IoT-AI Security for Dynamic Data-driven Environments.

With the high amount of data in applications like healthcare, education, and finance, there is a requirement for sophisticated analytical methods that can tune in real-time and generate precise forecasts. In this paper, a proposed methodology is introduced that is a novel model combining both artificial intelligence (AI) and machine learning (ML) for predictive analytics enhancement in a dynamic setting. Technologies presented are AI-assisted pre-processing of data that detects faults and extracts relevant features, and machine learning algorithms that dynamically tune the model parameters. The resilience mechanism is the most critical component in the working of the proposed methodology that utilizes adaptive learning rates and anomaly detection to enable the model to detect noise and anomalies. Furthermore, a feedback loop is included in the model to enable continuous optimization based on performance metrics to increase accuracy. The following work discusses the design of the proposed methodology, includes an algorithm, discusses its applicability in different industries, and the benefits of the proposed model compared to existing predictive analytics models, specifically in the ability to rapidly update data and detect anomalies.