{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:39:19Z","timestamp":1781102359730,"version":"3.54.1"},"reference-count":24,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<p>The volume of data in diverse data formats from various data sources has led the way for a new drift in the digital world, Big Data. This article proposes sl-LSTM (sequence labelling LSTM), a neural network architecture that combines the effectiveness of typical LSTM models to perform sequence labeling tasks. This is a bi-directional LSTM which uses stochastic gradient descent optimization and combines two features of the existing LSTM variants: coupled input-forget gates for reducing the computational complexity and peephole connections that allow all gates to inspect the current cell state. The model is tested on different datasets and the results show that the integration of various neural network models can further improve the efficiency of approach for identifying sensitive information in Big data.<\/p>","DOI":"10.4018\/ijghpc.2020070101","type":"journal-article","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T10:10:56Z","timestamp":1592907056000},"page":"1-16","source":"Crossref","is-referenced-by-count":3,"title":["sl-LSTM"],"prefix":"10.4018","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0640-5768","authenticated-orcid":true,"given":"Nancy","family":"Victor","sequence":"first","affiliation":[{"name":"Vellore Institute of Technology, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1452-2144","authenticated-orcid":true,"given":"Daphne","family":"Lopez","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJGHPC.2020070101-0","first-page":"177","article-title":"Large-scale machine learning with stochastic gradient descent.","author":"L.Bottou","year":"2010","journal-title":"Proceedings of COMPSTAT"},{"key":"IJGHPC.2020070101-1","unstructured":"Camron, G. (2016). Recurrent Neural Networks for Beginners. Retrieved from https:\/\/medium.com\/@camrongodbout\/recurrent-neural-networks-for-beginners-7aca4e933b82"},{"key":"IJGHPC.2020070101-2","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation.","DOI":"10.3115\/v1\/D14-1179"},{"issue":"177","key":"IJGHPC.2020070101-3","first-page":"5","article-title":"Introduction to information retrieval.","volume":"151","author":"D. M.Christopher","year":"2008","journal-title":"An Introduction To Information Retrieval"},{"key":"IJGHPC.2020070101-4","doi-asserted-by":"publisher","DOI":"10.3115\/1610075.1610158"},{"key":"IJGHPC.2020070101-5","doi-asserted-by":"crossref","unstructured":"Dernoncourt, F., Lee, J. Y., & Szolovits, P. (2017). NeuroNER: an easy-to-use program for named-entity recognition based on neural networks.","DOI":"10.18653\/v1\/D17-2017"},{"key":"IJGHPC.2020070101-6","doi-asserted-by":"crossref","unstructured":"Gers, F. A., Schmidhuber, J., & Cummins, F. (1999). Learning to forget: Continual prediction with LSTM.","DOI":"10.1049\/cp:19991218"},{"issue":"Aug","key":"IJGHPC.2020070101-7","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks.","volume":"3","author":"F. A.Gers","year":"2002","journal-title":"Journal of Machine Learning Research"},{"key":"IJGHPC.2020070101-8","unstructured":"Glorot, X., & Bengio, Y. (2010, March). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249-256). Academic Press."},{"key":"IJGHPC.2020070101-9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"IJGHPC.2020070101-10","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"IJGHPC.2020070101-11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"IJGHPC.2020070101-12","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553435"},{"key":"IJGHPC.2020070101-13","unstructured":"Jozefowicz, R., Zaremba, W., & Sutskever, I. (2015, June). An empirical exploration of recurrent network architectures. Proceedings of theInternational Conference on Machine Learning (pp. 2342-2350). Academic Press."},{"key":"IJGHPC.2020070101-14","unstructured":"Ma, M. (2006). Automatic conversion of natural language to 3D animation [Doctoral dissertation]. University of Ulster."},{"key":"IJGHPC.2020070101-15","unstructured":"Olah, C. (2015). Understanding LSTM Networks. Retrieved from http:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/"},{"key":"IJGHPC.2020070101-16","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"IJGHPC.2020070101-17","unstructured":"Powers, D. M. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation."},{"key":"IJGHPC.2020070101-18","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"issue":"1","key":"IJGHPC.2020070101-19","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting.","volume":"15","author":"N.Srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"IJGHPC.2020070101-20","doi-asserted-by":"publisher","DOI":"10.3115\/1119176.1119195"},{"key":"IJGHPC.2020070101-21","unstructured":"Vallor, S. (2016). Social Networking and Ethics. In The Stanford Encyclopedia of Philosophy. Academic Press. Retrieved from https:\/\/plato.stanford.edu\/archives\/win2016\/entries\/ethics-social-networking\/"},{"key":"IJGHPC.2020070101-22","doi-asserted-by":"crossref","unstructured":"Victor, N., & Lopez, D. (2018). Privacy Preserving Big Data Publishing: Challenges, Techniques, and Architectures. In HCI Challenges and Privacy Preservation in Big Data Security (pp. 47-70). Hershey, PA: IGI Global.","DOI":"10.4018\/978-1-5225-2863-0.ch003"},{"key":"IJGHPC.2020070101-23","doi-asserted-by":"publisher","DOI":"10.1504\/IJBDI.2016.073904"}],"container-title":["International Journal of Grid and High Performance Computing"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=257221","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T19:53:25Z","timestamp":1651866805000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJGHPC.2020070101"}},"subtitle":["A Bi-Directional LSTM With Stochastic Gradient Descent Optimization for Sequence Labeling Tasks in Big Data"],"short-title":[],"issued":{"date-parts":[[2020,7,1]]},"references-count":24,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,7]]}},"URL":"https:\/\/doi.org\/10.4018\/ijghpc.2020070101","relation":{},"ISSN":["1938-0259","1938-0267"],"issn-type":[{"value":"1938-0259","type":"print"},{"value":"1938-0267","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,1]]}}}