Classification of English Translated Holy Quran & Generating Verses Based on Search Index

Abstract

The English translation of the Holy Quran serves as an essential guide for spiritual enlightenment and wisdom globally. This research emphasizes the application of advanced deep learning techniques for classifying and generating verses from this translated Quran based on search indexes. This study explores the profound significance of making these teachings accessible worldwide and highlights the transformative impact of deep learning advancements in facilitating deeper understanding and interpretation. Utilizing models such as RNNs, CNNs, and our own Hybrid model, our research achieves significant classification accuracy. Specifically, RNN models all kinds and CNNs achieved accuracy above 85% while the Hybrid model surpassed them with 99.91%. These models enable efficient categorization and also this study focuses on retrieval of Quranic verses based on search queries, thereby enhancing accessibility and usability. This research underscores the significance of integrating religious teachings with cutting-edge deep learning innovations. By integrating deep learning into the study of religious texts, we facilitate scholarly research, educational tools, and personalized spiritual exploration.