[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-182964-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-182964-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","document-metadata-extraction-multimodal","Document Metadata Extraction (Multimodal)","","This document presents a comparative analysis of different algorithms, specifically FIMT-DD, UCAT, and UCATtwig, across various datasets. The primary metrics evaluated are MRCAE, MRRMSE, and MCAE. The data, presented in tabular format and visualized in a scatter plot, indicates the performance of each algorithm relative to its rank from 'Best' to 'Worst' on the FIMT-DD variation rank. The scatter plot highlights the distribution of MRCAE values, showing FIMT-DD generally performing worse than UCAT and UCATtwig at the higher ranks, while UCATtwig exhibits the lowest MRCAE values across most variations. The MRRMSE and MCAE metrics are also detailed in the tables, offering a comprehensive view of the algorithms' effectiveness. An illustrative diagram demonstrates a temporal feature extraction and prediction process, suggesting applications in sequential data analysis. 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It highlights that UCATtwig generally achieves lower MRCAE values across most variations.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},182964,1788350661,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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--- |\n| dataset | FIMT-DD | UCAT | UCATtwig | FIMT-DD | UCAT | UCATtwig | FIMT-DD | UCAT | UCATtwig |\n| c_ 10_multi_dim_tree_4_2 | 1.099 | 1.098 | 1.094 | 1.077 | 1.039 | 1.043 | 1.97E+05 | 2.02E+05 | 2.03E+05 |\n| c_ 10_steps_2_ 1 | 1.193 | 1.025 | 1.018 | 1.128 | 1.009 | 1.008 | 3.76E+05 | 3.11E+05 | 3.10E+05 |\n| c_ 13_multi_dim_splitter_tree_4_2 | 1.134 | 1.111 | 1.088 | 1.052 | 1.050 | 1.045 | 2.27E+05 | 2.43E+05 | 2.38E+05 |\n| c_ 13_sideways_helix_2_ 1 | 1.195 | 1.052 | 1.047 | 1.139 | 1.051 | 1.021 | 7.66E+05 | 6.63E+05 | 6.59E+05 |\n| c_ 15_multi_dim_fold_4_2 | 1.245 | 1.015 | 1.034 | 1.225 | 1.006 | 1.023 | 2.44E+06 | 2.02E+06 | 2.07E+06 |\n| c_20_double_fold_2_ 1 | 1.393 | 1.049 | 1.032 | 1.282 | 1.034 | 1.026 | 5.86E+05 | 4.53E+05 | 4.48E+05 |\n| c_26_layered_peak_2_ 1 | 1.180 | 1.058 | 1.049 | 1.139 | 1.039 | 1.034 | 1.14E+06 | 9.54E+05 | 9.46E+05 |\n| c_27_distorted_saddle_2_ 1 | 1.159 | 1.041 | 1.062 | 1.109 | 1.030 | 1.058 | 6.63E+08 | 5.77E+08 | 5.91E+08 |\n| c_29_stripes_2_ 1 | 1.142 | 1.025 | 1.036 | 1.112 | 1.020 | 1.029 | 4.02E+05 | 3.60E+05 | 3.65E+05 |\n| c_2_wave_2_ 1 | 1.129 | 1.046 | 1.038 | 1.117 | 1.022 | 1.020 | 4.05E+08 | 3.68E+08 | 3.63E+08 |\n| c_3_fold_2_ 1 | 1.097 | 1.081 | 1.076 | 1.070 | 1.041 | 1.042 | 5.12E+05 | 4.95E+05 | 4.93E+05 |\n| c_4_skew_raise_2_ 1 | 1.128 | 1.031 | 1.024 | 1.099 | 1.013 | 1.012 | 3.11E+08 | 2.67E+08 | 2.65E+08 |\n| c_6_polynomial_4_2 | 1.168 | 1.022 | 1.032 | 1.162 | 1.012 | 1.023 | 2.38E+09 | 2.05E+09 | 2.08E+09 |\n| c_9_broken_helix_2_ 1 | 1.220 | 1.046 | 1.024 | 1.164 | 1.037 | 1.016 | 7.02E+05 | 6.05E+05 | 6.02E+05 |\n| c_9_multi_dim_hook_4_2 | 1.153 | 1.036 | 1.043 | 1.132 | 1.024 | 1.031 | 2.98E+06 | 2.83E+06 | 2.85E+06 |\n| Avg. all datasets | 1.176 | 1.049 | 1.047 | 1.134 | 1.028 | 1.029 |  |  |  |","cbCaikDoDKlKx2KV","https://ap.wps.com/l/cbCaikDoDKlKx2KV","pdf",2719842,18,"English","# Performance Analysis of FIMT-DD, UCAT, and UCATtwig\n## Data and Metrics\n## Visual Representation of MRCAE\n## Temporal Feature Extraction and Prediction Diagram","[{\"question\":\"What are the main algorithms being compared in this document?\",\"answer\":\"The document compares three algorithms: FIMT-DD, UCAT, and UCATtwig.\"},{\"question\":\"Which metric shows the most significant performance difference between the algorithms?\",\"answer\":\"The MRCAE metric appears to show the most significant differences, especially at the 'Worst' end of the FIMT-DD variation rank, where FIMT-DD's values increase dramatically compared to UCAT and UCATtwig.\"},{\"question\":\"What does the scatter plot illustrate regarding the algorithm performance?\",\"answer\":\"The scatter plot illustrates the Mean Reciprocal Rank (MRCAE) for different variations of FIMT-DD, UCAT, and UCATtwig, ranked by their performance on the FIMT-DD variations. It highlights that UCATtwig generally achieves lower MRCAE values across most variations.\"}]","Document Metadata Extraction (Multimodal) | PDF",45]