Comparative study of EEG motor imagery classification based on DSCNN and ELM

Fast recognition model of Fusarium in agaric based on hyperspectral imaging

“…SFS is an algorithm used for dimensionality reduction and is suitable for filtering feature data by comparing selected initial feature values with other feature values to finally identify the feature variables that meet the requirements (Edward & Weidong, 2021; Sahameh et al., 2021). The ELM is a classifier with fast learning capability and good adaptation to the model, related to the number of hidden neural nodes, of which the parameters set in this study are determined by interval cyclic selection (Jixiang et al., 2023; Satapathy et al., 2019). BP is modeled by fitting through hidden nodes with learning rate settings to achieve improved accuracy by calculating minimum error back propagation (Jianguo et al., 2022; Yuanqiang et al., 2022).…”

“…Different fungi have different spectral values and compositions, and compositions of different fungi can be used to determine the spectrum technology's sensitivity to biomolecules (Rodriguez et al., 2013). In recent years, deep learning research has become a trend, as well as algorithms such as BP (Back Propagation) and CNN (Convolutional Neural Networks) have received a lot of attention and have been applied in many fields such as brain science, food, and medicine (Jixiang et al., 2023; TranDacThinh et al., 2021). By combining principal component analysis, clustering analysis, artificial neural networks, and other stoichiometric and machine learning methods (Rodriguez et al., 2013; Saha & Manickavasagan, 2021), various characteristics of different pathogenic fungi can be determined based on hyperspectral image information.…”