AUTOMATING METER CLASSIFICATION OF ARABIC POEMS: A HARRIS HAWKS OPTIMIZATION WITH DEEP LEARNING PERSPECTIVE

Badriyya B. Al-Onazi, Majdy M. Eltahir, Muhammad Swaileh A. Alzaidi, Shouki A. Ebad, Shoayee Dlaim Alotaibi and Ahmed Sayed
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Badriyya B. Al-Onazi: Department of Arabic Language and Literature, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Majdy M. Eltahir: Department of Information Systems, Applied College at Mahayil, King Khalid University, Saudi Arabia
Muhammad Swaileh A. Alzaidi: Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Shouki A. Ebad: Department of Computer Science, Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia
Shoayee Dlaim Alotaibi: Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Saudi Arabia
Ahmed Sayed: Research Center, Future University in Egypt, New Cairo 11835, Egypt

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-15

Abstract: Meter classification in Arabic poetry is a crucial factor that describes the rhythmic structure of poems. Classical Arabic poetry relies on explicit meters, referred to as “Arud†(), to create a structured and harmonious flow. Arabic meter is based on the pattern of short and long syllables, and each meter has a particular combination of feet (taf’ilah) that defines its unique rhythmic structure. Poets use diverse Arabic meters to evoke aesthetic or emotional qualities in their poetry. The mastery of meter is considered a sophisticated and skillful aspect of traditional Arabic poetry, which reflects the rich heritage of Arabic literature. The meter provides poets with unique opportunities and constraints, influencing the style and tone of their verses. Using deep learning (DL) for the meter classification of Arabic poems includes leveraging a neural network to automatically learn the features and patterns that discriminate between various meters. This paper presents a Fractal Harris Hawks Optimization with DL-based Meter Classification of Arabic Poems (HHODL-MCAP) technique. The HHODL-MCAP technique exploits the optimal DL model for the identification of distinct classes of meters of Arabic poems. The HHODL-MCAP technique involves a three-layered process. Primarily, the HHODL-MCAP technique performs data preprocessing to transform the data into a beneficial format. Second, the HHODL-MCAP technique applies long short-term memory (LSTM) with a Bidirectional Temporal Convolutional Networks (BiTCNs) model for the automated identification of various Arabic meter classes. At last, the HHO algorithm can be exploited to choose the hyperparameter values of the LSTM-BiTCN model optimally. A series of experiments were conducted to ensure the improved detection outcomes of the HHODL-MCAP technique. The extensive simulation results underline the supremacy of the HHODL-MCAP technique in the meter classification process.

Keywords: Arabic Poems; Meter Classification; Fractal Harris Hawks Optimization; Deep Learning; Long Short Term Memory (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25400080

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