Traffic Sign Recognition using CNN and Keras

Sahu, Monika

doi:10.22214/ijraset.2021.34602

Cited by 1 publication

(1 citation statement)

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“…To align with the memory capacity of the GPU, a batch size of 128 was utilized during the GPU acceleration phase. In terms of the experimental setup, Python libraries, specifically TensorFlow [ 43 ] and Keras [ 44 ], were employed to implement and train the GELT model given their efficient functionalities. For model optimization and hyperparameter tuning, we leveraged the Adam algorithm [ 45 ] to optimize all trainable parameters.…”

Section: Experiments and Resultsmentioning

confidence: 99%

GELT: A graph embeddings based lite-transformer for knowledge tracing

Liang,

Wu,

Liang

et al. 2024

PLoS ONE

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The development of intelligent education has led to the emergence of knowledge tracing as a fundamental task in the learning process. Traditionally, the knowledge state of each student has been determined by assessing their performance in previous learning activities. In recent years, Deep Learning approaches have shown promising results in capturing complex representations of human learning activities. However, the interpretability of these models is often compromised due to the end-to-end training strategy they employ. To address this challenge, we draw inspiration from advancements in graph neural networks and propose a novel model called GELT (Graph Embeddings based Lite-Transformer). The purpose of this model is to uncover and understand the relationships between skills and questions. Additionally, we introduce an energy-saving attention mechanism for predicting knowledge states that is both simple and effective. This approach maintains high prediction accuracy while significantly reducing computational costs compared to conventional attention mechanisms. Extensive experimental results demonstrate the superior performance of our proposed model compared to other state-of-the-art baselines on three publicly available real-world datasets for knowledge tracking.

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Section: Experiments and Resultsmentioning

confidence: 99%