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A Cnn-Lstm-Based Deep Learning Approach For Driver Drowsiness Prediction, Mohamed Gomaa
A Cnn-Lstm-Based Deep Learning Approach For Driver Drowsiness Prediction, Mohamed Gomaa
Journal of Engineering Research
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 at 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. This research tests a number of popular …
A Cnn-Lstm-Based Deep Learning Approach For Driver Drowsiness Prediction, Mohamed Gomaa
A Cnn-Lstm-Based Deep Learning Approach For Driver Drowsiness Prediction, Mohamed Gomaa
Journal of Engineering Research
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 at 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. This research tests a number of popular …
Neutrosophic Adaptive Lsb And Deep Learning Hybrid Framework For Ecg Signal Classification, Abdallah Rezk, Ahmed S. Sakr, H. M. Abdulkader
Neutrosophic Adaptive Lsb And Deep Learning Hybrid Framework For Ecg Signal Classification, Abdallah Rezk, Ahmed S. Sakr, H. M. Abdulkader
Applied Mathematics & Information Sciences
This paper proposes a novel hybrid framework for ECG signal classification and privacy preservation. The framework includes two phases: the first phase uses LSTM+CNN with attention gate for ECG classification, while the second phase utilizes adaptive least signal bit with neutrosophic for hiding important data during transmission. The proposed framework converts data into three sets of degrees (true, false, and intermediate) using neutrosophic and passes them to an embedding layer. In the sender part, the framework hides important data in ECG signal as true and false degrees, using the intermediate set as a shared dynamic key between sender and receiver. …