Bi-Channel Generative Recurrent Network-Based Identification of Marathi Poems
Keywords:
Bi-Channel Generative Recurrent Network, Marathi Poems, Adaptive Median Filter, Kernel Principal Component Analysis, ClassificationAbstract
The term "Marathi poems" refers to writings in the Marathi language, which is largely used in the Indian state of Maharashtra and certain surrounding areas. With contributions from notable poets over the ages, Marathi poetry has a rich legacy and a lengthy history. It is used to display various perspectives. Every poet has a particular purpose and point of view when we classifies the poem. A recurrent network that recognizes Marathi poetry may be trained using a dataset of Marathi poems, where each poem is represented as a collection of words or characters. The poem was categorized in the suggested way utilizing terms from several categories by its thoughts. The poem's classification is determined using the machine learning method Bi-Channel Generative Recurrent Network (BI-CGRN) classifier. Additionally, this method allows users to search for poems depending on the name and category of author. The recommended technique surpasses earlier approaches for 336 poems, increasing the BI-CGRN classification's accuracy. To evaluate the performance of the suggested approach, the dataset is used. The noisy data are taken out of the samples of raw data using the Adaptive Median Filter (AMF). The properties are extracted using a Kernel Principal Component Analysis (KPCA). The results of the research demonstrate that accuracy, precision, f1-score, and recall measures to illustrate the performance of poetry for five categories, including "Friend," "Prem," "Bhakti," "Prerna," and "Desh," are important. The recommended method makes it easier to identify and categorize Marathi poetry, which may help to preserve and promote Marathi literary history.
Downloads
References
Patil, R. S., & Kolhe, S. R. (2022). Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets. Social Network Analysis and Mining, 12(1), 51.
Saini, J. R., & Bafna, P. B. (2022, January). MaTop: An Evaluative Topic Model for Marathi. In Proceedings of Third International Conference on Sustainable Computing: SUSCOM 2021 (pp. 135-144). Singapore: Springer Nature Singapore.
Ruma, J. F., Akter, S., Laboni, J. J., & Rahman, R. M. (2022). A deep learning classification model for Persian Hafez poetry based on the poet’s era. Decision Analytics Journal, 4, 100111.
Shelke, M., Sawant, D. D., Kadam, C. B., Ambhure, K., & Deshmukh, S. N. (2023). Marathi SentiWordNet: A lexical resource for sentiment analysis of Marathi. Concurrency and Computation: Practice and Experience, e7497.
Deák, D. (2020). RESEARCHING MUSLIM SAINTS OF THE MARATHI DECCAN: SOME PROBLEMS AND CHALLENGES. Asian & African Studies (13351257), 29(2).
Bafna, P. B., & Saini, J. R. (2020). An Application of Zipf's Law for Prose and Verse Corpora Neutrality for Hindi and Marathi Languages. International Journal of Advanced Computer Science and Applications, 11(3).
Bafna, P. B., & Saini, J. R. (2020). Marathi document: similarity measurement using semantics-based dimension reduction technique. International Journal of Advanced Computer Science and Applications, 11(4).
Venkatkrishnan, A. (2020). Leaving Kashi: Sanskrit knowledge and cultures of consumption in eighteenth-century South India. The Indian Economic & Social History Review, 57(4), 567-581.
Nerlekar, A. (2020). The LCD (Lowest Common Denominator) of Language: The Materialist Poetry of Arun Kolatkar and RK Joshi. South Asia: Journal of South Asian Studies, 43(5), 943-969.
Deshmukh, M. (2020). The Mothers and Daughters of Bhakti: Janābāī in Marathi Literature. International Journal of Hindu Studies, 24, 33-59.
Saini, J. R., & Bafna, P. B. (2022, January). MaTop: An Evaluative Topic Model for Marathi. In Proceedings of Third International Conference on Sustainable Computing: SUSCOM 2021 (pp. 135-144). Singapore: Springer Nature Singapore.
Digamberrao, K. S., & Prasad, R. S. (2018). Author identification using sequential minimal optimization with rule-based decision tree on Indian literature in Marathi. Procedia computer science, 132, 1086-1101..
Glushkova, I. (2021). Janabai and Gangakhed of Das Ganu: Towards ethnic unity and religious cohesion in a time of transition. The Indian Economic & Social History Review, 58(4), 505-532.
Naik, R. R., Landge, M. B., & Mahender, C. N. (2019). Word level plagiarism detection of marathi text using N-Gram approach. In Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India, December 21–22, 2018, Revised Selected Papers, Part III 2 (pp. 14-23). Springer Singapore.
Naik, R. R., & Landge, M. B. (2019). Plagiarism detection in marathi language using semantic analysis. In Scholarly Ethics and Publishing: Breakthroughs in Research and Practice (pp. 473-482). IGI Global.
Bafna, P. B., & Saini, J. R. (2020). An Application of Zipf's Law for Prose and Verse Corpora Neutrality for Hindi and Marathi Languages. International Journal of Advanced Computer Science and Applications, 11(3).
Chakravarti, A. (2023). Time, Space and Loneliness in Bengali and Marathi Poetry. In The Routledge History of Loneliness (pp. 131-148). Routledge.
Boukhroufa-Trijaud, M. (2022). How the Internet is Transforming the Bombay Poetry Scene. Sillages critiques, (33).
Deshmukh, R., & Kiwelekar, A. W. (2022, March). Deep Convolutional Neural Network Approach for Classification of Poems. In Intelligent Human Computer Interaction: 13th International Conference, IHCI 2021, Kent, OH, USA, December 20–22, 2021, Revised Selected Papers (pp. 74-88). Cham: Springer International Publishing.
Shelke, M. B., & Deshmukh, S. N. (2020). Recent advances in sentiment analysis of Indian languages. International Journal of Future Generation Communication and Networking, 13(4), 1656-1675.
Deshmukh, R. A. (2022). Naive Bayes and Neural Network Techniques for Marathi Poem Classification into Nine Rasa using Feature Selection. International Journal of Performability Engineering, 18(9).
Ravi, C., Yasmeen, Y., Masthan, K. ., Tulasi, R. ., Sriveni, D. ., & Shajahan, P. . (2023). A Novel Machine Learning Framework for Tracing Covid Contact Details by Using Time Series Locational data & Prediction Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 204–211. https://doi.org/10.17762/ijritcc.v11i2s.6046
Tanaka, A., Min-ji, K., Silva, C., Cohen, D., & Mwangi, J. Predictive Analytics for Healthcare Resource Allocation. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/150
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
