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Journal articles on the topic 'Document optimization'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Et. al., Tamilselvan Jayaraman,. "Brainstorm optimization for multi-document summarization." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 7607–19. http://dx.doi.org/10.17762/turcomat.v12i10.5670.

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Document summarization is one of the solutions to mine the appropriate information from a huge number of documents. In this study, brainstorm optimization (BSO) based multi-document summarizer (MDSBSO) is proposed to solve the problem of multi-document summarization. The proposed MDSBSO is compared with two other multi-document summarization algorithms including particle swarm optimization (PSO) and bacterial foraging optimization (BFO). To evaluate the performance of proposed multi-document summarizer, two well-known benchmark document understanding conference (DUC) datasets are used. Perform

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K. Adi Narayana Reddy. "Multi-Document Summarization using Discrete Bat Optimization." Journal of Electrical Systems 20, no. 7s (2024): 831–42. http://dx.doi.org/10.52783/jes.3457.

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With the World Wide Web, we now have a wide range of data that was previously unavailable. Therefore, it has become a complex problem to find useful information in large datasets. In recent years, text summarization has emerged as a viable option for mining relevant data from massive collections of texts. We may classify summarizing as either "single document" or "multi document" depending on how many source documents we are working with. Finding an accurate summary from a collection of documents is more difficult for researchers than doing it from a single document. For this reason, this rese

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Slavin, O. A. "Optimizing the performance of a server-based classification for a large business document flow." «System analysis and applied information science», no. 4 (February 24, 2023): 60–64. http://dx.doi.org/10.21122/2309-4923-2022-4-60-64.

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The document categorization problem in the case of a large business document flow is considered. Textual and visual embeddings were employed for classification. Textual embeddings were extracted via OCR Tesseract. The Viola and Jones method was applied to generate visual embeddings. This paper describes the performance optimization technology for the implemented classification algorithm. Servers with Intel CPUs were used for the algorithm execution. For single-threaded implementation, high-level and low-level optimizations were performed. High-level optimization was based on the parametrizatio

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Cheng, Wen Zhi, Yi Yang, Liao Zhang, and Lian Li. "Optimization for Web-Based Online Document Management." Advanced Materials Research 756-759 (September 2013): 1135–40. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1135.

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In this paper, we construct a web-based document life-cycle management model. The model manages documents which consist of the institute library from their creation to the archive state. For an online office system, we aim at solving three issues: network delay, version storage problems and deletion strategy. To solve network delay, we propose both local and online document synchronized editing model. In addition, we combine the longest recursive chain with recursive chain time to optimize the system response time. In order to optimize documents to be deleted, we propose a two-step optimized m

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Jisheng Liang, I. T. Phillips, and R. M. Haralick. "An optimization methodology for document structure extraction on Latin character documents." IEEE Transactions on Pattern Analysis and Machine Intelligence 23, no. 7 (2001): 719–34. http://dx.doi.org/10.1109/34.935846.

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Orlov, I. V. "Role of document management system for business processes optimization." Problems of Theory and Methodology of Accounting, Control and Analysis, no. 2(58) (September 16, 2024): 45–49. http://dx.doi.org/10.26642/pbo-2024-2(58)-45-49.

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The automation of work processes and seamless integration with various business services highlight the essential role of digital document management systems (DMS) in enhancing organizational flexibility, productivity, and adaptability. DMS serve as centralized repositories that streamline, automate, and interconnect business processes, thereby facilitating effective document management. The integration of DMS with Enterprise Resource Planning (ERP) systems provides a comprehensive solution for optimizing business workflows, addressing the needs for both operational efficiency and strategic man

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T. Elavarasi. "Spectral Clustering-Based Particle Swarm Optimization Algorithm for Document Clustering." Journal of Information Systems Engineering and Management 10, no. 4s (2025): 134–46. https://doi.org/10.52783/jisem.v10i4s.487.

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The process of automatically grouping documents into clusters such that the documents in one cluster are very comparable to the documents in the remaining clusters have been known as document clustering. Due to its broad application in a number of fields, including search engines, web mining, and information retrieval, it has been the subject of much research. It involves clustering documents that are identical to one another and calculating how identical they are. It facilitates simple navigation by offering effective document representation as well as visualization. Hence, this research pape

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Liu, R. H., Q. Zhang, and G. Yin. "document." Applied Mathematics and Optimization 44, no. 2 (2001): 105–29. http://dx.doi.org/10.1007/s00245-001-0016-8.

