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Статті в журналах з теми "Text modeling"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Bell, Timothy, Ian H. Witten, and John G. Cleary. "Modeling for text compression." ACM Computing Surveys 21, no. 4 (December 1989): 557–91. http://dx.doi.org/10.1145/76894.76896.

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2

Leboeuf, J. ‐N, D. K. Lee, B. A. Carreras, N. Dominguez, J. H. Harris, C. L. Hedrick, C. Hidalgo, et al. "TEXT tokamak edge turbulence modeling." Physics of Fluids B: Plasma Physics 3, no. 8 (August 1991): 2291–99. http://dx.doi.org/10.1063/1.859596.

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3

CHEN, YE-SHO. "ZIPF'S LAWS IN TEXT MODELING." International Journal of General Systems 15, no. 3 (August 1989): 233–52. http://dx.doi.org/10.1080/03081078908935048.

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4

Kartika, Rica, and Yulianti Rasyid. "PENGARUH TEKNIK PEMODELAN TERHADAP KETERAMPILAN MENULIS TEKS PROSEDUR SISWA KELAS VII SMP NEGERI 14 PADANG." Pendidikan Bahasa Indonesia 8, no. 2 (June 10, 2019): 81. http://dx.doi.org/10.24036/104518-019883.

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Анотація:

ABSTRACT The purpose of this research is three. First, describing the procedure of writing the text of a class VII junior high School 14 Padang before using modelling techniques. Second, describing the procedure of writing the text of Class VII in SMP Negeri 14 Padang after using modeling technique. Third, describing the influence of use of modeling techniques to the skill level of writing the text procedures class VII SMP Negeri 14 Padang. This type of research is quantitative research with experimental methods. This research draft is one group Pretests and posttest design. The population in this study is a grade VII student at SMP Negeri 14 Padang enrolled in Lesson 2018/2019. The samples in this study were determined using purposive sampling techniques. This research variable is the skill of writing the procedure text before and after using the modeling technique of class VII SMP Negeri 14 Padang. This research Data is the test score of the text writing procedure before and after using the modeling technique of class VII SMP Negeri 14 Padang. The Data is analyzed with a percentage formula, average count formula, and test-T. The research instrument is a test of work, which is the test of the text of the procedure before and after using the modeling technique. Kata Kunci: Pengaruh, Teknik Pemodelan, Teks Prosedur

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5

Utami, Mimi Putri, Andria Catri Thamsin, and Mohammad Hafrison. "PENGARUH TEKNIK PEMODELAN TERHADAP KETERAMPILAN MENULIS TEKS PROSEDUR KOMPLEKS SISWA KELAS XI SMKN 1 TAPAN." Pendidikan Bahasa Indonesia 8, no. 1 (March 17, 2019): 121. http://dx.doi.org/10.24036/103925-019883.

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ABSTRACT This article has been writing techniques influence modeling text writing skills against the complex procedure of grade XI 1 — Tapan. This research is quantitative research with quasi experiment method of the one group pretest-postest design. The data in this study is the writing skills test score of complex procedure text grade XI 1 — Tapan before and after using the technique of modeling. The data obtained through test performance. Based on the results of data analysis, researchers concluded three things here; First, complex procedure text writing skills of students of Class XI 1 — Tapan before using the techniques of modelling are at Sufficient qualifications (62.02). Second, complex procedure text writing skills of students of Class XI — 1 using the technique of modeling after the Tapan is at Good qualifications (78.57). Third, based on the t-test, the alternative hypothesis (H1) are accepted on the degrees of confidence and degrees of freedom (dk) = (n-1) because thitung > ttabel (7.10 > 1.70). In other words, modeling techniques to complex procedures text writing skills of students of Class XI 1 — Tapan. Kata kunci: Teknik Pemodelan, Pengaruh, Keterampilan, Teks Prosedur Kompleks

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6

Rashid, Junaid, Syed Muhammad Adnan Shah, and Aun Irtaza. "Fuzzy topic modeling approach for text mining over short text." Information Processing & Management 56, no. 6 (November 2019): 102060. http://dx.doi.org/10.1016/j.ipm.2019.102060.

