HANDLING BIG TABULAR DATA OF ICT SUPPLY CHAINS: A MULTI-TASK, MACHINE-INTERPRETABLE APPROACH
Abstract
The essential details of ICT devices are frequently distilled into large tabular data sets that are distributed throughout supply chains as a result of the features of Information and Communications Technology (ICT) goods. With the explosion of electronic assets, it is crucial to automatically analyse tabular structures. We develop a Table Structure Recognition (TSR) work and a Table Cell Type Classification (CTC) task to convert the tabular data in electronic documents into a machine-interpretable format and give layout and semantic information for information extraction and interpretation. For the TSR job, complicated table structures are represented using a graph. Table cells are divided into three groups—Header, Attribute, and Data—based on how they work for the CTC job. Then, utilising the text modal and picture modal characteristics, we provide a multi-task model to accomplish the two tasks concurrently. Our test findings demonstrate that, using the ICDAR2013 and UNLV datasets, our suggested strategy can beat cutting-edge approaches.
References
[2] Jannatul Ferdous Ruma, Sharmin Akter, Jesrin Jahan Laboni, Rashedur M. Rahman, A deep learning classification model for Persian Hafez poetry based on the poet’s era, Decision Analytics Journal, Volume 4, 2022, 100111, ISSN 2772-6622, https://doi.org/10.1016/j.dajour.2022.100111.
[3] Lucheng Hong, Zehua Chen, Yifei Wang, Mohammad Shahidehpour, Minghe Wu,A novel SVM-based decision framework considering feature distribution for Power Transformer Fault Diagnosis, Energy Reports, Volume 8, 2022, Pages 9392-9401,ISSN 2352-4847, https://doi.org/10.1016/j.egyr.2022.07.062.
[4] Sara Barja-Martinez, Mònica Aragüés-Peñalba, Íngrid Munné-Collado, Pau Lloret-Gallego, Eduard Bullich-Massagué, Roberto Villafafila-Robles, Artificial intelligence techniques for enabling Big Data services in distribution networks: A review, Renewable and Sustainable Energy Reviews, Volume 150, 2021, 111459, ISSN 1364-0321, https://doi.org/10.1016/j.rser.2021.111459.
[5] G. Tang, L. Xie, L. Jin, J. Wang, J. Chen, Z. Xu, Q. Wang, Y. Wu, and H. Li, “Matchvie: Exploiting match relevancy between entities for visual information extraction,” arXiv preprint arXiv:2106.12940, 2021.
[6] Jonathan Cohen, Jorge Gil, An entity-relationship model of the flow of waste and resources in city-regions: Improving knowledge management for the circular economy, Resources, Conservation & Recycling Advances, Volume 12, 2021, 200058, ISSN 2667-3789,https://doi.org/10.1016/j.rcradv.2021.200058.
[7] Zhaoyang Qu, Zhenming Zhang, Shuai Liu, Jie Cao, Xiaoyong Bo, Knowledge-driven recognition methodology for electricity safety hazard scenarios,Energy Reports, Volume 8, 2022, Pages 10006-10016, ISSN 2352-4847,https://doi.org/10.1016/j.egyr.2022.07.158.
[8] Yang Huang, Zizhen Li, Qiyang Hong, Lizhi Zhou, Yue Ma, Yisha Hu, Jiabao Xin, Tingting Li, Zhibo Kong, Qingbing Zheng, Yixin Chen, Qinjian Zhao, Ying Gu, Jun Zhang, Yingbin Wang, Hai Yu, Shaowei Li, Ningshao Xia, A stepwise docking molecular dynamics approach for simulating antibody recognition with substantial conformational changes, Computational and Structural Biotechnology Journal, Volume 20, 2022, Pages 710720, ISSN 2001-0370, https://doi.org/10.1016/j.csbj.2022.01.012.
[9] Stefania Fresca, Andrea Manzoni, POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition,Computer Methods in Applied Mechanics and Engineering, Volume 388, 2022, 114181, ISSN 0045-7825, https://doi.org/10.1016/j.cma.2021.114181.
[10] Kazuhide Mimura, Shugo Minabe, Kentaro Nakamura, Kazutaka Yasukawa, Junichiro Ohta, Yasuhiro Kato, Automated detection of microfossil fish teeth from slide images using combined deep learning models, Applied Computing and Geosciences, 2022, 100092, ISSN 2590-1974, https://doi.org/10.1016/j.acags.2022.100092.
[11] Ruihui Xue, Wei Xiang, Yansong Deng, Improved Faster R-CNN Based On CSP-DPN, Procedia Computer Science, Volume 199, 2022, Pages 1490-1497, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2022.01.190.
[12] Hongjie He, Hongzhang Xu, Ying Zhang, Kyle Gao, Huxiong Li, Lingfei Ma, Jonathan Li,Mask R-CNN based automated identification and extraction of oil well sites, International Journal of Applied Earth Observation and Geoinformation, Volume 112, 2022, 102875, ISSN 1569-8432, https://doi.org/10.1016/j.jag.2022.102875.
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