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Journal articles on the topic 'Text-based'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

D., Mhamdi. "Job Recommendation System based on Text Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1025–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201575.

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2

SI, Qin, Li ZHANG, and De-liang LIAN. "Text watermarking based on text feature." Journal of Computer Applications 29, no. 9 (November 13, 2009): 2348–50. http://dx.doi.org/10.3724/sp.j.1087.2009.02348.

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3

Lockwood, Robert, and Kevin Curran. "Text based steganography." International Journal of Information Privacy, Security and Integrity 3, no. 2 (2017): 134. http://dx.doi.org/10.1504/ijipsi.2017.088700.

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4

Lockwood, Robert, and Kevin Curran. "Text based steganography." International Journal of Information Privacy, Security and Integrity 3, no. 2 (2017): 134. http://dx.doi.org/10.1504/ijipsi.2017.10009581.

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5

YOSHIMI, TAKEHIKO, JIRI JELINEK, OSAMU NISHIDA, NAOYUKI TAMURA, and HARUO MURAKAMI. "Text Analysis based on Text-Wide Grammar." Journal of Natural Language Processing 4, no. 1 (1997): 3–21. http://dx.doi.org/10.5715/jnlp.4.3.

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6

Li, De, Xue Zhe Jin, and LiHua Cui. "Text recognition algorithm based on text features." International Journal of Multimedia and Ubiquitous Engineering 11, no. 5 (May 31, 2016): 209–20. http://dx.doi.org/10.14257/ijmue.2016.11.5.19.

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7

More, Prof Vijay, Ms Ankita Shetty, and Ms Aishwarya Mapara Mr Rahul Ghuge Mr Rohit Sharma. "Employee Data Mining Based on Text and Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 379–81. http://dx.doi.org/10.31142/ijtsrd10791.

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8

Chatterjee, Ayan, Gourab Dolui, and Dr Uttam Kumar Roy. "Text Based Steganography – A Theoritical Proposal of Text Based Hiding Strategy." International Journal of Scientific and Engineering Research 6, no. 11 (November 25, 2015): 625–29. http://dx.doi.org/10.14299/ijser.2015.11.005.

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9

Chatterjee, Ayan, Gourab Dolui, and Dr Uttam Kumar Roy. "Text Based Steganography - A Theoritical Proposal of Text Based Hiding Strategy." International Journal of Scientific and Engineering Research 6, no. 11 (November 25, 2015): 625–29. http://dx.doi.org/10.14299/ijser.2015.11.010.

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10

S. J, Rexline, Robert L, and Trujilla Lobo.F. "Dictionary Based Text Filter for Lossless Text Compression." International Journal of Computer Trends and Technology 49, no. 3 (July 25, 2017): 143–49. http://dx.doi.org/10.14445/22312803/ijctt-v49p122.

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11

Hoberg, Gerard, and Gordon M. Phillips. "Text-Based Industry Momentum." Journal of Financial and Quantitative Analysis 53, no. 6 (November 9, 2018): 2355–88. http://dx.doi.org/10.1017/s0022109018000479.

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We test the hypothesis that low-visibility shocks to text-based network industry peers can explain industry momentum. We consider industry peer firms identified through 10-K product text and focus on economic peer links that do not share common Standard Industrial Classification (SIC) codes. Shocks to less visible peers generate economically large momentum profits and are stronger than own-firm momentum variables. More visible traditional SIC-based peers generate only small, short-lived momentum profits. Our findings are consistent with momentum profits arising partially from inattention to economic links of less visible industry peers.

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12

LEWITTER, F. "Text-based database searching." Trends in Biotechnology 16 (November 1998): 3–5. http://dx.doi.org/10.1016/s0167-7799(98)00126-7.

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13

Ferhat, Ensar. "Schema-based text comprehension." Educational Research and Reviews 10, no. 18 (September 23, 2015): 2568–74. http://dx.doi.org/10.5897/err2015.2469.

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14

Moffat, Alistair. "Word-based text compression." Software: Practice and Experience 19, no. 2 (February 1989): 185–98. http://dx.doi.org/10.1002/spe.4380190207.

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15

Schwarz, Christoph. "Content based text handling." Information Processing & Management 26, no. 2 (January 1990): 219–26. http://dx.doi.org/10.1016/0306-4573(90)90027-y.

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16

Norvig, Peter. "Text-based intelligent systems." Artificial Intelligence 66, no. 1 (March 1994): 181–88. http://dx.doi.org/10.1016/0004-3702(94)90007-8.

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17

Qi, Wenfa, Wei Guo, Tong Zhang, Yuxin Liu, Zongming Guo, and Xifeng Fang. "Robust authentication for paper-based text documents based on text watermarking technology." Mathematical Biosciences and Engineering 16, no. 4 (2019): 2233–49. http://dx.doi.org/10.3934/mbe.2019110.

