Cross-Domain Adaptation Of Intelligent Systems In Heterogeneous Environments

Abstract

Contemporary intelligent systems are increasingly being deployed in heterogeneous settings such as the Internet of Things (IoT) and Industrial Internet of things (IIoT), which are fraught with differences in protocols, traffic flows, data patterns, etc., making adaptation extremely difficult for the models. However, the current learning models are highly dependent on the underlying data, and consequently, it is difficult to expand to other data. The goal of this research is to improve domain-invariant knowledge transfer and generalization in heterogeneous systems using an effective and generalized cross-domain adaptive learning model. In order to provide a transportable domain invariant representation, an intelligent kernel search recurrent neural network (Int-KS-RNN) is proposed. It is based on recurrent neural networks (RNNs), which describe temporal correlations, and increased feature mapping using intelligent kernel search (Int-KS). By using Min-max normalization to scale down each individual input feature, input values are converged faster, through independent component analysis (ICA), which captures statistically independent and compact domain-invariant features that enhance effective generalization in heterogeneous systems while avoiding redundancy. Simulation is performed in Python to demonstrate stability across heterogeneous datasets with the best performance (detection rate (98.46%), high accuracy (98.48%), F1 score (0.98), and AUC (0.9828) and a very low rate of false alarms (0.72%), compared with the existing model. The suggested model provides a dependable, generalized, and extendable way to increase adaptability for intelligent systems deployment in complicated, diverse contexts.