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Abualigah, Laith, Amir H. Gandomi, Mohamed Abd Elaziz, et al. "Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis." Algorithms 13, no. 12 (2020): 345. http://dx.doi.org/10.3390/a13120345.

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Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experime

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Kayest, Mamta, and Sanjay Kumar Jain. "An incremental learning approach for the text categorization using hybrid optimization." International Journal of Intelligent Computing and Cybernetics 12, no. 3 (2019): 333–51. http://dx.doi.org/10.1108/ijicc-12-2018-0170.

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Purpose Document retrieval has become a hot research topic over the past few years, and has been paid more attention in browsing and synthesizing information from different documents. The purpose of this paper is to develop an effective document retrieval method, which focuses on reducing the time needed for the navigator to evoke the whole document based on contents, themes and concepts of documents. Design/methodology/approach This paper introduces an incremental learning approach for text categorization using Monarch Butterfly optimization–FireFly optimization based Neural Network (MB–FF ba

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Nakazato, Rie, Kugatsu Sadamitsu, and Mikio Yamamoto. "Document‐level optimization in speech recognition." Journal of the Acoustical Society of America 120, no. 5 (2006): 3043. http://dx.doi.org/10.1121/1.4787227.

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Laptin, Yuri, Tamara Bardadym, and Alexandr Lefterov. "Optimization Problems of Document Processing Management." Cybernetics and Computer Technologies, no. 3 (October 27, 2020): 5–13. http://dx.doi.org/10.34229/2707-451x.20.3.1.

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Introduction. The use of various cloud services is becoming an integral part of modern life. At the same time, the owners of such services usually are not going to inform users with the theoretical foundations of the deployment and provision of these services, as well as with issues of security. On the other hand, as the above literature review shows, researchers often limit themselves to describing certain aspects of cloud technologies. The introduction of optimization approaches will contribute to both the development of the capabilities of providers and the rational use of resources by end

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Rautaray, Jyotirmayee, Sangram Panigrahi, and Ajit Kumar Nayak. "Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidirectional long short-term memory classifier for effective single and multi-document summarization." PeerJ Computer Science 10 (December 12, 2024): e2435. https://doi.org/10.7717/peerj-cs.2435.

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The internet now offers a vast amount of information, which makes finding relevant data quite challenging. Text summarization has become a prominent and effective method towards glean important information from numerous documents. Summarization techniques are categorized into single-document and multi-document. Single-document summarization (SDS) targets on single document, whereas multi-document summarization (MDS) combines information from several sources, posing a greater challenge for researchers to create precise summaries. In the realm of automatic text summarization, advanced methods su

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Garba, Adamu, Shah Khalid, Aliya Aleryni, et al. "Utilizing Ant Colony Optimization for Result Merging in Federated Search." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 14832–39. http://dx.doi.org/10.48084/etasr.7302.

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Federated search or distributed information retrieval routes the user's search query to multiple component collections and presents a merged result list in ranked order by comparing the relevance score of each returned result. However, the heterogeneity of the component collections makes it challenging for the central broker to compare these relevance scores while fusing the results into a single ranked list. To address this issue, most existing approaches merge the returned results by converting the document ranks to their ranking scores or downloading the documents and computing their releva

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Rautray, Rasmita, Rakesh Chandra Balabantaray, Rasmita Dash, and Rajashree Dash. "CSMDSE-Cuckoo Search Based Multi Document Summary Extractor." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 4 (2019): 56–70. http://dx.doi.org/10.4018/ijcini.2019100103.

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In the current scenario, managing of a useful web of information has become a challenging issue due to a large amount of information related to many fields is online. The summarization of text is considered as one of the solutions to extract pertinent text from vast documents. Hence, a novel Cuckoo Search-based multi document summary extractor (CSMDSE) is presented to handle the multi-document summarization (MDS) problem. The proposed CSMDSE is assimilating with few other swarm-based summary extractors, such as Cat Swarm Optimization based Extractor (CSOE), Particle Swarm Optimization based Ex

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KYAW, KHIN SANDAR, and Somchai Limsiroratana. "An Optimization of Multi-Class Document Classification with Computational Search Policy." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 2 (2020): 149–61. http://dx.doi.org/10.37936/ecti-cit.2020142.227431.