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18

Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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19

Vidhya, P. M., and Varghese Paul. "Unicode-based method for text steganography with malayalam text." Journal of Intelligent & Fuzzy Systems 28, no. 4 (2015): 1591–600. http://dx.doi.org/10.3233/ifs-141444.

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20

Schraw, Gregory, Suzanne E. Wade, and Carol A. Kardash. "Interactive effects of text-based and task-based importance on learning from text." Journal of Educational Psychology 85, no. 4 (1993): 652–61. http://dx.doi.org/10.1037/0022-0663.85.4.652.

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21

Kaur, Gurjeet. "Text Mining Based Approach to Customer Sentiment Analysis Using Machine Learning." Journal of Advances and Scholarly Researches in Allied Education 15, no. 6 (July 1, 2018): 58–65. http://dx.doi.org/10.29070/15/57680.

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22

N.J., Anjala. "Algorithmic Assessment of Text based Data Classification in Big Data Sets." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1231–34. http://dx.doi.org/10.5373/jardcs/v12sp4/20201598.

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23

Alomran, Hamad Ibrahim. "Text Mining-Based Semantic Web Architecture (TMSWA) for e-Learning Systems." International Journal of Machine Learning and Computing 4, no. 4 (2014): 333–38. http://dx.doi.org/10.7763/ijmlc.2014.v4.433.

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24

Mohd Ismail, Hamidah, and Faizah Abd Majid. "Intertextuality in Text-based Discussions." Advances in Language and Literary Studies 2, no. 1 (January 1, 2011): 18–25. http://dx.doi.org/10.7575/aiac.alls.v.2n.1p.18.

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25

Binwahlan, Mohammed Salem, Naomie Salim, and Ladda Suanmali. "Fuzzy Swarm Based Text Summarization." Journal of Computer Science 5, no. 5 (May 1, 2009): 338–46. http://dx.doi.org/10.3844/jcssp.2009.338.346.

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26

Mehul, Gupta, Patel Ankita, Dave Namrata, Goradia Rahul, and Saurin Sheth. "Text-based Image Segmentation Methodology." Procedia Technology 14 (2014): 465–72. http://dx.doi.org/10.1016/j.protcy.2014.08.059.

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27

Lijo, V. P., and Hari Seetha. "Text-based sentiment analysis: review." International Journal of Knowledge and Learning 12, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijkl.2017.088163.

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28

Seetha, Hari, and V. P. Lijo. "Text-based sentiment analysis: review." International Journal of Knowledge and Learning 12, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijkl.2017.10009108.

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29

Aditya, Kuwar, Bhalekar Arjun, and Bade Ankush. "An Ontology Based Text Mining." International Journal of Engineering Trends and Technology 10, no. 6 (April 25, 2014): 292–96. http://dx.doi.org/10.14445/22315381/ijett-v10p257.

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30

Greenhalgh, Jack, and Majid Mirmehdi. "Recognizing Text-Based Traffic Signs." IEEE Transactions on Intelligent Transportation Systems 16, no. 3 (June 2015): 1360–69. http://dx.doi.org/10.1109/tits.2014.2363167.

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31

Lockhart, Elaine. "Psychiatry An Evidence-based Text." Scottish Medical Journal 55, no. 3 (August 2010): 10. http://dx.doi.org/10.1258/rsmsmj.55.3.10.

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32

Wang, Eric Ke, and Yun Ming Ye. "A New Text Based CAPTCHA." Applied Mechanics and Materials 373-375 (August 2013): 644–49. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.644.

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Attacks on text-based CAPTCHAs are getting more sophisticated. Most existing schemes defend attacks by increasing deformation or distortion rate of words which sacrifice the friendliness of the system. In this paper, we propose a secure one time text-based CAPTCHA scheme which can effectively defend multiple attacks without decreasing the friendliness of the scheme for valid users. The core of our design is an information obfuscation scheme which determines which character image to display and which to hide in an online manner from a sequence of 62 character images which makes it more difficult to attack. Encryption method is employed for protecting display policies which control showing and hiding of information. Prototype has been implemented and the preliminary results are encouraging.

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33

Paul, Peter V. "Deafness and Text-Based Literacy." American Annals of the Deaf 138, no. 2 (1993): 72–75. http://dx.doi.org/10.1353/aad.2012.0671.

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34

Nguyen, Vu H., Hien T. Nguyen, Hieu N. Duong, and Vaclav Snasel. "n-Gram-Based Text Compression." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/9483646.

