{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:26:03Z","timestamp":1754155563568,"version":"3.41.2"},"reference-count":20,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2017,4,3]],"date-time":"2017-04-03T00:00:00Z","timestamp":1491177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2017,4,3]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title>\n<jats:p>The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-09-2016-0228","type":"journal-article","created":{"date-parts":[[2017,4,3]],"date-time":"2017-04-03T04:03:09Z","timestamp":1491192189000},"page":"693-705","source":"Crossref","is-referenced-by-count":11,"title":["Deep learning architecture using rough sets and rough neural networks"],"prefix":"10.1108","volume":"46","author":[{"given":"Yasser F.","family":"Hassan","sequence":"first","affiliation":[]}],"member":"140","reference":[{"key":"key2020120800053435900_ref012","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TSMC.2014.2378215","article-title":"Analysis of a heterogeneous social network of humans and cultural objects","volume":"45","year":"2015","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"key2020120800053435900_ref019","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patrec.2015.06.003","article-title":"Scene analysis by mid-level attribute learning using 2D LSTM networks and an application to web-image tagging","volume":"63","year":"2015","journal-title":"Pattern Recognition Letters"},{"key":"key2020120800053435900_ref005","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.asoc.2016.04.003","article-title":"Incremental algorithm for attribute reduction with variable precision rough sets","volume":"45","year":"2016","journal-title":"Applied Soft Computing"},{"key":"key2020120800053435900_ref003","first-page":"26","article-title":"Ensembles of deep learning architectures for the early diagnosis of Alzheimer\u2019s Disease","volume":"26","year":"2016","journal-title":"International Journal of Neural Systems"},{"key":"key2020120800053435900_ref014","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1109\/TSMC.2015.2478401","article-title":"A New method for intuitionistic fuzzy multi-attribute decision making","volume":"46","year":"2016","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"key2020120800053435900_ref015","first-page":"752","article-title":"Rough neural networks in adapting cellular automata rule for reducing image noise","volume":"8","year":"2014","journal-title":"International Journal of Computer, Information, Science and Engineering"},{"key":"key2020120800053435900_ref007","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1142\/S012906570200131X","article-title":"Decision making using hybrid rough sets and neural networks","volume":"12","year":"2002","journal-title":"International Journal of Neural Systems"},{"key":"key2020120800053435900_ref016","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.socnet.2015.04.011","article-title":"Learning in social networks: selecting profitable choices among alternatives of uncertain profitability in various networks","volume":"43","year":"2015","journal-title":"Social Networks"},{"key":"key2020120800053435900_ref002","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.knosys.2016.03.018","article-title":"Image matting in the perception granular deep learning","volume":"102","year":"2016","journal-title":"Knowledge-Based Systems"},{"key":"key2020120800053435900_ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijar.2015.12.014","article-title":"Optimal approximations with Rough Sets and similarities in measure spaces","volume":"71","year":"2016","journal-title":"International Journal of Approximate Reasoning"},{"key":"key2020120800053435900_ref006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.socnet.2016.04.001","article-title":"Effects of competition on collective learning in advice networks","volume":"47","year":"2016","journal-title":"Social Networks"},{"key":"key2020120800053435900_ref018","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.techfore.2016.07.032","article-title":"Social network analysis: a tool for evaluating and predicting future knowledge flows from an insurance organization","volume":"114","year":"2017","journal-title":"Technological Forecasting & Social Change"},{"issue":"6","key":"key2020120800053435900_ref017","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1016\/j.ijinfomgt.2016.06.009","article-title":"Enterprise social networking: a knowledge management perspective","volume":"36","year":"2016","journal-title":"International Journal of Information Management"},{"key":"key2020120800053435900_ref008","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ijar.2016.01.002","article-title":"Multi-objective optimization method for learning thresholds in a decision-theoretic rough set model","volume":"71","year":"2016","journal-title":"International Journal of Approximate Reasoning"},{"key":"key2020120800053435900_ref010","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.knosys.2015.07.021","article-title":"Proximal three-way decisions: theory and applications in social networks","volume":"91","year":"2016","journal-title":"Knowledge-Based Systems"},{"key":"key2020120800053435900_ref011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSMC.2013.2238922","article-title":"Infusing social networks with culture","volume":"44","year":"2014","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"key2020120800053435900_ref013","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/TSMC.2015.2468192","article-title":"Satellite objects extraction and classification based on similarity measure","volume":"46","year":"2016","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"key2020120800053435900_ref020","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.imavis.2012.03.001","article-title":"LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework","volume":"31","year":"2013","journal-title":"Image and Vision Computing"},{"key":"key2020120800053435900_ref001","first-page":"308","article-title":"Learning deep representations via extreme learning machines","volume":"149","year":"2015","journal-title":"Neuro Computing"},{"key":"key2020120800053435900_ref004","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.ijar.2013.04.006","article-title":"Multi-class decision-theoretic rough sets","volume":"55","year":"2014","journal-title":"International Journal of Approximate Reasoning"}],"container-title":["Kybernetes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-09-2016-0228\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-09-2016-0228\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:50:03Z","timestamp":1753393803000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/k\/article\/46\/4\/693-705\/516241"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,3]]},"references-count":20,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,4,3]]}},"alternative-id":["10.1108\/K-09-2016-0228"],"URL":"https:\/\/doi.org\/10.1108\/k-09-2016-0228","relation":{},"ISSN":["0368-492X"],"issn-type":[{"type":"print","value":"0368-492X"}],"subject":[],"published":{"date-parts":[[2017,4,3]]}}}