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In the era of internet communication, many electronic documents are spread and flow on the platform of website in every splits of seconds. The research interest for the process of knowledge discovery is changed from the traditional data to online data such as online news document classification. Most percentage of the online data is text document and therefore the optimization of multi-class document classification is becoming a challenge for today society. Traditional search policy for feature selection process is degrading with exhaustive search for complex feature in document classification

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Prathyusha, Kosuru. "Performance Optimization in Web Applications." European Journal of Advances in Engineering and Technology 6, no. 8 (2019): 100–104. https://doi.org/10.5281/zenodo.13919484.

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Application performance optimization is an essential process that helps improve the usability of web applications. This document aims to discuss various ways of increasing both the front-end and back-end performance levels. It includes basic front-end optimizations such as lazy loading and caching and back-end optimizations including database indexing and query optimization. By using these methods, developers can increase the effectiveness of work and efficiency of web applications (Ahmed et al., 2016)

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Dodda, Ratnam, and Alladi Suresh Babu. "Text document clustering using mayfly optimization algorithm with k-means technique." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 1099. http://dx.doi.org/10.11591/ijeecs.v35.i2.pp1099-1109.

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Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgro

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Ratnam, Dodda Alladi Suresh Babu. "Text document clustering using mayfly optimization algorithm with k-means technique." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 2 (2024): 1099–109. https://doi.org/10.11591/ijeecs.v35.i2.pp1099-1109.

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Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgro

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Kim, Beom Su, Hyung Il Koo, and Nam Ik Cho. "Document dewarping via text-line based optimization." Pattern Recognition 48, no. 11 (2015): 3600–3614. http://dx.doi.org/10.1016/j.patcog.2015.04.026.

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Che, Dunren, Karl Aberer, and M. Tamer Özsu. "Query optimization in XML structured-document databases." VLDB Journal 15, no. 3 (2006): 263–89. http://dx.doi.org/10.1007/s00778-005-0172-6.

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Abasi, Ammar Kamal, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Syibrah Naim, Mohammed A. Awadallah, and Osama Ahmad Alomari. "Text documents clustering using modified multi-verse optimizer." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 6361. http://dx.doi.org/10.11591/ijece.v10i6.pp6361-6369.

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In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space

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Ammar, Kamal Abasi, Tajudin Khader Ahamad, Azmi Al-Betar Mohammed, Naim Syibrah, A. Awadallah Mohammed, and Ahmad Alomari Osama. "Text documents clustering using modified multi-verse optimizer." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 6361–69. https://doi.org/10.11591/ijece.v10i6.pp6361-6369.

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In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belong- ing to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search spa

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Li, Dan. "Effective collection construction for information retrieval evaluation and optimization." ACM SIGIR Forum 54, no. 2 (2020): 1–2. http://dx.doi.org/10.1145/3483382.3483401.

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The availability of test collections in Cranfield paradigm has significantly benefited the development of models, methods and tools in information retrieval. Such test collections typically consist of a set of topics, a document collection and a set of relevance assessments. Constructing these test collections requires effort of various perspectives such as topic selection, document selection, relevance assessment, and relevance label aggregation etc. The work in the thesis provides a fundamental way of constructing and utilizing test collections in information retrieval in an effective, effic

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Ikhwan, Ali, Rafikha Aliana A. Raof, Phaklen Ehkan, Yasmin Yacob, and Nuri Aslami. "Implementation of image file security using the advanced encryption standard method." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 1 (2023): 562. http://dx.doi.org/10.11591/ijeecs.v31.i1.pp562-569.

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The application of technology in this era has entered digitalization and is modern. Therefore, we are already in an era of advanced and rapid technological development. It has become a human need to exchange information in every activity. Documents that contain information that is frequently sought or used. The document's use also includes essential information. Document security is undoubtedly a significant factor in prioritizing important information in a document to prevent unauthorized people from misusing the document's vital information. Cryptography is a method of overcoming document se

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Nandi, Rakesh, Sujit Kumar Samanta, and Chesoong Kim. "Analysis of \begin{document}$ D $\end{document}-\begin{document}$ BMAP/G/1 $\end{document} queueing system under \begin{document}$ N $\end{document}-policy and its cost optimization." Journal of Industrial & Management Optimization 13, no. 5 (2017): 0. http://dx.doi.org/10.3934/jimo.2020135.