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7

庞, 劲羽. "Polarity-GuidedShort Text Sentiment Analysis Modeling." Instrumentation and Equipments 08, no. 04 (2020): 124–30. http://dx.doi.org/10.12677/iae.2020.84016.

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8

Strok, Fedor. "Modeling Text Similarity with Parse Thickets." Procedia Computer Science 31 (2014): 1012–21. http://dx.doi.org/10.1016/j.procs.2014.05.354.

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9

Kino, Yasunobu. "Conceptual Modeling supported by Text Analysis." Procedia Computer Science 126 (2018): 1387–94. http://dx.doi.org/10.1016/j.procs.2018.08.090.

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10

SHIEBER, STUART M., and RANI NELKEN. "Abbreviated text input using language modeling." Natural Language Engineering 13, no. 2 (July 6, 2006): 165–83. http://dx.doi.org/10.1017/s1351324906004311.

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Анотація:

We address the problem of improving the efficiency of natural language text input under degraded conditions (for instance, on mobile computing devices or by disabled users), by taking advantage of the informational redundancy in natural language. Previous approaches to this problem have been based on the idea of prediction of the text, but these require the user to take overt action to verify or select the system's predictions. We propose taking advantage of the duality between prediction and compression. We allow the user to enter text in compressed form, in particular, using a simple stipulated abbreviation method that reduces characters by 26.4%, yet is simple enough that it can be learned easily and generated relatively fluently. We decode the abbreviated text using a statistical generative model of abbreviation, with a residual word error rate of 3.3%. The chief component of this model is an n-gram language model. Because the system's operation is completely independent from the user's, the overhead from cognitive task switching and attending to the system's actions online is eliminated, opening up the possibility that the compression-based method can achieve text input efficiency improvements where the prediction-based methods have not. We report the results of a user study evaluating this method.

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11

Misra, Hemant, François Yvon, Olivier Cappé, and Joemon Jose. "Text segmentation: A topic modeling perspective." Information Processing & Management 47, no. 4 (July 2011): 528–44. http://dx.doi.org/10.1016/j.ipm.2010.11.008.

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12

Serrano, M. Ángeles, Alessandro Flammini, and Filippo Menczer. "Modeling Statistical Properties of Written Text." PLoS ONE 4, no. 4 (April 29, 2009): e5372. http://dx.doi.org/10.1371/journal.pone.0005372.

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13

Lin, Chenghua, Ebuka Ibeke, Adam Wyner, and Frank Guerin. "Sentiment-topic modeling in text mining." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 5, no. 5 (August 14, 2015): 246–54. http://dx.doi.org/10.1002/widm.1161.

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14

Razmara, Jafar. "A Method for Multiple Structural Alignment of Proteins Using Text Modeling Techniques." International Journal of Future Computer and Communication 4, no. 2 (April 2015): 143–46. http://dx.doi.org/10.7763/ijfcc.2015.v4.373.

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15

Kuropiatnykov, Olena Serhiivna. "Constructive and object-oriented modeling text for detection of text borrowings." System technologies 4, no. 123 (October 12, 2019): 34–47. http://dx.doi.org/10.34185/1562-9945-4-123-2019-04.

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Анотація:

The scientific community is encouraged to use such models and data structures as arrays of LERP-RSA (the longest expected duplicate array of reduced suffix templates), tag classifier-a model based on Stanford NER's three-class, structures based on DN-sequences, graph representations, etc. The following algorithms are used: GreedyString-Tiling, ARPAD, shingle, statistical methods, genetic algorithms, and others. It should also be noted that much attention is paid to morphological analysis and lemmatization, pre-processing of texts. Models and algorithms only partly have program realization.The purpose of this work is to develop a text model to identify borrowings and bring it to program implementation. The task is to develop the object-oriented model and program implementation of a graph text model, with the application of the problem of detection of borrowing. As well as obtaining timeframes for program implementation work for further evaluation of the possibility of its use in the academic environment.The main idea of the graph model is to present the text as a weighted oriented graph. The vertex weight is a character or sequence of characters. Edge weight is the set of numbers of paths into which the edge enters. To formalize the model will use the apparatus of constructive-synthesizing modeling. To create graphs, a constructor and its components are defined: carrier, signature, multiple statements of information support for design. Transformations are made for the constructor: specialization, interpretation and concretization.On the basis of this model, the object-oriented model is constructed. it includes three classes: vertex, graph and work .The object of class Work presents the text as a set of objects of class Graph. The correspondences between the components of the presented models are established.The object-oriented model is implemented by software. Data are given about the execution time of graph construction and texts comparison.At this stage, software implementation of the model has shown acceptable time performance. Further research in this direction is promising. Directions for improving the model and program are proposed.