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We propose an efficient method for compressing Vietnamese text usingn-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it inton-grams and then encodes them based onn-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Eachn-gram is encoded by two to four bytes accordingly based on its correspondingn-gram dictionary. We collected 2.5 GB text corpus from some Vietnamese news agencies to buildn-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total. In order to evaluate our method, we collected a testing set of 10 different text files with different sizes. The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods.

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35

Alberola, C., and G. V. Cybenko. "Tracking with text-based messages." IEEE Intelligent Systems 14, no. 4 (July 1999): 70–78. http://dx.doi.org/10.1109/5254.784087.

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36

Barr, Nancy E. "Text-based information management systems." Journal of Academic Librarianship 19, no. 1 (March 1993): 45. http://dx.doi.org/10.1016/0099-1333(93)90773-x.

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37

Wu, Jui-Chen, Jun-Wei Hsieh, and Yung-Sheng Chen. "Morphology-based text line extraction." Machine Vision and Applications 19, no. 3 (August 3, 2007): 195–207. http://dx.doi.org/10.1007/s00138-007-0092-0.

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38

Minaee, Shervin, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. "Deep Learning--based Text Classification." ACM Computing Surveys 54, no. 3 (June 2021): 1–40. http://dx.doi.org/10.1145/3439726.

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Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.

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39

Qian, Yili, Chaochao Jia, and Yimei Liu. "Bert-Based Text Keyword Extraction." Journal of Physics: Conference Series 1992, no. 4 (August 1, 2021): 042077. http://dx.doi.org/10.1088/1742-6596/1992/4/042077.

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40

Sabol, Vedran, Keith Andrews, Wolfgang Kienreich, and Michael Granitzer. "Text mapping: Visualising unstructured, structured, and time-based text collections." Intelligent Decision Technologies 2, no. 2 (May 15, 2008): 117–28. http://dx.doi.org/10.3233/idt-2008-2204.

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41

Yadav, Anurag Kumar, Mukesh Kumar, and Ayonija Pathre. "Implemented Text Rank based Automatic Text Summarization using Keyword Extraction." International Research Journal of Innovations in Engineering and Technology 04, no. 11 (2020): 20–25. http://dx.doi.org/10.47001/irjiet/2020.411003.

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42

Ye, Wei, Xiaogang Gong, Quan Zhang, and Xiaoming Ju. "Text Tiling Text Segmentation Based on Hierarchical Dirichlet Process Model." Journal of Physics: Conference Series 1237 (June 2019): 052004. http://dx.doi.org/10.1088/1742-6596/1237/5/052004.

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43

Pacharne, Ashish, Pramod S. Nair, and Srinivasa Rao D. "TM-SGTD: Text Mining Based Semantic Graph for Text Document Approach for Text Representation." International Journal of Engineering and Technology 9, no. 4 (August 31, 2017): 2820–27. http://dx.doi.org/10.21817/ijet/2017/v9i4/170904007.

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44

Leopold, Claudia, Annette Brückner, and Stephan Dutke. "Summarizing as a Strategy for Science Text Comprehension: Text-Based Versus Content-Based Processing." Discourse Processes 56, no. 8 (January 15, 2019): 728–47. http://dx.doi.org/10.1080/0163853x.2018.1563849.

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45

Nguyen, K. "Comment: Text compression as rule-based pattern recognition; Text compression using rule based encoder." Electronics Letters 31, no. 9 (1995): 701. http://dx.doi.org/10.1049/el:19950510.

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46

Weintrop, David, and Uri Wilensky. "How block-based, text-based, and hybrid block/text modalities shape novice programming practices." International Journal of Child-Computer Interaction 17 (September 2018): 83–92. http://dx.doi.org/10.1016/j.ijcci.2018.04.005.

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47

Bernát, Péter. "Evaluation of Block-Based and Text-Based Coding." Central-European Journal of New Technologies in Research, Education and Practice 2, no. 2 (2020): 15–25. http://dx.doi.org/10.36427/cejntrep.2.2.471.

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48

Baritha Begum, M., and Y. Venkataramani. "LSB Based Audio Steganography Based On Text Compression." Procedia Engineering 30 (2012): 703–10. http://dx.doi.org/10.1016/j.proeng.2012.01.917.

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49

Scardamalia, Marlene, and Carl Bereiter. "Text-Based and Knowledge Based Questioning by Children." Cognition and Instruction 9, no. 3 (September 1992): 177–99. http://dx.doi.org/10.1207/s1532690xci0903_1.

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50

Wang, Peng, Yun Yan, Yingdong Si, Gancheng Zhu, Xiangping Zhan, Jun Wang, and Runsheng Pan. "Classification of Proactive Personality: Text Mining Based on Weibo Text and Short-Answer Questions Text." IEEE Access 8 (2020): 97370–82. http://dx.doi.org/10.1109/access.2020.2995905.

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