INTEGRATING HYBRID DEEP LEARNING PARADIGMS FOR ACCURATE TAXONOMIC IDENTIFICATION

Abstract

Taxonomic classification plays a vital role in biological research, ecology, and medical sciences, where accurate identification of species is essential for knowledge discovery and decision-making. Traditional classification methods often rely on manual expertise or shallow machine learning models, which face limitations in handling high-dimensional, heterogeneous, and noisy biological datasets. This research proposes a hybrid deep learning approach that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) and attention mechanisms to achieve precise taxonomic identification. The CNN component extracts spatial and morphological features, while the RNN captures sequential patterns inherent in genetic and phenotypic data. The attention layer further enhances model interpretability by prioritizing the most relevant features. Experimental evaluations conducted on benchmark taxonomic datasets demonstrate significant improvements in classification accuracy, robustness, and generalization compared to conventional methods. The proposed framework highlights the potential of hybrid deep learning paradigms in advancing automated taxonomy, paving the way for scalable, accurate, and intelligent biological classification systems