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Sambetbayeva, Madina, Inkarzhan Kuspanova, Aigerim Yerimbetova, Sandugash Serikbayeva, and Shynar Bauyrzhanova. "Development of intelligent electronic document management system model based on machine learning methods." Eastern-European Journal of Enterprise Technologies 1, no. 2(115) (2022): 68–76. http://dx.doi.org/10.15587/1729-4061.2022.251689.

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With the daily increase in document flow, as well as the transition to paperless document management around the world, the demand for electronic document management systems is increasing. This significantly requires optimization of these systems in terms of quality document information retrieval and document management. However, research based on statistical methods cannot effectively handle large amounts of data extracted from electronic documents. In this regard, machine learning methods can effectively solve this problem. This paper presents an approach to building a model of an intelligent

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Madina, Sambetbayeva, Kuspanova Inkarzhan, Yerimbetova Aigerim, Serikbayeva Sandugash, and Bauyrzhanova Shynar. "Development of intelligent electronic document management system model based on machine learning methods." Eastern-European Journal of Enterprise Technologies 1, no. 2(115) (2022): 68–76. https://doi.org/10.15587/1729-4061.2022.251689.

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With the daily increase in document flow, as well as the transition to paperless document management around the world, the demand for electronic document management systems is increasing. This significantly requires optimization of these systems in terms of quality document information retrieval and document management. However, research based on statistical methods cannot effectively handle large amounts of data extracted from electronic documents. In this regard, machine learning methods can effectively solve this problem. This paper presents an approach to building a model of an intelligent

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Horvat, Marko, Alan Jović, and Danko Ivošević. "Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases." Information 11, no. 9 (2020): 429. http://dx.doi.org/10.3390/info11090429.

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Evaluation of document classification is straightforward if complete information on the documents’ true categories exists. In this case, the rank of each document can be accurately determined and evaluated. However, in an unsupervised setting, where the exact document category is not available, lift charts become an advantageous method for evaluation of the retrieval quality and categorization of ranked documents. We introduce lift charts as binary classifiers of ranked documents and explain how to apply them to the concept-based retrieval of emotionally annotated images as one of the possible

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Mariyam, Ayesha, SK Althaf Hussain Basha, and S. Viswanadha Raju. "On Optimality of Long Document Classification using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (2022): 51–58. http://dx.doi.org/10.17762/ijritcc.v10i12.5866.

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Document classification is effective with elegant models of word numerical distributions. The word embeddings are one of the categories of numerical distributions of words from the WordNet. The modern machine learning algorithms yearn on classifying documents based on the categorical data. The context of interest on the categorical data is posed with weights and the sense and quality of the sentences is estimated for sensible classification of documents. The focus of the current work is on legal and criminal documents extracted from the popular news channels, particularly on classification of

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Cheng, Jiehan, Zhicheng Dou, Yutao Zhu, and Xiaoxi Li. "Descriptive and Discriminative Document Identifiers for Generative Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11518–26. https://doi.org/10.1609/aaai.v39i11.33253.

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Generative document retrieval is a novel retrieval framework, which represents documents as identifiers (DocID) and retrieves documents by generating DocIDs. It has the advantage of end-to-end optimization over traditional retrieval methods and has attracted much research interest. Nonetheless, the development of efficient and precise DocIDs for document representation remains a pertinent issue within the field. Existing methods for designing DocIDs tend to consider only the relevance of DocIDs to the corresponding documents, while neglecting the ability of the DocIDs to distinguish the corres

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Al-Obaydy, Wasseem N. Ibrahem, Hala A. Hashim, Yassen AbdelKhaleq Najm, and Ahmed Adeeb Jalal. "Document classification using term frequency-inverse document frequency and K-means clustering." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1517. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1517-1524.

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Increased advancement in a variety of study subjects and information technologies, has increased the number of published research articles. However, researchers are facing difficulties and devote a significant time amount in locating scientific research publications relevant to their domain of expertise. In this article, an approach of document classification is presented to cluster the text documents of research articles into expressive groups that encompass a similar scientific field. The main focus and scopes of target groups were adopted in designing the proposed method, each group include

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Nechepurenko, Dmytro. "Optimization of e-document workflow for order calculation." Technology audit and production reserves 3, no. 4(41) (2018): 53–58. http://dx.doi.org/10.15587/2312-8372.2018.134982.

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Jabbar, Ayad Mohammed, and Ku Ruhana Ku-Mahamud. "Grey wolf optimization algorithm for hierarchical document clustering." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1744. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1744-1758.

