NOVEL HYBRID APPROACH TO TAXONOMIC CLASSIFICATION USING DEEP LEARNING TECHNIQUES

Authors

  • Pabboju Mounika, Gade Vishnavi, Gundam Poojitha

Keywords:

Deeplearning;CNN; RNN;DNA;randomprojection;wavelettransform; taxonomicclassification

Abstract

This paper presents a novel hybrid approach for taxonomic classification leveraging advanced deep learning techniques to improve accuracy and efficiency in categorizing diverse biological entities. Taxonomic classification is crucial in various fields, including ecology, agriculture, and biodiversity conservation, as it aids in the identification and understanding of species relationships. The proposed method integrates Convolutional Neural Networks (CNNs) with recurrent neural networks (RNNs) to capture both spatial and temporal features from the data, thereby enhancing classification performance. We evaluate the hybrid model using a comprehensive dataset of images and textual descriptions from multiple taxonomic categories. Experimental results demonstrate that our approach significantly outperforms traditional classification methods, achieving higher accuracy rates and faster processing times. Additionally, we explore the model’s adaptability to varying data types, emphasizing its potential for real-world applications in ecological monitoring and species identification. This research contributes to the ongoing development of robust machine learning frameworks that can effectively address the complexities of taxonomic classification in an increasingly data-driven world.

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