@article{Jiang_Hu_Qi_Neal Mazur_2021, title={A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud}, volume={1}, url={https://ojs.istp-press.com/jait/article/view/9}, DOI={10.37965/jait.2020.0037}, abstractNote={<p>As one chemical composition, nicotine content has an important influence on the quality of tobacco leaves. Rapid and non-destructive quantitative analysis of nicotine is an important task in the tobacco industry. Near-infrared (NIR) spectroscopy as an effective chemical-composition analysis technique has been widely used. In this paper, we propose a one-dimensional Fully Convolutional Network (1D-FCN) model to quantitatively analyze the nicotine composition of tobacco leaves using NIRspectroscopy data in a cloud environment. This 1D-FCN model uses one-dimension convolution layers to directly extract the complex features from sequential spectroscopy data. It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss.Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing.Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches. This research provides a deep learning foundation for quantitative analysis of NIR spectra data in the tobacco industry.</p>}, number={1}, journal={Journal of Artificial Intelligence and Technology}, author={Jiang, Daiyu and Hu, Gang and Qi, Guanqiu and Neal Mazur}, year={2021}, month={Jan.}, pages={74–82} }