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16

JONG, JING-PING, MEI-HUNG CHIU, and SHIAO-LAN CHUNG. "The Use of Modeling-Based Text to Improve Students' Modeling Competencies." Science Education 99, no. 5 (May 14, 2015): 986–1018. http://dx.doi.org/10.1002/sce.21164.

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17

Menshakova, Nadezhda N., Svetlana V. Shustova, Ekaterina A. Kostina, and Dzhafar Mallaev. "Fantasy space modeling in English academic text." XLinguae 13, no. 2 (April 2020): 272–91. http://dx.doi.org/10.18355/xl.2020.13.02.23.

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18

Jiang, Jing Jing, Xiao Yu Wang, Xiang Wei Mu, Jia Xing Hu, and You Qin Zhu. "The Study of Generative Modeling of Text." Applied Mechanics and Materials 444-445 (October 2013): 1713–17. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.1713.

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Анотація:

Text mining is the task of automatic discovery of new, previously unknown information from unstructured document collections. Vector space or bag of words representation is one of the mainstream descriptions of text, in which each document is a data point in high-dimensional space and order between words is omitted. Generative models are probabilistic representation of data that can be regarded as the generator of observed data. Being probabilistic modelling approaches, a set of methods and criterions are available for model estimation, inference, comparison and selection for generative models. In this paper, we review several existing probabilistic models that are commonly applied to discrete exchangeable collections in English text. We hope this will shed some light on the Chinese text modelling and mining tasks.

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19

Li, Xi-ming, Ji-hong Ouyang, and You Lu. "Topic modeling for large-scale text data." Frontiers of Information Technology & Electronic Engineering 16, no. 6 (June 2015): 457–65. http://dx.doi.org/10.1631/fitee.1400352.

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20

Cao, Shuqiang, Bairui Wang, Wei Zhang, and Lin Ma. "Visual Consensus Modeling for Video-Text Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 167–75. http://dx.doi.org/10.1609/aaai.v36i1.19891.

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Анотація:

In this paper, we propose a novel method to mine the commonsense knowledge shared between the video and text modalities for video-text retrieval, namely visual consensus modeling. Different from the existing works, which learn the video and text representations and their complicated relationships solely based on the pairwise video-text data, we make the first attempt to model the visual consensus by mining the visual concepts from videos and exploiting their co-occurrence patterns within the video and text modalities with no reliance on any additional concept annotations. Specifically, we build a shareable and learnable graph as the visual consensus, where the nodes denoting the mined visual concepts and the edges connecting the nodes representing the co-occurrence relationships between the visual concepts. Extensive experimental results on the public benchmark datasets demonstrate that our proposed method, with the ability to effectively model the visual consensus, achieves state-of-the-art performances on the bidirectional video-text retrieval task. Our code is available at https://github.com/sqiangcao99/VCM.

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21

Hanini, Muna, Nidal A.M Jabari, and Radwan Tahboub. "Text Modeling in Adaptive Educational Chat Room." International Journal of Computer Applications 103, no. 5 (October 18, 2014): 33–37. http://dx.doi.org/10.5120/18073-9022.

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22

Ipeirotis, Panagiotis G., Alexandros Ntoulas, Junghoo Cho, and Luis Gravano. "Modeling and managing changes in text databases." ACM Transactions on Database Systems 32, no. 3 (August 2007): 14. http://dx.doi.org/10.1145/1272743.1272744.

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23

Trofimova, Yu M. "COHESION IN COGNITIVE MODELING A HISTORICAL TEXT." Voprosy Kognitivnoy Lingvistiki, no. 1 (2019): 82–90. http://dx.doi.org/10.20916/1812-3228-2019-1-82-90.

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24

Nowlin, Matthew C. "Modeling Issue Definitions Using Quantitative Text Analysis." Policy Studies Journal 44, no. 3 (June 2, 2015): 309–31. http://dx.doi.org/10.1111/psj.12110.

