Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

Abstract

The goal of this article is to analyze the problem of low computational efficiency and propagation error rate in entity recognition and relation extraction. This paper proposes a personalized education resource recommendation algorithm framework XMAMBLSTM based on deep learning in an intelligent education robot environment. XMAMBLSTM uses XLNet to assign word vectors to text sequences, employs a Multi-Bi-LSTM layer to represent complex information of word vectors, and combines a multi-headed attention layer to realize weight distribution of each word vector. The experimental results show that compared with the traditional collaborative filtering algorithm, the comprehensive evaluation indexes of the proposed method, based on the intelligent education robot environment on the two platforms, are higher than 5.05% and 17.3%, respectively.

Steel Surface Defect Detection Based on SSAM-YOLO

“…However, these methods are all based on hand-made features, which depend on the experience of the designers and are not suitable for steel with defects of various types and configurations. In recent years, the bloom of deep learning (Li & Bo, 2023;Lin, Z. et al, 2023;P. Sun, 2023) prompts the development of various fields, including steel surface detection.…”