IJAEMS
IJAEMS
Dynamic operational environments generate continuously evolving data, making conventional anomaly detection and predictive analytics increasingly ineffective in capturing changing patterns and emerging system behaviors. Conventional anomaly detection and prediction techniques are less effective in dynamic environments because the patterns, the data itself, and changes in the operational environment are constantly evolving. This paper presents an adaptive artificial intelligence (AI) model for real-time anomaly detection and predictive analytics. The proposed framework continuously processes the incoming data, recognizes any deviations from the normal behavior, and dynamically updates its learning patterns to adapt to the changing conditions. Inconsistencies are cleaned and relevant features are extracted from streaming data during data preprocessing. The adaptive learning mechanism is used to identify normal and abnormal patterns by analyzing these features, and the predictive analytics module is employed to estimate potential future events and risks by analyzing historical and real-time observations. The model has been designed to include continuous feedback to enhance its detection capability and to minimize the effect of concept drift. Real-time processing helps to detect unusual events quickly so that appropriate responses can be taken and better decisions can be made. The framework is tailored for applications like industrial monitoring, cybersecurity systems, smart systems, and data-driven applications. Experimental results show the proposed adaptive AI approach is able to effectively identify anomalies while allowing for high prediction accuracy under varying data conditions. The findings demonstrate the potential for improving system reliability, responsiveness, and operational efficiency by continuously adapting systems and leveraging real-time analytics.