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25

Zilca, R. D. "Text-independent speaker verification using covariance modeling." IEEE Signal Processing Letters 8, no. 4 (April 2001): 97–99. http://dx.doi.org/10.1109/97.911465.

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26

Kinariwala, Supriya A., and Sachin N. Deshmukh. "SHORT TEXT TOPIC MODELING WITH EMPIRICAL LEARNING." Indian Journal of Computer Science and Engineering 11, no. 5 (October 31, 2020): 510–16. http://dx.doi.org/10.21817/indjcse/2020/v11i5/201105168.

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27

Sulistyo, Teguh, and Dwi Fita Heriyawati. "Reformulation, text modeling, and the development of EFL academic writing." Journal on English as a Foreign Language 7, no. 1 (March 3, 2017): 1. http://dx.doi.org/10.23971/jefl.v7i1.457.

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Анотація:

<p>This paper mainly investigates the benefits of the implementation of Reformulation and Text Modelling in an EFL writing setting. Reformulation and Text Modeling (henceforth RTM) is intended to help EFL students understand better how to write academic texts to make their texts sound as nativelike as possible. Therefore, RTM was implemented in a writing class in which 35 students participated as the respondents of the study. They were treated with RTM and their essays were then analyzed to examine the effects of the implementation of RTM on their writing products. Besides, this study investigated further the students’ perception towards RTM in EFL writing settings. The findings of this study proved that RTM is beneficial to improve students’ writing performances and students have positive perceptions on RTM. The implications of the findings for language learning are also discussed.</p>

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28

Banica, Teodor, and Amaury Freslon. "Modeling questions for quantum permutations." Infinite Dimensional Analysis, Quantum Probability and Related Topics 21, no. 02 (June 2018): 1850009. http://dx.doi.org/10.1142/s0219025718500091.

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Анотація:

Given a quantum permutation group [Formula: see text], with orbits having the same size [Formula: see text], we construct a universal matrix model [Formula: see text], having the property that the images of the standard coordinates [Formula: see text] are projections of rank [Formula: see text]. Our conjecture is that this model is inner faithful under suitable algebraic assumptions, and is in addition stationary under suitable analytic assumptions. We prove this conjecture for the classical groups, and for several key families of group duals.

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29

Xiong, Hao, Zhongjun He, Hua Wu, and Haifeng Wang. "Modeling Coherence for Discourse Neural Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7338–45. http://dx.doi.org/10.1609/aaai.v33i01.33017338.

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Discourse coherence plays an important role in the translation of one text. However, the previous reported models most focus on improving performance over individual sentence while ignoring cross-sentence links and dependencies, which affects the coherence of the text. In this paper, we propose to use discourse context and reward to refine the translation quality from the discourse perspective. In particular, we generate the translation of individual sentences at first. Next, we deliberate the preliminary produced translations, and train the model to learn the policy that produces discourse coherent text by a reward teacher. Practical results on multiple discourse test datasets indicate that our model significantly improves the translation quality over the state-of-the-art baseline system by +1.23 BLEU score. Moreover, our model generates more discourse coherent text and obtains +2.2 BLEU improvements when evaluated by discourse metrics.

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30

Fisher, Douglas, Nancy Frey, and Diane Lapp. "Shared Readings: Modeling Comprehension, Vocabulary, Text Structures, and Text Features for Older Readers." Reading Teacher 61, no. 7 (April 2008): 548–56. http://dx.doi.org/10.1598/rt.61.7.4.

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31

Guo, Hongzhi, Zhi Sun, Jingbo Sun, and Natalia M. Litchinitser. "$\text{M}^2\text{I}$: Channel Modeling for Metamaterial-Enhanced Magnetic Induction Communications." IEEE Transactions on Antennas and Propagation 63, no. 11 (November 2015): 5072–87. http://dx.doi.org/10.1109/tap.2015.2480095.

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32

Ren, Xiaohang, Yi Zhou, Jianhua He, Kai Chen, Xiaokang Yang, and Jun Sun. "A Convolutional Neural Network-Based Chinese Text Detection Algorithm via Text Structure Modeling." IEEE Transactions on Multimedia 19, no. 3 (March 2017): 506–18. http://dx.doi.org/10.1109/tmm.2016.2625259.

