AI-Augmented Religious Practice: Neural Intelligence Frameworks for the Transformation of Ritual Systems

Abstract

The incorporation of artificial intelligence within digital spiritual and ritual systems has revolutionized the field of human-computer interaction in culturesensitive settings. In this study, an artificial intelligence-supported ritual behavior prediction scheme through the Generalized Simplicial Attention Neural Network (GSANN) algorithm is proposed to address the modeling of highly complicated human-to-ritual interactions. The primary aim of this study is to model ritual behavior prediction to include factors such as context, emotion, and culture in order to create more intelligent and adaptive digital ritual systems. This is achieved by the use of the synthetically constructed Human-Ritual Interaction Dataset that includes the user’s context, emotion, culture, and ritual activity attributes in the ratio of 70% training, 15% validation, and 15% testing sets. Data cleaning, handling of missing values, encoding, and normalization are part of the preprocessing phase to enhance the data quality. The proposed GSANN model is designed based on the Python 3.9 programming language, which uses the PyTorch framework, and its performance is measured in terms of accuracy, precision, recall, and F1 score. According to the experimental results, the proposed model offers an accuracy rate of 97.2%, surpassing current approaches like Machine Learning (83.2%), Random Forest (78.6%), and Artificial Neural Networks (80.1%).