{"id":"W4387491074","doi":"10.1109/tvt.2023.3323313","title":"SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"Higher Education Commission, Pakistan","keywords":"Drone; Scalability; Feature (linguistics); Computer science; FLOPS; Artificial intelligence; Feature engineering; Feature extraction; Deep learning; Machine learning; Real-time computing; Data mining; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000135132,0.0002149824,0.0002001506,0.0004407221,0.0005142983,0.00004026162,0.0004640823,0.0003163051,0.000003459409],"category_scores_gemma":[0.000007783288,0.0002167449,0.00006817698,0.003193578,0.0001436224,0.0001131825,0.00001626727,0.0005788933,0.0001512218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007796831,"about_ca_system_score_gemma":0.00001702546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003927787,"about_ca_topic_score_gemma":0.00004571638,"domain_scores_codex":[0.9984118,0.00004790295,0.0002042283,0.0006680372,0.0001955325,0.0004724874],"domain_scores_gemma":[0.9988203,0.0001252775,0.00007707562,0.00082708,0.00006106041,0.0000892298],"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":[0.0000137935,0.0000731221,0.000006761813,0.000007647663,0.00002700203,0.00002041834,0.00007067453,0.7279698,0.02039945,0.002091503,0.000134613,0.2491852],"study_design_scores_gemma":[0.0005931843,0.000215824,0.0003273374,0.00004089183,0.00002938727,0.0001386509,0.00003531214,0.8825883,0.1024065,0.004292364,0.008935366,0.0003968737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0835514,0.00016253,0.9097384,0.003463872,0.0004214245,0.0004771325,0.000003887173,0.002157628,0.00002374459],"genre_scores_gemma":[0.9833408,0.0001890422,0.01571062,0.0001027888,0.00004339122,0.0003138994,0.00000144432,0.00002665078,0.0002713864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8997894,"threshold_uncertainty_score":0.8838602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007779800556877475,"score_gpt":0.2323362406122583,"score_spread":0.2245564400553808,"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."}}