METHODS FOR ANALYZING REAL-TIME WEB USERS USING ARTIFICIAL INTELLIGENCE
Ключевые слова:
Keywords: Real-Time Web User Analysis, Artificial Intelligence (AI), TensorFlow, libraries : scikit-learn and paho-mqtt , machine learning, natural language processing, predictive analytics.Аннотация
Abstract: In today’s digital era, businesses and organizations heavily rely on web-based platforms to reach, engage, and convert their audience. Understanding user behavior in real time has become a critical aspect of decision-making for marketing, design, cybersecurity, and performance optimization. The evolution of Artificial Intelligence (AI) has enabled more sophisticated, accurate, and scalable analysis of real-time web user interactions. By leveraging machine learning, deep learning, and natural language processing, AI systems can detect patterns, predict user actions, personalize experiences, and identify anomalies—often within milliseconds. This paper explores the main methods and technologies used for analyzing real-time web users through AI, highlighting technical strategies, tools, and use cases.
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