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In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids

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Sartakhti, Moein Salimi, Ahmad Yoosofan, Ali Asghar Fatehi, and Ali Rahimi. "Single Document Summarization Based on Grey Wolf Optimization." Global Journal of Computer Sciences: Theory and Research 10, no. 2 (2020): 48–56. http://dx.doi.org/10.18844/gjcs.v10i2.5807.

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The amazing growth of online services has caused an information explosion issue. Text summarisation is condensing the text into a small version and preserving its overall concept. Text summarisation is an important way to extract significant information from documents and offer that information to the user in an abbreviated form while preserving its major content. For human beings, it is very difficult to summarise large documents. To do this, this paper uses some sentence features and word features. These features assign scores to all the sentences. In this paper, we combine these features by

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ABD ELFATTAH, Mohamed, Aboul Ella HASSANIEN, and Sherihan ABUELENIN. "A Hybrid Swarm Optimization Approach for Document Binarization." Studies in Informatics and Control 28, no. 1 (2019): 65–76. http://dx.doi.org/10.24846/v28i1y201907.

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Alguliev, Rasim M., Ramiz M. Aliguliyev, and Chingiz A. Mehdiyev. "AN OPTIMIZATION APPROACH TO AUTOMATIC GENERIC DOCUMENT SUMMARIZATION." Computational Intelligence 29, no. 1 (2012): 129–55. http://dx.doi.org/10.1111/j.1467-8640.2012.00437.x.

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Yarlagadda, Madhulika, Gangadhara Rao Kancherla, and Srikrishna Atluri. "Incremental document clustering using fuzzy-based optimization strategy." Evolutionary Intelligence 13, no. 3 (2019): 497–510. http://dx.doi.org/10.1007/s12065-019-00335-1.

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Master, Lawrence. "Improving document ranking with genetic and optimization algorithms." International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 31, no. 3 (2017): e2310. http://dx.doi.org/10.1002/jnm.2310.

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Kalaiselvi, R., and K. Kousalya. "Statistical modelling and parametric optimization in document fragmentation." Neural Computing and Applications 32, no. 10 (2019): 5909–18. http://dx.doi.org/10.1007/s00521-019-04068-1.

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Jabbar, Ayad Mohammed, and Ku Ruhana Ku-Mahamud. "Grey wolf optimization algorithm for hierarchical document clustering." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1744–58. https://doi.org/10.11591/ijeecs.v24.i3.pp1744-1758.

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In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids

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Zhang, Miao, Jiawei Wang, Kui Xiao, et al. "Learning Concept Prerequisite Relation via Global Knowledge Relation Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 1638–46. https://doi.org/10.1609/aaai.v39i2.32156.

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Learning concept prerequisite relations helps better master and build a logically coherent knowledge structure. Many studies use graph neural networks to create heterogeneous knowledge networks that enhance concept representations. However, different types of relations in these networks can influence each other. Existing research often focuses solely on concept relations, neglecting other types of knowledge connections. To address this issue, this paper proposes a novel concept prerequisite relation learning model, named the Global Knowledge Relation Optimization Model(GKROM). Specifically, we

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Krasnov, Fedor, Irina Smaznevich, and Elena Baskakova. "Optimization approach to the choice of explicable methods for detecting anomalies in homogeneous text collections." Informatics and Automation 20, no. 4 (2021): 869–904. http://dx.doi.org/10.15622/ia.20.4.5.

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 The problem of detecting anomalous documents in text collections is considered. The existing methods for detecting anomalies are not universal and do not show a stable result on different data sets. The accuracy of the results depends on the choice of parameters at each step of the problem solving algorithm process, and for different collections different sets of parameters are optimal. Not all of the existing algorithms for detecting anomalies work effectively with text data, which vector representation is characterized by high dimensionality with strong sparsity.The problem of finding

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Bezdan, Timea, Catalin Stoean, Ahmed Al Naamany, et al. "Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering." Mathematics 9, no. 16 (2021): 1929. http://dx.doi.org/10.3390/math9161929.