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33

Tao, Jiang, Li Jia, Ma Cao Wan, and Jia Hao Meng. "The Text modeling method of Tibetan text combining Word2vec and improved TF-IDF." Journal of Physics: Conference Series 1601 (July 2020): 042007. http://dx.doi.org/10.1088/1742-6596/1601/4/042007.

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34

Escobar, Roberto A., Larysa Tryputen, Sebastian Castillo-Sepulveda, Dora Altbir, Sunjae Chung, T. N. Anh Nguyen, M. Mohseni, Johan Akerman, and Caroline A. Ross. "Monte Carlo Modeling of Mixed-Anisotropy $[\text{Co/Ni}]_{2}/\text{NiFe}$ Multilayers." IEEE Magnetics Letters 7 (2016): 1–5. http://dx.doi.org/10.1109/lmag.2015.2512530.

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35

Nemkina, E. S. "Modeling in the process of solving text problems." Elementary School, no. 3 (2022): 15–17. http://dx.doi.org/10.51906/0027-7371_2022_3_15.

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36

Nemkina, E. S. "Modeling in the process of solving text problems." Elementary School, no. 3 (2022): 15–17. http://dx.doi.org/10.51906/0027-7371_2022_3_15.

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37

Derouault, Anne-Marie, and Bernard Merialdo. "Natural Language Modeling for Phoneme-to-Text Transcription." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8, no. 6 (November 1986): 742–49. http://dx.doi.org/10.1109/tpami.1986.4767855.

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38

Desylvia, Syeiva Nurul, Agus Buono, and Bib Paruhum Silalahi. "Modeling Text Independent Speaker Identification with Vector Quantization." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 1 (March 1, 2017): 322. http://dx.doi.org/10.12928/telkomnika.v15i1.4656.

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39

Gao, Yunze, Yingying Chen, Jinqiao Wang, Ming Tang, and Hanqing Lu. "Reading scene text with fully convolutional sequence modeling." Neurocomputing 339 (April 2019): 161–70. http://dx.doi.org/10.1016/j.neucom.2019.01.094.

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40

Friedman, C., T. Borlawsky, L. Shagina, H. R. Xing, and Y. A. Lussier. "Bio-Ontology and text: bridging the modeling gap." Bioinformatics 22, no. 19 (July 26, 2006): 2421–29. http://dx.doi.org/10.1093/bioinformatics/btl405.

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41

Liu, Jingang, Chunhe Xia, Haihua Yan, Zhipu Xie, and Jie Sun. "Hierarchical Comprehensive Context Modeling for Chinese Text Classification." IEEE Access 7 (2019): 154546–59. http://dx.doi.org/10.1109/access.2019.2949175.

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42

Velantina Kapale, Miss, and Prof Mangesh Ghonge. "Detection of Optical Characters Using Text Structure Modeling." International Journal of Engineering & Technology 7, no. 3.8 (July 7, 2018): 92. http://dx.doi.org/10.14419/ijet.v7i3.8.15228.

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Анотація:

The basic use of back propagation(bp) neural network for textual content categorization and category in addition to type and could gather high everyday giant overall performance and accuracy. But, the high-quality drawback of this network is its prolonged training time. The genetic algorithm is regularly used to generate weights on network layer and beneficial for optimization. In proposed system we employ the mixture of each genetic set of rules and the returned propagation neural community for text categorization additionally the usage of the textual content structure we will understand the unique enter object through using characteristic extraction input item. We're able to use the genetic algorithm to reduce weights of edges within the but returned propagation neural community in used to backpropagating techniques. On the equal body, we are able to used to enhance the genetic algorithm to increase its performance and accuracy of neural network.

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43

Peng, Xi, Zhang Yi, Xiao-Yong Wei, De-Zhong Peng, and Yong-Sheng Sang. "Free-gram phrase identification for modeling Chinese text." Information Processing Letters 113, no. 4 (February 2013): 137–44. http://dx.doi.org/10.1016/j.ipl.2012.11.005.

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44

Li, Ximing, Changchun Li, Jinjin Chi, and Jihong Ouyang. "Short text topic modeling by exploring original documents." Knowledge and Information Systems 56, no. 2 (September 18, 2017): 443–62. http://dx.doi.org/10.1007/s10115-017-1099-0.

