02442nas a2200253   4500000000100000000000100001008004100002260001200043653001600055653003200071653002100103653001100124653001600135100001500151700001500166700001600181700002000197245005600217856005800273300001100331490001300342520181900355022001402174       9998                            d  c09/202510aArud Meters10aNatural Language Processing10aPattern Matching10aPoetry10aUrdu Ghazal1 aAsia Zaman1 aZia-Ud-Din1 aSajid Iqbal1 aAsma Al Shuhail00aUPMVM: A Metrics Verification Model for Urdu Poetry  uhttps://www.ijimai.org/journal/bibcite/reference/3589  a1 - 150 vIn press3 aUrdu poetry retains a prominent position in the cultural heritage of Urdu language. Rhyme schemes and meters are frequently employed in poetry, which follow specific patterns and structures. Natural Language Processing has the capacity to recognize and analyze these patterns, which is beneficial in the investigation of poetic forms. This research presents the UPMVM (Urdu Poetry Metrics Verification Model), a novel rulebased architecture, designed for detecting meter of any given Urdu ghazal verse. In this work, we propose an algorithm that consists of sixteen steps that identifies the Arud meter in the Urdu verses using a custom developed system. This application will not only assist professional poets but also enable students to examine poetry within the framework of prosody principles. The accurate analysis of the prosody of any poetry relies on the act of uttering words rather than on a written record. UPMVM consists of two phases: 1) The primary objective of the initial phase is to consolidate all available literature of the Arud system into a unified digital platform, then develop individual and combined DFA of each identified meter for pattern recognition; 2) the second phase is about the algorithmic implementation. All these rhythmical patterns are matched with 290 Arud meters and their sub-meters developed during this study. The implementation strategy of phase 2 comprises of five essential sub-phases including tokenization, orthography, syllable identification, weight assignment, and meter detection. For evaluation of the proposed method, three different datasets are used for feature extraction, token identification and performance measurement for identification of rhythmic patterns in Urdu poetry. The UPMVM model reached to promising outcome with an average accuracy of 94%.  a1989-1660