Intelligent incremental classification using a dynamic grasshopper-enhanced neural network for data streams.

ABSTRACT: Complex data streams are highly dynamic, large-scale, and prone to continuous distributional shifts, posing significant challenges for neural network-based classification systems-particularly in maintaining accuracy and efficiency without frequent retraining. To address these issues, this study proposes an intelligent incremental learning framework that integrates a Dynamic Grasshopper Optimization Algorithm (DGOA) with a Multilayer Perceptron (MLP) neural network for real-time hyperparameter optimization. The proposed system functions as an adaptive intelligent system, utilizing DGOA-an enhanced form of the traditional Grasshopper Optimization Algorithm equipped with dynamic parameter control and online swarm reconfiguration-to autonomously adjust to evolving data characteristics. Unlike

SUBMITTER: Darwish SM 

PROVIDER: S-EPMC12949023 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature