{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T19:20:48Z","timestamp":1754162448121,"version":"3.41.2"},"reference-count":22,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The digitization of important documents and their segregation can be a beneficial and time-saving activity as individuals will have greater access to important documents and will be able to use them in regular tasks as well as endeavours. In recent years, research into the application of deep networks in robot systems has increased as a direct consequence of the advancements made in classification algorithms over the past few decades. Robotic vision automation for the segregation of sensitive and non-sensitive documents is required for many security concerns. The methodology of this article is initially focused on the identification of a good computer vision-based technique for the classification of sensitive documents from non-sensitive documents. The authors first identified the standard parameters in terms of reliability, loss, precision, and recall by employing deep learning techniques, such as neural networks with convolutions and transfer learning (TL) algorithms. The extraction of features based on pre-trained deep learning models was referenced in numerous publications. Similarly, we applied most of the feature extraction techniques to identify feature extraction from the images. Then, these features were classified by machine and ensemble learning models. However, the pre-trained models-based feature extraction along with machine learning classification resulted better in comparison to the deep learning and TL procedures. Further, the better-identified techniques were applied as the brain behind the vision of a robotic structure to automate the segregation of sensitive documents from non-sensitive documents. This proposed robotic structure could be applied when we have to find some specific and classified document from the haystack.<\/jats:p>","DOI":"10.1515\/pjbr-2022-0125","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T06:00:46Z","timestamp":1753855246000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep trained features extraction and dense layer classification of sensitive and normal documents for robotic vision-based segregation"],"prefix":"10.1515","volume":"15","author":[{"given":"Vikas","family":"Khullar","sequence":"first","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University , Punjab , India"}]},{"given":"Isha","family":"Kansal","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University , Punjab , India"}]},{"given":"Jyoti","family":"Verma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Punjabi University Patiala , Patiala , Punjab , India"}]},{"given":"Rajeev","family":"Kumar","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University , Punjab , India"}]},{"given":"Karuna","family":"Salgotra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, CT Institute of Engineering, Management and Technology , Punjab , India"}]},{"given":"Gurpreet Singh","family":"Saini","sequence":"additional","affiliation":[{"name":"School of Electronics and Electrical Engineering, Lovely Professional University , Punjab , India"}]}],"member":"374","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"2025073006004125573_j_pjbr-2022-0125_ref_001","doi-asserted-by":"crossref","unstructured":"R. Sicre, A. M. Awal, and T. Furon, \u201cIdentity documents classification as an image classification problem,\u201d Image Anal. Process. - ICIAP, vol. 2017, pp. 602\u2013613, 2017.","DOI":"10.1007\/978-3-319-68548-9_55"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_002","doi-asserted-by":"crossref","unstructured":"R. Kumari and S. K. Srivastava, \u201cMachine learning: A review on binary classification,\u201d Int. J. Comput. Appl., vol. 160, no. 7, pp. 11\u201315, 2017.","DOI":"10.5120\/ijca2017913083"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_003","doi-asserted-by":"crossref","unstructured":"H. Blockeel, K. Kersting, S. Nijssen, and F. \u017delezn\u00fd, Machine learning and knowledge discovery in databases, Springer Berlin, Heidelberg, 2013.","DOI":"10.1007\/978-3-642-40994-3"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_004","doi-asserted-by":"crossref","unstructured":"I. Kansal and S. S. Kasana, \u201cMinimum preserving subsampling-based fast image de-fogging,\u201d J. Mod. Opt., vol. 65, no. 18, pp. 2103\u20132123, 2018.","DOI":"10.1080\/09500340.2018.1499976"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_005","doi-asserted-by":"crossref","unstructured":"J. Snehi, M. Snehi, D. Prasad, S. Simaiya, I. Kansal, and V. Baggan, \u201cSDN\u2010based cloud combining edge computing for IoT infrastructure,\u201d In: Software Defined Networks: Architecture and Applications, Wiley, Texas, 2022, pp. 497\u2013540.","DOI":"10.1002\/9781119857921.ch14"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_006","unstructured":"N. Khandan, \u201cAn intelligent hybrid model for identity document classification,\u201d arXiv preprint arXiv:2106.0434, 2021, 10.48550\/arXiv.2106.04345."