{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T19:49:16Z","timestamp":1772567356309,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>One of the effectual text classification approaches for learning extensive information is incremental learning. The big issue that occurs is enhancing the accuracy, as the text is comprised of a large number of terms. In order to address this issue, a new incremental text classification approach is designed using the proposed hybrid optimization algorithm named the Henry Fuzzy Competitive Multi-verse Optimizer (HFCVO)-based Deep Maxout Network (DMN). Here, the optimal features are selected using Invasive Weed Tunicate Swarm Optimization (IWTSO), which is devised by integrating Invasive Weed Optimization (IWO) and the Tunicate Swarm Algorithm (TSA), respectively. The incremental text classification is effectively performed using the DMN, where the classifier is trained utilizing the HFCVO. Nevertheless, the developed HFCVO is derived by incorporating the features of Henry Gas Solubility Optimization (HGSO) and the Competitive Multi-verse Optimizer (CMVO) with fuzzy theory. The proposed HFCVO-based DNM achieved a maximum TPR of 0.968, a maximum TNR of 0.941, a low FNR of 0.032, a high precision of 0.954, and a high accuracy of 0.955.<\/jats:p>","DOI":"10.3390\/computation11010013","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T01:32:26Z","timestamp":1673487146000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["HFCVO-DMN: Henry Fuzzy Competitive Verse Optimizer-Integrated Deep Maxout Network for Incremental Text Classification"],"prefix":"10.3390","volume":"11","author":[{"given":"Gunjan","family":"Singh","sequence":"first","affiliation":[{"name":"School of Engineering and Science, G. D. Goenka University, Gurugram, Goenka Educational City, Sohna-Gurgaon Rd., Sohna 122103, Haryana, India"}]},{"given":"Arpita","family":"Nagpal","sequence":"additional","affiliation":[{"name":"School of Engineering and Science, G. D. Goenka University, Gurugram, Goenka Educational City, Sohna-Gurgaon Rd., Sohna 122103, Haryana, India"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yan, Y., Tao, Y., Jin, S., Xu, J., and Lin, H. (2019, January 23\u201326). An Interactive Visual Analytics System for Incremental Classification Based on Semi-supervised Topic Modeling. Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), Bangkok, Thailand.","DOI":"10.1109\/PacificVis.2019.00025"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.aej.2016.12.013","article-title":"Multi kernel and dynamic fractional lion optimization algorithm for data clustering","volume":"57","author":"Chander","year":"2018","journal-title":"Alex. Eng. 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