{"id":"W4321062728","doi":"10.1109/indicon56171.2022.10039830","title":"An Efficient Floor Plan Classification with Optimized Image Features using Machine Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 19th India Council International Conference (INDICON)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Floor plan; Computer science; Artificial intelligence; Pipeline (software); Plan (archaeology); Listing (finance); Point (geometry); Machine learning; Image (mathematics); Contextual image classification; Computer vision; Engineering drawing; Engineering; Mathematics","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":[0.000220415,0.0008857006,0.0007783696,0.001088684,0.0003219091,0.0007630199,0.001136658,0.0009226227,0.004762775],"category_scores_gemma":[0.0005215863,0.0004246429,0.0009209605,0.0009098428,0.0002680084,0.0009691081,0.0005524302,0.001003719,0.00281009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006952404,"about_ca_system_score_gemma":0.001183399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01041494,"about_ca_topic_score_gemma":0.01652235,"domain_scores_codex":[0.999729,0.00001698151,0.00001059345,0.0000976775,0.0000715123,0.00007424063],"domain_scores_gemma":[0.9998385,0.00002882034,0.00001505915,0.00003510727,0.00006566223,0.00001692384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002222984,0.0002065661,0.002112285,0.00009283645,0.00005328791,0.0001714066,0.00004201846,0.06234216,0.03644015,0.001345448,0.01978382,0.8771878],"study_design_scores_gemma":[0.00001243039,0.0000534895,0.0016372,0.00001051046,0.00001342083,0.00009616365,0.00004066613,0.9793594,0.01495962,0.001345111,0.002456923,0.00001500141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07348054,0.0005197847,0.9047877,0.0005035798,0.0001917585,0.0001852673,0.001261801,0.0150467,0.004022782],"genre_scores_gemma":[0.4075187,0.0003126045,0.5718723,0.0003037391,0.00009243823,0.0001664569,0.005979422,0.0004036862,0.01335071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01041494,"threshold_uncertainty_score":0.02070862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06615422196470154,"score_gpt":0.2979351052722615,"score_spread":0.2317808833075599,"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."}}