At the Mahakumbh, Faith Met Tragedy: Computational Analysis of Stampede Patterns Using Machine Learning and NLP

This study employs machine learning, historical analysis, and natural language processing (NLP) to examine recurring lethal stampedes at Indias mass religious gatherings, focusing on the 2025 Mahakumbh tragedy in Prayagraj (48+ deaths) and its 1954 predecessor (700+ casualties). Through computational modeling of crowd dynamics and administrative records, it investigates how systemic vulnerabilities contribute to these disasters. Temporal trend analysis identifies persistent choke points, with narrow riverbank access routes linked to 92% of past stampede sites and lethal crowd densities (eight or more persons per square meter) recurring during spiritually significant moments like Mauni Amavasya. NLP analysis of seven decades of inquiry reports reveals cyclical administrative failures, where VIP route prioritization diverted safety resources in both 1954 and 2025, exacerbating fatalities. Statistical modeling demonstrates how ritual urgency overrides risk perception, leading to panic propagation patterns that mirror historical incidents. Findings support the Institutional Amnesia Theory, highlighting how disaster responses remain reactionary rather than preventive. By correlating archival patterns with computational crowd behavior analysis, this study frames stampedes as a collision of infrastructure limitations, socio spiritual urgency, and governance inertia, challenging disaster discourse to address how spiritual economies normalize preventable mortality.

Meursault as a Data Point

In an era dominated by datafication, the reduction of human experiences to quantifiable metrics raises profound philosophical and ethical questions. This paper explores these issues through the lens of Meursault, the protagonist of Albert Camus' The Stranger, whose emotionally detached existence epitomizes the existential concept of absurdity. Using natural language processing (NLP) techniques including emotion detection (BERT), sentiment analysis (VADER), and named entity recognition (spaCy)-this study quantifies key events and behaviors in Meursault's life. Our analysis reveals the inherent limitations of applying algorithmic models to complex human experiences, particularly those rooted in existential alienation and moral ambiguity. By examining how modern AI tools misinterpret Meursault's actions and emotions, this research underscores the broader ethical dilemmas of reducing nuanced human narratives to data points, challenging the foundational assumptions of our data-driven society. The findings presented in this paper serve as a critique of the increasing reliance on data-driven narratives and advocate for incorporating humanistic values in artificial intelligence.

Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms

This study addresses the need for accurate and efficient object detection in assistive technologies for visually impaired individuals. We evaluate four real-time object detection algorithms YOLO, SSD, Faster R-CNN, and Mask R-CNN within the context of indoor navigation assistance. Using the Indoor Objects Detection dataset, we analyze detection accuracy, processing speed, and adaptability to indoor environments. Our findings highlight the trade-offs between precision and efficiency, offering insights into selecting optimal algorithms for realtime assistive navigation. This research advances adaptive machine learning applications, enhancing indoor navigation solutions for the visually impaired and promoting accessibility.