Study of the Organization of the Qur’anic Surahs Using the Similarity-Based Approach in Deep Learning

According to numerous studies, the Qur’anic surahs exhibit internal structure and organization, with each surah serving a distinct purpose. Although each surah focuses on a specific theme and the Qur’an identifies ۱۱۴ broad themes, the arrangement of the surahs and the remarkable similarity between adjacent surahs (neighbors) underscores the chain-link and deliberate positioning of the surahs within the Qur’an. To investigate this phenomenon, a multifaceted and compound model was developed, comprising two main parts: embedding and autoencoding. The first part was carried out by preparing the words and roots of the Qur’anic text using the BERT model for meaning-topic representation. In the second part, the data was clustered in a soft labeling mode by the autoencoder. Analysis of the distribution of surahs within clusters revealed that neighboring surahs exhibited an average similarity of ۸۰, while surahs with greater distance showed an average similarity of ۲۰. The findings support the placement of similar surahs in close proximity,  substantiating the organized sequence of Qur’anic surahs. To conclude, the results provide compelling evidence for the structured arrangement of Qur’anic surahs.

کلیدواژه ها:

the Qur’an ، Deep Learning ، Deep neural network ، Clustering ، surah similarity ، Natural Language Processing