Advancing Biotechnological Research with a Recurrent Time Convolutional Network: A Novel Approach to Data Analysis and Pattern Recognition

Abstract

This study explores the application of a novel deep learning framework, the Recurrent Time Convolutional Network (RTCNN), initially developed for piano music recommendation and demonstrates its broader utility in biotechnological research. The RTCNN, designed to learn complex sequential patterns from raw data with minimal supervision, shows potential beyond its initial musical domain. In our experiments, the RTCNN successfully categorized six popular music genres with an accuracy rate of 73.88%, showcasing its capability to discern intricate patterns in diverse datasets. Further, the paper investigates the application of RTCNN in monitoring and improving health outcomes, specifically through music therapy for autistic children. Significant improvements were observed across several dimensions, including emotional response, communication ability, motor coordination, and cognitive functions. These results highlight the RTCNN’s potential in intelligent health monitoring, suggesting a promising avenue for its use in analyzing behavioral and cognitive patterns in biotechnological settings. The ability of the RTCNN to handle complex, sequential data could be crucial in developing more effective biotechnological applications, such as patient monitoring and personalized therapy interventions.