10.22214/ijraset.2022.44577","title":"Deep Learning Process in Analyzing Crimes","year":2022,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Crime Patterns and Interventions","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Decision tree; Computer science; Crime analysis; Artificial intelligence; Process (computing); Machine learning; Criminology; Data science; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001715915,0.0003799468,0.0003598421,0.001480921,0.0003480567,0.001473747,0.0005250486,0.0007429078,0.002346264],"category_scores_gemma":[0.0061214,0.0001978545,0.000411193,0.001267857,0.0006291032,0.001910696,0.0007330509,0.001463284,0.0003361568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001253225,"about_ca_system_score_gemma":0.001214552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01133277,"about_ca_topic_score_gemma":0.00773455,"domain_scores_codex":[0.9995691,0.0001479159,0.00002574566,0.00007762232,0.00009480006,0.00008480416],"domain_scores_gemma":[0.997797,0.001344945,0.0002586442,0.0001031782,0.0004156599,0.00008058841],"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":[0.0002077477,0.0003871298,0.1101017,0.0001523404,0.0002007693,0.0002769206,0.0005003192,0.609798,0.00162469,0.0833101,0.003668097,0.1897722],"study_design_scores_gemma":[0.000002942167,0.00001936523,0.003869517,0.00002298998,0.000009342023,0.00001754539,0.00005348624,0.9787283,0.0003718844,0.01645688,0.0004425047,0.000005227722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6421206,0.002189832,0.332362,0.005926912,0.0002017935,0.0001110352,0.0005796817,0.0002922286,0.01621597],"genre_scores_gemma":[0.9844245,0.0004070383,0.01234633,0.0001062882,0.00003343137,0.00002647721,0.0001226108,0.000008950109,0.00252441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01133277,"threshold_uncertainty_score":0.02253366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0831829695976723,"score_gpt":0.4724665732862821,"score_spread":0.3892836036886098,"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."}}