{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T01:37:20Z","timestamp":1763429840848,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031212437"},{"type":"electronic","value":"9783031212444"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21244-4_6","type":"book-chapter","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T20:06:23Z","timestamp":1668110783000},"page":"71-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Applying Rough Set Theory for\u00a0Digital Forensics Evidence Analysis"],"prefix":"10.1007","author":[{"given":"Khushi","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Razaq","family":"Jinad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhou","family":"Bing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"6_CR1","unstructured":"Digforasp - digital forensics: evidence analysis via intelligent systems and practices. https:\/\/www.umu.se\/en\/research\/projects\/ca17124\u2013digital-forensics-evidence-analysis-via-intelligent-systems-and-practices\/"},{"key":"6_CR2","unstructured":"UCI machine learning repository: spambase data set. https:\/\/archive.ics.uci.edu\/ml\/datasets\/Spambase"},{"key":"6_CR3","unstructured":"Feature selection in python sklearn (2020). https:\/\/www.datacamp.com\/community\/tutorials\/feature-selection-python"},{"key":"6_CR4","unstructured":"An introduction to particle swarm optimization (PSO) algorithm (2021). https:\/\/www.analyticsvidhya.com\/blog\/2021\/10\/an-introduction-to-particle-swarm-optimization-algorithm\/"},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"10","DOI":"10.4236\/jcc.2016.49002","volume":"4","author":"Z Abbas","year":"2016","unstructured":"Abbas, Z., Burney, S.: A survey of software packages used for rough set analysis. J. Comput. Commun. 4, 10\u201318 (2016). https:\/\/doi.org\/10.4236\/jcc.2016.49002","journal-title":"J. Comput. Commun."},{"key":"6_CR6","doi-asserted-by":"publisher","unstructured":"Adedayo, O.M.: Big data and digital forensics. In: 2016 IEEE International Conference on Cybercrime and Computer Forensic (ICCCF), pp. 1\u20137 (2016). https:\/\/doi.org\/10.1109\/ICCCF.2016.7740422","DOI":"10.1109\/ICCCF.2016.7740422"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Al-Mayyan, W., Own, H.S., Zedan, H.: Rough set approach to online signature identification. Digit. Signal Process. 21(3), 477\u2013485. Elsevier (2011)","DOI":"10.1016\/j.dsp.2011.01.007"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Andhalkar, S., Momin, B.F.: Rough set theory and its extended algorithms. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1434\u20131438. IEEE (2018)","DOI":"10.1109\/ICCONS.2018.8663100"},{"key":"6_CR9","doi-asserted-by":"publisher","unstructured":"Bhattacharya, A., Goswami, R.T., Mukherjee, K.: A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of Android malwares. Int. J. Mach. Learn. Cybern. 10(7), 1893\u20131907. Springer (2019). https:\/\/doi.org\/10.1007\/s13042-018-0838-1","DOI":"10.1007\/s13042-018-0838-1"},{"issue":"6","key":"6_CR10","doi-asserted-by":"publisher","first-page":"172096","DOI":"10.1098\/rsos.172096","volume":"5","author":"M Clader","year":"2018","unstructured":"Clader, M., et al.: Computational modelling for decision-making: where, why, what, who and how. Roy. Soc. Open Sci. 5(6), 172096 (2018)","journal-title":"Roy. Soc. Open Sci."},{"key":"6_CR11","doi-asserted-by":"publisher","unstructured":"Chen, X.W., Jeong, J.C.: Enhanced recursive feature elimination. In: Sixth International Conference on Machine Learning and Applications (ICMLA 2007), pp. 429\u2013435 (2007). https:\/\/doi.org\/10.1109\/ICMLA.2007.35","DOI":"10.1109\/ICMLA.2007.35"},{"key":"6_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-642-16248-0_16","volume-title":"Rough Set and Knowledge Technology","author":"C Cornelis","year":"2010","unstructured":"Cornelis, C., Verbiest, N., Jensen, R.: Ordered weighted average based fuzzy rough sets. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS (LNAI), vol. 6401, pp. 78\u201385. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-16248-0_16"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Dawid, A.P., Mortera, J.: Forensic identification with imperfect evidence. Biometrika 85(4), 835\u2013849 (1998). https:\/\/www.jstor.org\/stable\/2337487. [Oxford University Press, Biometrika Trust]","DOI":"10.1093\/biomet\/85.4.835"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Han, J., Kamber, M., Pei, J.: 9 - Classification: advanced methods. In: Han, J., Kamber, M., Pei, J. (eds.) Data Mining (Third Edition), pp. 393\u2013442. The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, Boston (2012). https:\/\/doi.org\/10.1016\/B978-0-12-381479-1.00009-5, https:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780123814791000095","DOI":"10.1016\/B978-0-12-381479-1.00009-5"},{"key":"6_CR15","doi-asserted-by":"publisher","unstructured":"Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manage. Process 5, 01\u201311 (2015). https:\/\/doi.org\/10.5121\/ijdkp.2015.5201","DOI":"10.5121\/ijdkp.2015.5201"},{"key":"6_CR16","doi-asserted-by":"publisher","unstructured":"Ibrahim, D.: An overview of soft computing. Proc. Comput. Sci. 102, 34\u201338 (2016). https:\/\/doi.org\/10.1016\/j.procs.2016.09.366, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050916325467","DOI":"10.1016\/j.procs.2016.09.366"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Jensen, R., Shen, Q.: Tolerance-based and fuzzy-rough feature selection. In: 2007 IEEE International Fuzzy Systems Conference, pp. 1\u20136. IEEE (2007)","DOI":"10.1109\/FUZZY.2007.4295481"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Lang, R., Lu, H.: A general steganalysis method based on rough set theory. In: 2009 Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics (PrimeAsia), pp. 241\u2013244. IEEE (2009)","DOI":"10.1109\/PRIMEASIA.2009.5397401"},{"issue":"3","key":"6_CR19","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s00500-009-0531-0","volume":"15","author":"S Lian","year":"2011","unstructured":"Lian, S., Heileman, G.L., Noore, A.: Special issue on soft computing for digital information forensics. Soft. Comput. 