{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:21:41Z","timestamp":1742937701073,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030656201"},{"type":"electronic","value":"9783030656218"}],"license":[{"start":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T00:00:00Z","timestamp":1607731200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T00:00:00Z","timestamp":1607731200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-65621-8_20","type":"book-chapter","created":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T12:32:07Z","timestamp":1607689927000},"page":"299-307","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Study the Significance of ML-ELM Using Combined PageRank and Content-Based Feature Selection"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6295-262X","authenticated-orcid":false,"given":"Rajendra Kumar","family":"Roul","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6104-5171","authenticated-orcid":false,"given":"Jajati Keshari","family":"Sahoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,12]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Du, J., Vong, C.-M., Chen, C. P.: Novel efficient RNN and LSTM-like architectures: Recurrent and gated broad learning systems and their applications for text classification. IEEE Trans. Cybern. (2020)","key":"20_CR1","DOI":"10.1109\/TCYB.2020.2969705"},{"issue":"4","key":"20_CR2","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s41060-018-0146-6","volume":"7","author":"R Sambasivan","year":"2019","unstructured":"Sambasivan, R., Das, S.: Classification and regression using augmented trees. Int. J. Data Sci. Anal. 7(4), 259\u2013276 (2019)","journal-title":"Int. J. Data Sci. Anal."},{"issue":"8","key":"20_CR3","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1007\/s00500-018-3645-4","volume":"23","author":"SIT Joseph","year":"2019","unstructured":"Joseph, S.I.T., Sasikala, J., Juliet, D.S.: A novel vessel detection and classification algorithm using a deep learning neural network model with morphological processing (m-dlnn). Soft Comput. 23(8), 2693\u20132700 (2019)","journal-title":"Soft Comput."},{"issue":"15","key":"20_CR4","doi-asserted-by":"publisher","first-page":"4239","DOI":"10.1007\/s00500-016-2189-8","volume":"21","author":"RK Roul","year":"2017","unstructured":"Roul, R.K., Asthana, S.R., Kumar, G.: Study on suitability and importance of multilayer extreme learning machine for classification of text data. Soft Comput. 21(15), 4239\u20134256 (2017)","journal-title":"Soft Comput."},{"issue":"1","key":"20_CR5","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/s00521-017-2988-6","volume":"31","author":"GI Sayed","year":"2019","unstructured":"Sayed, G.I., Hassanien, A.E., Azar, A.T.: Feature selection via a novel chaotic crow search algorithm. Neural Comput. Appl. 31(1), 171\u2013188 (2019)","journal-title":"Neural Comput. Appl."},{"issue":"15","key":"20_CR6","first-page":"1","volume":"23","author":"C-J Tsai","year":"2019","unstructured":"Tsai, C.-J.: New feature selection and voting scheme to improve classification accuracy. Soft Comput. 23(15), 1\u201314 (2019)","journal-title":"Soft Comput."},{"unstructured":"Roul, R.K., Rai, P.: A new feature selection technique combined with elm feature space for text classification. In: Proceedings of the 13th International Conference on Natural Language Processing, pp. 285\u2013292 (2016)","key":"20_CR7"},{"issue":"6","key":"20_CR8","first-page":"31","volume":"28","author":"LLC Kasun","year":"2013","unstructured":"Kasun, L.L.C., Zhou, H., Huang, G.-B., Vong, C.M.: Representational learning with extreme learning machine for big data. IEEE Intell. Syst. 28(6), 31\u201334 (2013)","journal-title":"IEEE Intell. Syst."},{"issue":"2","key":"20_CR9","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G-B Huang","year":"2012","unstructured":"Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(2), 513\u2013529 (2012)","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"issue":"4","key":"20_CR10","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/TNN.2006.875977","volume":"17","author":"G-B Huang","year":"2006","unstructured":"Huang, G.-B., Chen, L., Siew, C.K., et al.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17(4), 879\u2013892 (2006)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1\u20132","key":"20_CR11","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1504\/IJCISTUDIES.2019.098017","volume":"8","author":"RK Roul","year":"2019","unstructured":"Roul, R.K.: Suitability and importance of deep learning feature space in the domain of text categorisation. Int. J. Comput. Intell. Stud. 8(1\u20132), 73\u2013102 (2019)","journal-title":"Int. J. Comput. Intell. Stud."},{"unstructured":"Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Tech. Rep, Stanford InfoLab (1999)","key":"20_CR12"},{"issue":"2","key":"20_CR13","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G-B Huang","year":"2011","unstructured":"Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: \"Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Part B Cybern.(Cybern.) 42(2), 513\u2013529 (2011)","journal-title":"IEEE Trans. Syst. Man Part B Cybern.(Cybern.)"},{"issue":"3","key":"20_CR14","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1109\/72.846750","volume":"11","author":"G-B Huang","year":"2000","unstructured":"Huang, G.-B., Chen, Y.-Q., Babri, H.A.: Classification ability of single hidden layer feedforward neural networks. IEEE Trans. Neural Netw. 11(3), 799\u2013801 (2000)","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Distributed Computing and Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65621-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:26:19Z","timestamp":1607991979000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-65621-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,12]]},"ISBN":["9783030656201","9783030656218"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65621-8_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020,12,12]]},"assertion":[{"value":"12 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDCIT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Distributed Computing and Internet Technology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bhubaneswar","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdcit2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdcit.ac.in\/17th-icdcit-2021\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","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":"13","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":"4","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":"13% - 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":"2.5","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":"6","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)"}}]}}