{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:34:34Z","timestamp":1754156074789,"version":"3.41.2"},"reference-count":40,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T00:00:00Z","timestamp":1595462400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2020,7,23]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This paper aims to model a technique that categorizes the texts from huge documents. The progression in internet technologies has raised the count of document accessibility, and thus the documents available online become countless. The text documents comprise of research article, journal papers, newspaper, technical reports and blogs. These large documents are useful and valuable for processing real-time applications. Also, these massive documents are used in several retrieval methods. Text classification plays a vital role in information retrieval technologies and is considered as an active field for processing massive applications. The aim of text classification is to categorize the large-sized documents into different categories on the basis of its contents. There exist numerous methods for performing text-related tasks such as profiling users, sentiment analysis and identification of spams, which is considered as a supervised learning issue and is addressed with text classifier.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>At first, the input documents are pre-processed using the stop word removal and stemming technique such that the input is made effective and capable for feature extraction. In the feature extraction process, the features are extracted using the vector space model (VSM) and then, the feature selection is done for selecting the highly relevant features to perform text categorization. Once the features are selected, the text categorization is progressed using the deep belief network (DBN). The training of the DBN is performed using the proposed grasshopper crow optimization algorithm (GCOA) that is the integration of the grasshopper optimization algorithm (GOA) and Crow search algorithm (CSA). Moreover, the hybrid weight bounding model is devised using the proposed GCOA and range degree. Thus, the proposed GCOA + DBN is used for classifying the text documents.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The performance of the proposed technique is evaluated using accuracy, precision and recall is compared with existing techniques such as naive bayes, k-nearest neighbors, support vector machine and deep convolutional neural network (DCNN) and Stochastic Gradient-CAViaR + DCNN. Here, the proposed GCOA + DBN has improved performance with the values of 0.959, 0.959 and 0.96 for precision, recall and accuracy, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper proposes a technique that categorizes the texts from massive sized documents. From the findings, it can be shown that the proposed GCOA-based DBN effectively classifies the text documents.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijwis-03-2020-0015","type":"journal-article","created":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T13:54:25Z","timestamp":1596030865000},"page":"347-368","source":"Crossref","is-referenced-by-count":6,"title":["Optimized deep belief network and entropy-based hybrid bounding model for incremental text categorization"],"prefix":"10.1108","volume":"16","author":[{"given":"V.","family":"Srilakshmi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Anuradha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. Shoba","family":"Bindu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020100707571192700_ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","article-title":"A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm","volume":"169","year":"2016","journal-title":"Computers and Structures"},{"key":"key2020100707571192700_ref002","doi-asserted-by":"crossref","first-page":"115134","DOI":"10.1109\/ACCESS.2019.2935416","article-title":"Using the Tsetlin machine to learn human-interpretable rules for high-accuracy text categorization with medical applications","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"key2020100707571192700_ref003","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/1031171.1031186","article-title":"Hierarchical document classification with support vector machines","volume-title":"Proceedings of the thirteenth ACM international conference on information and knowledge management","year":"2004"},{"key":"key2020100707571192700_ref004","first-page":"47","article-title":"Italian text categorization with lemmatization and support vector machines","volume":"151","year":"2019","journal-title":"Neural Approaches to Dynamics of Signal Exchanges"},{"issue":"6","key":"key2020100707571192700_ref005","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1137\/S0097539702418498","article-title":"Incremental clustering and dynamic information retrieval","volume":"33","year":"2004","journal-title":"SIAM Journal on Computing"},{"key":"key2020100707571192700_ref006","first-page":"546","article-title":"Text feature selection based on water wave optimization algorithm","volume-title":"proceedings of Tenth International Conference on Advanced Computational Intelligence (ICACI)","year":"2018"},{"article-title":"On hybridizing fuzzy min max neural network and firefly algorithm for automated heart disease diagnosis","volume-title":"the proceeding of Fourth International Conference on Computing, Communications and Networking Technologies, Tiruchengode, India, July","year":"2013","key":"key2020100707571192700_ref007"},{"journal-title":"The Computer Journal","article-title":"Weighed query-specific distance and hybrid NARX neural network for video object retrieval","year":"2019","key":"key2020100707571192700_ref008"},{"issue":"7","key":"key2020100707571192700_ref009","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","year":"2006","journal-title":"Neural Computation"},{"article-title":"Improving K nearest neighbor into string vector version for text categorization","volume-title":"the proceeding of 21st International Conference on Advanced Communication Technology (ICACT)","year":"2019","key":"key2020100707571192700_ref010"},{"key":"key2020100707571192700_ref011","first-page":"137","article-title":"Text categorization with support vector machines: learning with many relevant features","volume-title":"proceedings of European conference on machine learning","year":"1998"},{"key":"key2020100707571192700_ref012","article-title":"Trigonometric comparison measure: a feature selection method for text categorization","volume":"119","year":"2018","journal-title":"Data and Knowledge Engineering"},{"issue":"11","key":"key2020100707571192700_ref013","article-title":"Some effective techniques for navive Bayes text classification","volume":"18","year":"2006","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2020100707571192700_ref014","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.engappai.2017.12.014","article-title":"A novel multivariate filter method for feature selection in text classification problems","volume":"70","year":"2018","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"key2020100707571192700_ref015","article-title":"Memetic