Advanced Data-Driven Approaches for Intelligent Olfaction

Abstract

Advanced data-driven approaches have transformed the development of intelligent systems, gaining recognition from researchers and industrialists. Data plays a critical role in shaping intelligent systems, including artificial olfaction systems (AOS). AOS has evolved from manual feature extraction to leveraging artificial neural networks (ANNs) and convolutional neural networks (CNNs) for automated feature extraction. This chapter comprehensively overviews the synergy between data-driven approaches and CNNs in intelligent AOS. CNNs have significantly improved the accuracy and efficiency of scent and odor detection in AOS by automating feature extraction. Exploiting abundant data and leveraging CNN capabilities can enhance AOS performance. However, challenges and opportunities remain, requiring further research and development for optimal utilization of data-driven approaches in intelligent AOS.