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

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” ([Formula: see text]), 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.