Optimizing Gated Recurrent Units with Gerbil Inspired Techniques (GGRU) for Superior Data Classification and Clustering

The proposed GGRU algorithm presents an innovative approach to enhance the performance of Gated Recurrent Units (GRU) using a series of gerbil-inspired optimization techniques. GGRU introduces key innovations, such as behavioral parameter tuning, resource-efficient learning, adaptive memory mechanisms, exploratory optimization pathways, convergence acceleration techniques, and sustainability-driven pruning. These enhancements are targeted to improve the accuracy, efficiency, and robustness of GRU in handling complex classification and clustering tasks. By focusing on optimizing memory usage, reducing computational overhead, and accelerating convergence the GGRU addresses several critical challenges in sequential data processing. The combination of the bio-inspired strategies enables GGRU to achieve superior results in various metrics such as classification accuracy, F-measure, clustering effectiveness, and balanced performance metrics. This makes GGRU a highly effective model for applications requiring precise and reliable data analysis, positioning it as one of the most powerful tools for advanced machine learning tasks that demand both accuracy and efficiency.