feature selection for multilabel text categorization using label frequency difference","volume":"485","year":"2019","journal-title":"Information Information Sciences"},{"issue":"1","key":"key2020100707571192700_ref016","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1109\/TASL.2006.876860","article-title":"A vector space modeling approach to spoken language identification","volume":"15","year":"2007","journal-title":"IEEE Transactions on Audio, Speech and Language Processing"},{"key":"key2020100707571192700_ref017","first-page":"71","article-title":"Data clustering with grasshopper optimization algorithm","volume-title":"2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague","year":"2017"},{"issue":"10","key":"key2020100707571192700_ref018","doi-asserted-by":"crossref","first-page":"2642","DOI":"10.1109\/TITS.2017.2656387","article-title":"Delivering real-time information services on public transit: a framework","volume":"18","year":"2017","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"1","key":"key2020100707571192700_ref019","article-title":"Arabic text categorization using support vector machine","volume":"5","year":"2018","journal-title":"GSTF Journal on Computing (Joc))"},{"key":"key2020100707571192700_ref020","unstructured":"Newsgroup database (2018), http:\/\/qwone.com\/\u223cjason\/20Newsgroups\/, accessed on October."},{"issue":"1","key":"key2020100707571192700_ref021","first-page":"17","article-title":"Multiple feature sets and SVM classifier for the detection of diabetic retinopathy using retinal images","volume":"1","year":"2018","journal-title":"Multimedia Research (MR)"},{"journal-title":"IEEE Transactions on Cybernetics","article-title":"Incremental class learning for hierarchical classification","year":"2018","key":"key2020100707571192700_ref022"},{"key":"key2020100707571192700_ref023","article-title":"LFNN: Lion fuzzy neural network-based evolutionary model for text classification using context and sense based features","volume":"71","year":"2018","journal-title":"Applied Soft Computing"},{"key":"key2020100707571192700_ref024","unstructured":"Reuter database (2018), https:\/\/archive.ics.uci.edu\/ml\/datasets\/reuters-8+text+categorization+collection accessed on October 2018."},{"key":"key2020100707571192700_ref025","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.eswa.2018.07.049","article-title":"Incremental personalized E-mail spam filter using novel TFDCR feature selection with dynamic feature update","volume":"115","year":"2019","journal-title":"Expert Systems with Applications"},{"issue":"11","key":"key2020100707571192700_ref026","doi-asserted-by":"crossref","first-page":"2758","DOI":"10.1109\/78.650102","article-title":"Comparing support vector machines with gaussian kernels to radial basis function classifiers","volume":"45","year":"1997","journal-title":"IEEE Transactions on Signal Processing"},{"journal-title":"arXiv Preprint arXiv:1709.08716","article-title":"Doc: Deep open classification of text documents","year":"2017","key":"key2020100707571192700_ref027"},{"key":"key2020100707571192700_ref028","first-page":"247","article-title":"Hierarchical text classification incremental learning","volume-title":"proceedings of International Conference on Neural Information Processing ICONIP, Neural Information Processing","year":"2009"},{"key":"key2020100707571192700_ref029","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.cmpb.2019.01.011","article-title":"Effect of incremental feature enrichment on healthcare text classification system: a machine learning paradigm","volume":"172","year":"2019","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"6","key":"key2020100707571192700_ref030","first-page":"484","volume":"3","year":"2013","journal-title":"Improving Login Authorization by Providing Graphical Password (Security)"},{"issue":"1","key":"key2020100707571192700_ref031","first-page":"99","article-title":"K nearest neighbor for text categorization using feature similarity","volume":"2","year":"2019","journal-title":"ICAEIC-2019"},{"issue":"9","key":"key2020100707571192700_ref032","doi-asserted-by":"crossref","first-page":"2508","DOI":"10.1109\/TKDE.2016.2563436","article-title":"Toward optimal feature selection in naive Bayes for text categorization","volume":"28","year":"2016","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"key2020100707571192700_ref033","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.knosys.2018.03.003","article-title":"An automated text categorization framework based on hyperparameter optimization","volume":"149","year":"2018","journal-title":"Knowledge-Based Systems"},{"volume-title":"Text Categorization Using k Nearest Neighbor Classification","year":"2013","key":"key2020100707571192700_ref034"},{"key":"key2020100707571192700_ref035","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.asoc.2015.04.044","article-title":"Incremental learning with partial-supervision based on hierarchical Dirichlet process and the application for document classification","volume":"33","year":"2015","journal-title":"Applied Soft Computing"},{"issue":"10","key":"key2020100707571192700_ref036","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1109\/TIFS.2018.2825952","article-title":"Efficient retrieval over documents encrypted by attributes in cloud computing","volume":"13","year":"2018","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"11","key":"key2020100707571192700_ref037","first-page":"1","article-title":"New incremental learning algorithm with support vector machines","volume":"49","year":"2018","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"key2020100707571192700_ref038","first-page":"412","article-title":"A comparative study on feature selection in text categorization","volume-title":"proceedings of International Conference on Machine Learning","year":"1997"},{"journal-title":"arXiv Preprint arXiv:1803.00159","article-title":"A class-incremental learning method based on one class support vector machine","year":"2018","key":"key2020100707571192700_ref039"},{"issue":"16","key":"key2020100707571192700_ref040","doi-asserted-by":"crossref","first-page":"16875","DOI":"10.1007\/s11042-016-3545-5","article-title":"Maximum entropy model for mobile text classification in cloud computing using improved information gain algorithm","volume":"76","year":"2017","journal-title":"Multimedia Tools and Applications"}],"container-title":["International Journal of Web Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-03-2020-0015\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-03-2020-0015\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:23:49Z","timestamp":1753395829000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijwis\/article\/16\/3\/347-368\/165656"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,23]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,7,23]]}},"alternative-id":["10.1108\/IJWIS-03-2020-0015"],"URL":"https:\/\/doi.org\/10.1108\/ijwis-03-2020-0015","relation":{},"ISSN":["1744-0084","1744-0084"],"issn-type":[{"type":"print","value":"1744-0084"},{"type":"print","value":"1744-0084"}],"subject":[],"published":{"date-parts":[[2020,7,23]]}}}