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"Optimising Automatic Text Classification Approach in Adaptive Online C" by Ya feng Zheng, Zhang hao Gao et al.

A text semantic classification is an essential approach to recognising the verbal intention of online learners, empowering reliable understanding and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current deep learning models, such as CNN and RNN, are ineffective in classifying verbal content contextually. Moreover, the contribution of verbal elements to semantics is often considerably varied, requiring the attachment of weights to these elements to increase verbal recognition precision. The Bi-LSTM is considered to be an adaptive model to investigate semantic relations according to the context. Moreover, the attention mechanism in deep learning simulating human vision could assign weights to target texts effectively. This study proposed to construct a deep learning model combining Bi-LSTM and attention mechanism, in which Bi-LSTM obtained the v ....

Adaptation Models , Attention Mechanism , Deep Learning , Feature Extraction , Long Short Term Memory Network , Nline Collaborative Discussion , Task Analysis , Ext Categorization , Text Classification ,

Intel Arc AV1 encoder beats AMD and Nvidia's H.264 encoders in early tests

The AV1 hardware encoder in Intel's new Arc A380 destroyed Nvidia and AMD's H.264 encoders in initial real-world tests this week. The results are sorely needed good. ....

Youtuber Eposvox , Arc Alchemist , Tuber Eposvox , Qualcomm Snapdragon , Halo Infinite , Intel Arc ,

BBCNEWS BBC News at One June 4, 2024 12:27:00

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