Users often use tags to annotate and categorize web content. A folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags. The most significant feature of a folksonomy is that it directly reflects the vocabulary of users. This feature is very useful in tag-based content searching and user browsing. Based on mutual-overlapping measurement of tag's instance sets, an ontology learning algorithm to construct concept hierarchy from folksonomy is proposed. A case study of datasets from a famous Chinese e-business website taobao is carried out. The precision, valid, recall and F-measure rates of the constructed concept hierarchy are 54%, 84%, 100% and 70% respectively. The experimental results on real world datasets show that the proposed method is feasible.
Variety identification of seeds is essential to ensure the purity and yield of the variety. A model based on hyperspectral imaging technology and one-dimensional convolutional neural network (1D CNN) was proposed to distinguish soybean seed varieties in this article. A total of 3,600 soybean seeds (900 seeds per variety) of hyperspectral images in the spectral range of 866.4-1,701.0 nm were collected.Traditional machine learning models (k-nearest neighbor, support vector machines, partial least squares discriminant analysis) and 1D CNN model were established based on different numbers of sample sets. The 1D CNN model was the most stable and had the highest classification accuracy, higher than 98%. Fivefold cross validation was used to evaluate the model, and the model achieved an accuracy of more than 95% in both the training set and the validation set. Finally, the t-distributed stochastic neighbor embedding was used to visualize the feature values extracted by 1D CNN.The research results demonstrated that the 1D CNN model maintained good classification performance in soybean seed classification and had good application prospects in hyperspectral imaging technology.
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