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45

Zha, Daochen, and Chenliang Li. "Multi-label dataless text classification with topic modeling." Knowledge and Information Systems 61, no. 1 (December 8, 2018): 137–60. http://dx.doi.org/10.1007/s10115-018-1280-0.

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46

Jang, Woosol, Ye Eun Kim, and Won Son. "Feature selection for text data via topic modeling." Korean Journal of Applied Statistics 35, no. 6 (December 31, 2022): 739–54. http://dx.doi.org/10.5351/kjas.2022.35.6.739.

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47

Zhuchkova, Irina Igorevna. "Thesaurus Modeling of English Text Typology Terminology (By the Example of the Term “Text Type”)." Filologičeskie nauki. Voprosy teorii i praktiki, no. 9 (July 2021): 2749–53. http://dx.doi.org/10.30853/phil210470.

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48

Mylläri, Sanna, Suoma Eeva Saarni, Ville Ritola, Grigori Joffe, Jan-Henry Stenberg, Ole André Solbakken, Nikolai Olavi Czajkowski, and Tom Rosenström. "Text Topics and Treatment Response in Internet-Delivered Cognitive Behavioral Therapy for Generalized Anxiety Disorder: Text Mining Study." Journal of Medical Internet Research 24, no. 11 (November 9, 2022): e38911. http://dx.doi.org/10.2196/38911.

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Background Text mining methods such as topic modeling can offer valuable information on how and to whom internet-delivered cognitive behavioral therapies (iCBT) work. Although iCBT treatments provide convenient data for topic modeling, it has rarely been used in this context. Objective Our aims were to apply topic modeling to written assignment texts from iCBT for generalized anxiety disorder and explore the resulting topics’ associations with treatment response. As predetermining the number of topics presents a considerable challenge in topic modeling, we also aimed to explore a novel method for topic number selection. Methods We defined 2 latent Dirichlet allocation (LDA) topic models using a novel data-driven and a more commonly used interpretability-based topic number selection approaches. We used multilevel models to associate the topics with continuous-valued treatment response, defined as the rate of per-session change in GAD-7 sum scores throughout the treatment. Results Our analyses included 1686 patients. We observed 2 topics that were associated with better than average treatment response: “well-being of family, pets, and loved ones” from the data-driven LDA model (B=–0.10 SD/session/∆topic; 95% CI –016 to –0.03) and “children, family issues” from the interpretability-based model (B=–0.18 SD/session/∆topic; 95% CI –0.31 to –0.05). Two topics were associated with worse treatment response: “monitoring of thoughts and worries” from the data-driven model (B=0.06 SD/session/∆topic; 95% CI 0.01 to 0.11) and “internet therapy” from the interpretability-based model (B=0.27 SD/session/∆topic; 95% CI 0.07 to 0.46). Conclusions The 2 LDA models were different in terms of their interpretability and broadness of topics but both contained topics that were associated with treatment response in an interpretable manner. Our work demonstrates that topic modeling is well suited for iCBT research and has potential to expose clinically relevant information in vast text data.

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Burchakov, A. V., I. K. Garkushin, I. M. Kondratyuk, E. M. Egorova, and S. N. Milov. "Phase Complex Modeling for the Na+,Sr2+||Cl–,$${\text{WO}}_{{\text{4}}}^{{{\text{2}}-}}$$ Three-Component Reciprocal System." Russian Journal of Inorganic Chemistry 66, no. 7 (July 2021): 1021–30. http://dx.doi.org/10.1134/s0036023621070044.

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Samanta, Gauranga C., and Nisha Godani. "Wormhole modeling supported by non-exotic matter." Modern Physics Letters A 34, no. 28 (September 13, 2019): 1950224. http://dx.doi.org/10.1142/s0217732319502249.

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In the present paper, the modeling of traversable wormholes, proposed by Morris and Thorne [Am. J. Phys. 56, 395 (1988)], is performed within the [Formula: see text] gravity with particular viable case [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text]. The energy conditions are analyzed using the shape function [Formula: see text] defined by Godani and Samanta [Int. J. Mod. Phys. D 28, 1950039 (2018)] and the geometric nature of wormholes is analyzed.

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