مجلة الاربعين المحكمة
The Arbaeen pilgrimage continuous to show increase in the size of the crowed, emphasizing the essential demand for precise analytical system to optimize crowed organization, service managing, and decision support. The present work develops two frameworks that employ predictive statistical modeling combined with contemporary artificial intelligence based solutions for managing this extensive religious occasion. In this regrad, the first framework involves a thorough analysis on established and other types of forecasting methods such as Box–Jenkins time series models, nonparametric regressions (Nadaraya–Watson and Epanechnikov kernels), and so on. One-way ANOVA—combined with multilayer perceptron models applied to forecast health indicators such as pilgrim birth rates. Inspired by best practices in the classification of Arbaeen films; the second framework proposes Comparison Replicating best practices from the Arbaeen film scene classification, the second strand, a video scene recognition method is proposed based on transfer learning from Inception V3 and VGG-16. These neural networks trained to recognize and distinguish between images from the Arbaeen procession and other mass gatherings, this system improves deeper machine-based understanding of context and support the implementation of AI-driven surveillance systems.Using transfer learning results in ingesting and interpreting largescale pilgrimage data. Engineered to suit Arbaeen’s distinct characteristics,this plan also provides a flexible foundation for managing similar large-scale events such as other eligious or public gatherings. To support real time monitoring we explore visual scene recognition after presenting forecasting.