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The fast-growing Internet results in massive amounts of text data. Due to the large volume of the unstructured format of text data, extracting relevant information and its analysis becomes very challenging. Text document clustering is a text-mining process that partitions the set of text-based documents into mutually exclusive clusters in such a way that documents within the same group are similar to each other, while documents from different clusters differ based on the content. One of the biggest challenges in text clustering is partitioning the collection of text data by measuring the relev

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Sydorko, D., and V. Glukhov. "PROGRAM IMPLEMENTATION OF METHODS FOR ANALYSIS AND VERIFICATION OF TECHNICAL REPORTS." Computer systems and network 6, no. 2 (2024): 204–18. https://doi.org/10.23939/csn2024.02.204.

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The paper provides a solution to the problem of software implementation of methods of analysis and standard control of textual technical reports in docx format to check their compliance with given standards. The proposed solution uses .NET, WPF and DocumentFormat.OpenXml technologies are used to check the correctness of page formatting, the consistency of styles, the presses, and compliance of page parameters with standards (A4, Letter). Administrators are also allowed to flexibly configure the sequence of styles using the “before” and “after” parameters. One of the key features of the impleme

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Sydorko, D., and V. Glukhov. "PROGRAM IMPLEMENTATION OF METHODS FOR ANALYSIS AND VERIFICATION OF TECHNICAL REPORTS." Computer systems and network 6, no. 2 (2024): 208–21. https://doi.org/10.23939/csn2024.02.208.

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Abstract:

The paper provides a solution to the problem of software implementation of methods of analysis and standard control of textual technical reports in docx format to check their compliance with given standards. The proposed solution uses .NET, WPF and DocumentFormat.OpenXml technologies are used to check the correctness of page formatting, the consistency of styles, the presses, and compliance of page parameters with standards (A4, Letter). Administrators are also allowed to flexibly configure the sequence of styles using the “before” and “after” parameters. One of the key features of the impleme

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Gamido, Marlon V., Heidilyn V. Gamido, and Daryl James P. Macaspac. "Electronic document management system for local area network-based organizations." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 2 (2023): 1154. http://dx.doi.org/10.11591/ijeecs.v31.i2.pp1154-1163.

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The paper discusses the design and development of an electronic document management system (EDMS) that prepares documents for sharing and information dissemination. EDMS has to do with capturing, storing, indexing, retrieval, and disposal of documents. The electronic document management system process starts by converting paper document into digital record to efficiently store and organize document in standardized file structure and format, promoting a paper waste reduction in reproducing the document. The EDMS provided an easier way of sharing information with different stakeholders and secur

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Jia, Longjia, and Bangzuo Zhang. "A new document representation based on global policy for supervised term weighting schemes in text categorization." Mathematical Biosciences and Engineering 19, no. 5 (2022): 5223–40. http://dx.doi.org/10.3934/mbe.2022245.

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<abstract> <p>There are two main factors involved in documents classification, document representation method and classification algorithm. In this study, we focus on document representation method and demonstrate that the choice of representation methods has impacts on quality of classification results. We propose a document representation strategy for supervised text classification named document representation based on global policy (<italic>DRGP</italic>), which can obtain an appropriate document representation according to the distribution of terms. The main idea o

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Kyaw, Khin Sandar, Somchai Limsiroratana, and Tharnpas Sattayaraksa. "A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection." International Journal of Applied Metaheuristic Computing 13, no. 1 (2022): 1–34. http://dx.doi.org/10.4018/ijamc.292517.

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Algorithmic – based search approach is ineffective at addressing the problem of multi-dimensional feature selection for document categorization. This study proposes the use of meta heuristic based search approach for optimal feature selection. Elephant optimization (EO) and Ant Colony optimization (ACO) algorithms coupled with Naïve Bayes (NB), Support Vector Machin (SVM), and J48 classifiers were used to highlight the optimization capability of meta-heuristic search for multi-dimensional feature selection problem in document categorization. In addition, the performance results for feature sel

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Mamidala, Kishore Kumar, and Suresh Kumar Sanampudi. "A Novel Framework for Multi-Document Temporal Summarization (MDTS)." Emerging Science Journal 5, no. 2 (2021): 184–90. http://dx.doi.org/10.28991/esj-2021-01268.

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Internet or Web consists of a massive amount of information, handling which is a tedious task. Summarization plays a crucial role in extracting or abstracting key content from multiple sources with its meaning contained, thereby reducing the complexity in handling the information. Multi-document summarization gives the gist of the content collected from multiple documents. Temporal summarization concentrates on temporally related events. This paper proposes a Multi-Document Temporal Summarization (MDTS) technique that generates the summary based on temporally related events extracted from mult

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