15(3), 413\u2013415 (2011). https:\/\/doi.org\/10.1007\/s00500-009-0531-0","journal-title":"Soft. Comput."},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Ma, Y., Luo, X., Li, X., Bao, Z., Zhang, Y.: Selection of rich model steganalysis features based on decision rough set $$\\alpha $$ -positive region reduction. IEEE Trans. Circ. Syst. Video Technol. 29(2), 336\u2013350, IEEE (2018)","DOI":"10.1109\/TCSVT.2018.2799243"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Mohtashami, M., Eftekhari, M.: Using a novel merit for feature selection based on rough set theory. In: 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 68\u201370. IEEE (2018)","DOI":"10.1109\/CFIS.2018.8336632"},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ijar.2018.01.008","volume":"97","author":"Y Qian","year":"2018","unstructured":"Qian, Y., et al.: Local rough set: a solution to rough data analysis in big data. Int. J. Approximate Reasoning 97, 38\u201363 (2018). https:\/\/doi.org\/10.1016\/j.ijar.2018.01.008","journal-title":"Int. J. Approximate Reasoning"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Quick, D., Choo, K.K.R.: Impacts of increasing volume of digital forensic data: a survey and future research challenges. Digit. Investig. 11(4), 273\u2013294, Elsevier (2014)","DOI":"10.1016\/j.diin.2014.09.002"},{"key":"6_CR24","unstructured":"robin.materese@nist.gov: Digital evidence (2016). https:\/\/www.nist.gov\/digital-evidence. Accessed 25 Oct 2021"},{"key":"6_CR25","doi-asserted-by":"publisher","unstructured":"Shen, Q., Jensen, R.: Rough sets, their extensions and applications. Int. J. Autom. Comput. 4(3), 217\u2013228. Springer (2007). https:\/\/doi.org\/10.1007\/s11633-007-0217-y","DOI":"10.1007\/s11633-007-0217-y"},{"issue":"3","key":"6_CR26","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s13042-017-0699-z","volume":"10","author":"AK Singh","year":"2017","unstructured":"Singh, A.K., Baranwal, N., Nandi, G.C.: A rough set based reasoning approach for criminal identification. Int. J. Mach. Learn. Cybern. 10(3), 413\u2013431 (2017). https:\/\/doi.org\/10.1007\/s13042-017-0699-z","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"4","key":"6_CR27","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/s11047-018-9700-3","volume":"17","author":"A Skowron","year":"2018","unstructured":"Skowron, A., Dutta, S.: Rough sets: past, present, and future. Nat. Comput. 17(4), 855\u2013876 (2018). https:\/\/doi.org\/10.1007\/s11047-018-9700-3","journal-title":"Nat. Comput."},{"key":"6_CR28","doi-asserted-by":"publisher","unstructured":"Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft Comput. 22(2), 387\u2013408. Springer (2018). https:\/\/doi.org\/10.1007\/s00500-016-2474-6","DOI":"10.1007\/s00500-016-2474-6"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Lee, H.C.: Research on some relevant problems in computer forensics. In: Conference of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), pp. 1564\u20131571. Atlantis Press (2013)","DOI":"10.2991\/iccsee.2013.393"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, M., Yao, J.T.: A rough sets based approach to feature selection. In: IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS 2004, vol. 1, pp. 434\u2013439. IEEE (2004)","DOI":"10.1109\/NAFIPS.2004.1336322"},{"key":"6_CR31","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Xie, Q., Wang, G.: A survey on rough set theory and its applications. CAAI Trans. Intell. Technol. 1(4), 323\u2013333 (2016). https:\/\/doi.org\/10.1016\/j.trit.2016.11.001, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2468232216300786","DOI":"10.1016\/j.trit.2016.11.001"},{"key":"6_CR32","doi-asserted-by":"publisher","unstructured":"Zhang, T., Zhao, P.: Insider threat identification system model based on rough set dimensionality reduction. In: 2010 Second World Congress on Software Engineering, vol. 2, pp. 111\u2013114 (2010). https:\/\/doi.org\/10.1109\/WCSE.2010.106","DOI":"10.1109\/WCSE.2010.106"}],"container-title":["Lecture Notes in Computer Science","Rough Sets"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21244-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:05:10Z","timestamp":1709831110000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21244-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031212437","9783031212444"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21244-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IJCRS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Rough Sets","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Suzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcrs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ijcrs2022.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}