DELAYED DECISION PATTERN DETECTION IN DIGITAL PLATFORMS
Keywords:
Delayed Decision Detection, User Behavior Analysis, Interaction Delay Patterns, Long Short-Term Memory (LSTM), Deep Learning, Real-Time Analytics, Time-Series Modeling, Human–Computer Interaction, Bot Detection, Behavioral Biometrics, Web Analytics, Sequential Pattern Detection, Digital Platforms, Temporal Data Analysis, User Intent Prediction.Abstract
This project presents a real-time delayed decision pattern detection system for digital platforms using deep learning techniques, specifically the Long Short-Term Memory (LSTM) model. In modern web applications, understanding user behavior during an active session is essential for improving user experience, increasing conversion rates, and enhancing security. Traditional analytics tools mainly focus on retrospective metrics such as session duration, page views, or conversion outcomes, which fail to capture the temporal dynamics of user interactions. This project addresses that limitation by analyzing the time gaps between consecutive user actions, such as clicks, scrolls, and navigation events, to identify behavioral patterns. The proposed system treats these interaction delays as sequential time-series data and uses an LSTM-based neural network to learn temporal dependencies within user sessions. Based on the learned patterns, the system classifies user behavior into categories such as decisive users, hesitant users, and anomalous or bot-like users. The architecture integrates a React-based frontend for capturing user interaction events, a Node.js and Express backend for processing and session management, a MongoDB database for storing interaction sequences, and a Python-based machine learning service for real-time inference. By continuously analyzing interaction rhythms during an active session, the system can generate predictions with confidence scores and provide immediate feedback or adaptive responses. This enables platforms to offer proactive assistance to confused users, optimize interface design, and detect suspicious automated activity. The proposed approach is also privacy-friendly since it relies solely on timing data rather than personal or contextual information. Overall, this project demonstrates how temporal behavioral analytics combined with deep learning can transform conventional web analytics into an intelligent real-time system that improves both user engagement and platform security.
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Published
2026-03-16
How to Cite
P. ASHOK KUMAR, MUKKU GAYATRI SWETHA, PULI SWATHI, MOHAMMAD RIYAZ, CHINNAM UDAY, & DOI: 10.48047/IJCNIS.18.3.69. (2026). DELAYED DECISION PATTERN DETECTION IN DIGITAL PLATFORMS. International Journal of Communication Networks and Information Security (IJCNIS), 18(3), 61–69. Retrieved from http://www.ijcnis.org/index.php/ijcnis/article/view/8870
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Section
Research Articles
