No abstract

Alcohol dependence is a common mental disease worldwide. Excessive alcohol consumption may lead to alcoholism and many complications. In severe cases, it will lead to inhibition and paralysis of the centers of the respiratory and circulatory systems and even death. In addition, there is a lack of effective standard test procedures to detect alcoholism. EEG signals are data obtained by measuring brain changes in the cerebral cortex and can be used for the diagnosis of alcoholism. Existing diagnostic methods mainly employ machine learning techniques, which rely on human intervention to learn. In contrast, deep learning, as an end-to-end learning method, can automatically extract EEG signal features, which is more convenient. Nonetheless, there are few studies on the classification of alcohol’s EEG signals using deep learning models. Therefore, in this paper, a new deep learning method is proposed to automatically extract and classify EEG’s features. The method first adopts a multilayer discrete wavelet transform to denoise the input data. Then, the denoised data are used as input, and a convolutional neural network and bidirectional long short-term memory network are used for feature extraction. Finally, alcohol EEG signal classification is performed. The experimental results show that the method proposed in this study can be utilized to effectively diagnose patients with alcoholism, achieving a diagnostic accuracy of 99.32%, which is better than most current algorithms.

No abstract

With the development of artificial intelligence technology, deep learning is widely used as a method to extract features from complex networks. However, deep learning models often run in cloud computing data centers with powerful computing capabilities. Traditional cloud computing methods rely heavily on the network, which has high latency, and has problems of security and privacy. Edge computing complements cloud computing by performing tasks at the edge of the network, resulting in significant reductions in system operation time, memory cost, and power consumption. At the same time, because it is deployed in an edge computing environment, network performance can be optimized and user privacy can be protected. This review discusses the application of deep learning on the Internet of things(IOT) in the environment of edge computing, compares the results of edge computing and cloud computing in the field of deep learning, shows the superiority of the edge computing. This paper introduces the commonly used method of edge computing, and at the same time puts forward the possible problems of edge computing in the field of deep learning.Finally, we make a prospect for the future in the cross field of edge computing and deep learning.