},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_007","doi-asserted-by":"crossref","unstructured":"J. Snehi, A. Bhandari, M. Snehi, U. Tandon, and V. Baggan, \u201cGlobal intrusion detection environments and platform for anomaly-based intrusion detection systems,\u201d in Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, 2021, pp. 817\u2013831.","DOI":"10.1007\/978-981-16-0733-2_58"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_008","doi-asserted-by":"crossref","unstructured":"H. Kaur, D. Koundal, and V. Kadyan, \u201cImage fusion techniques: A survey,\u201d Arch. Comput. Methods Eng., vol. 28, no. 2, pp. 4425\u20134447, 2021.","DOI":"10.1007\/s11831-021-09540-7"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_009","doi-asserted-by":"crossref","unstructured":"M. Mukhtar, M. Bilal, A. Rahdar, M. Barani, R. Arshad, T. Behl, et al., \u201cNanomaterials for diagnosis and treatment of brain cancer: Recent updates,\u201d Chemosensors, vol. 8, no. 2, p. 117, 2020.","DOI":"10.3390\/chemosensors8040117"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_010","doi-asserted-by":"crossref","unstructured":"W. Xiong, X. Jia, D. Yang, M. Ali, L. Li, and S. Wang, \u201cDP-LinkNet: A convolutional network for historical document image binarization,\u201d KSII Trans. Internet Inf. Syst. (TIIS), vol. 15, no. 1, pp. 1778\u20131797, 2021.","DOI":"10.3837\/tiis.2021.05.011"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_011","doi-asserted-by":"crossref","unstructured":"J. Snehi, A. Bhandari, M. Snehi, V. Baggan, and H. Kaur, \u201cAIDAAS: Incident handling and remediation anomaly-based IDaaS for cloud service providers,\u201d 10th International Conference on System Modeling & Advancement in Research Trends, vol. 1, no. 4, 2021, pp. 356\u2013360.","DOI":"10.1109\/SMART52563.2021.9676296"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_012","doi-asserted-by":"crossref","unstructured":"M. Phankokkruad, \u201cCOVID-19 pneumonia detection in chest X-ray images using transfer learning of convolutional neural networks,\u201d Proceedings of the 3rd International Conference on Data Science and Information Technology, vol. 2, no. 2, 2020, pp. 147\u2013152.","DOI":"10.1145\/3414274.3414496"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_013","doi-asserted-by":"crossref","unstructured":"H. Li, X. Dou, C. Tao, Z. Wu, J. Chen, J. Peng, et al. Rsi-cb: A large-scale remote sensing image classification benchmark using crowdsourced data. Sensors, vol. 20, no. 6, pp. 1594, 2020.","DOI":"10.3390\/s20061594"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_014","doi-asserted-by":"crossref","unstructured":"A. M. Awal, N. Ghanmi, R. Sicre, and T. Furon, \u201cComplex document classification and localization application on identity document images,\u201d Proceedings of the International Conference on Document Analysis and Recognition (ICDAR), vol. 1, no. 2, 2017, pp. 426\u2013431.","DOI":"10.1109\/ICDAR.2017.77"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_015","doi-asserted-by":"crossref","unstructured":"J. Ahamed, M. Ahmed, N. Afreen, M. Ahmed, and M. Sameer, \u201cAn inception V3 approach for malware classification using machine learning and transfer learning,\u201d SSRN Electron. J., vol. 4, no. 4, pp. 11\u201318, 2022.","DOI":"10.1016\/j.ijin.2022.11.005"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_016","unstructured":"N. Dominic, D. Daniel, T. W. Cenggoro, A. Budiarto, and B. Pardamean, \u201cTransfer learning using inception-ResNet-v2 model to the augmented neuroimages data for autism spectrum disorder classification,\u201d Commun. Math. Biol. Neurosci., vol. 39, pp. 1\u201321, 2021."},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_017","unstructured":"M. Kozlenko, V. Sendetskyi, O. Simkiv, N. Savchenko, and A. Bosyi, \u201cIdentity documents recognition and detection using semantic segmentation with convolutional neural network,\u201d CEUR Workshop Proceedings, vol. 2923, no. 1, 2021, pp. 234\u2013242. 10.5281\/zenodo.5758182."},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_018","doi-asserted-by":"crossref","unstructured":"A. Guha, D. Samanta, A. Banerjee, and D. Agarwal, \u201cA deep learning model for information loss prevention from multi-page digital documents,\u201d IEEE Access, vol. 9, pp. 80451\u201380465, 2021.","DOI":"10.1109\/ACCESS.2021.3084841"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_019","doi-asserted-by":"crossref","unstructured":"C. Tensmeyer and T. Martinez, \u201cAnalysis of convolutional neural networks for document image classification,\u201d arXiv preprint arXiv:1708.03273, 2017, 10.48550\/arXiv.1708.03273.","DOI":"10.1109\/ICDAR.2017.71"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_020","doi-asserted-by":"crossref","unstructured":"L. Shu, H. Xu, and B. Liu, \u201cDOC: Deep open classification of text documents,\u201d Conference on Empirical Methods in Natural Language Processing, vol. 1, no. 2, 2017, pp. 2911\u20132916.","DOI":"10.18653\/v1\/D17-1314"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_021","doi-asserted-by":"crossref","unstructured":"Y. Lu, \u201cIndustry 4.0: A survey on technologies, applications and open research issues,\u201d J. Ind. Inf. Integr., vol. 6, no. 1, pp. 1\u201310, 2017.","DOI":"10.1016\/j.jii.2017.04.005"},{"key":"2025073006004125573_j_pjbr-2022-0125_ref_022","doi-asserted-by":"crossref","unstructured":"I. M. De Diego, A. R. Redondo, R. R. Fern\u00e1ndez, J. Navarro, and J. M. Moguerza, \u201cGeneral performance score for classification problems,\u201d Appl. Intell., vol. 52, no. 2, pp. 12049\u201312063, 2022.","DOI":"10.1007\/s10489-021-03041-7"}],"container-title":["Paladyn"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0125\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0125\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T06:03:19Z","timestamp":1753855399000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/pjbr-2022-0125\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":22,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,10,29]]},"published-print":{"date-parts":[[2024,10,29]]}},"alternative-id":["10.1515\/pjbr-2022-0125"],"URL":"https:\/\/doi.org\/10.1515\/pjbr-2022-0125","relation":{},"ISSN":["2081-4836"],"issn-type":[{"type":"electronic","value":"2081-4836"}],"subject":[],"published":{"date-parts":[[2024,1,1]]},"article-number":"20220125"}}