الفهرس 
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Abstract
The development of neural networks and machine learning techniques has recently been
the cornerstone for many applications of artificial intelligence. These applications are now found
in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly
valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier
studies, drowsy driving is responsible for 25 to 50% of all traffic accidents, which account for
1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents
caused by drowsy drivers by detecting the drowsiness of the driver and alerting him or the
responsible authority. This research tests a number of popular deep learning-based models and
presents a novel deep learning-based model for predicting driver drowsiness using a combination
of convolutional neural networks (CNN) and Long-Short-Term Memory (LSTM) to achieve
results that are superior to those of state-of-the-art methods. Utilizing convolutional layers, CNN
has excellent feature extraction abilities, whereas LSTM can learn sequential dependencies.
The National Tsing Hua University (NTHU) [20] driver drowsiness dataset is used to train
and test the model, which is compared to several other current models as well as state-of-the-art
models. The proposed model outperformed state-of-the-art models, with results up to 98.30% for
training accuracy and 97.31% for validation accuracy.