Published in Data Science and Engineering (DSE) Record 2024 Vol. 5 No. 1 pp. 169-206
Abstract
The work presents the application of complex-valued deep learning for classifying microbial organisms, highlighting its significance for rapid pathogen identification crucial in healthcare. It explores the efficiency of complex-valued neural networks over traditional real-valued networks, focusing on efficiency, computational resource usage, and accuracy in genome sequencing classification. The research employs theoretical analysis and empirical testing, comparing the performance of complex-valued and real-valued models. Findings indicate that complex-valued CNNs offer advantages in encoding genomic sequences and processing efficiency. The study’s significance lies in its potential to advance pathogen classification methods, offering insights into the practical trade-offs between model complexity and computational efficiency, and contributing to the development of more effective tools for epidemic prevention and control