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In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimensional (1D) features of EEG signals into many segments and employ 1D-CNNs. Moreover, these investigations are further constrained by the absence of consideration for temporal links between time series segments or spectrogram images. Therefore, we propose a Dual-Modal Information Bottleneck (Dual-modal IB) network for EEG seizure detection. The network extracts EEG features from both time series and spectrogram dimensions, allowing information from different modalities to pass through the Dual-modal IB, requiring the model to gather and condense the most pertinent information in each modality and only share what is necessary. Specifically, we make full use of the information shared between the two modality representations to obtain key information for seizure detection and to remove irrelevant feature between the two modalities. In addition, to explore the intrinsic temporal dependencies, we further introduce a bidirectional long-short-term memory (BiLSTM) for Dual-modal IB model, which is used to model the temporal relationships between the information after each modality is extracted by convolutional neural network (CNN). For CHB-MIT dataset, the proposed framework can achieve an average segment-based sensitivity of 97.42%, specificity of 99.32%, accuracy of 98.29%, and an average event-based sensitivity of 96.02%, false detection rate (FDR) of 0.70/h. We release our code at https://github.com/LLLL1021/Dual-modal-IB.

Sign language recognition (SLR) is a useful tool for the deaf-mute to communicate with the outside world. Although many SLR methods have been proposed and have demonstrated good performance, continuous SLR (CSLR) is still challenging. Meanwhile, due to the heavy occlusions and closely interacting motions, there is a higher requirement for the real-time efficiency of CSLR. Therefore, the performance of CSLR needs further improvement. The highlights include: (1) to overcome these challenges, this paper proposes a novel video-based CSLR framework. This framework consists of three components: an OpenPose-based skeleton stream extraction module, a RGB stream extraction module, and a combination module of the BiLSTM network and the conditional hidden Markov model (CHMM) for CSLR. (2) A new residual network with Squeeze-and-Excitation blocks (SEResNet50) for video sequence feature extraction. (3) This paper combines the SEResNet50 module with the BiLSTM network to extract the feature information from video streams with different modalities. To evaluate the effectiveness of our proposed framework, experiments are conducted on two CSL datasets. The experimental results indicate that our method is superior to the methods in the literature.

Aiming at the low accuracy of the track signal intrusion detection (IDe) algorithm in the traditional cloud-side collaborative computing environment, this paper proposes a deep learning (D-L)-based track signal IDe method in the cloud edge collaborative computing environment. First, the main framework of the IDe method is constructed by comprehensively considering the backbone network, network transmission and ground equipment, and edge computing (EC) is introduced to cloud services. Then, the The CNN (Convolutional Neural Networks)-attention-based BiLSTM (Bi-directional Long Short-Term Memory) neural network is used in the cloud center layer of the system to train the historical data, a D-L method is proposed. Finally, a pooling layer and a dropout layer are introduced into the model to effectively prevent the overfitting of the model and achieve accurate detection of track signal intrusion. The purpose of introducing the pooling layer is to accelerate the model convergence, remove the redundancy and reduce the feature dimension, and the purpose of introducing the dropout layer is to prevent the overfitting of the model. Through simulation experiments, the proposed IDe method and the other three methods are compared and analyzed under the same conditions. The results show that the F1 value of the method proposed in this paper is optimal under four different types of sample data. The F1 value is the lowest of 0.948 and the highest of 0.963. The performance of the algorithm is better than the other three comparison algorithms. The method proposed in this paper is important for solving the IDe signal in the cloud-edge cooperative environment, and also provides a theoretical basis for tracking the signal IDe direction.

Aiming at the problem of low accuracy rate of current sentiment analysis methods for book review texts, a book review sentiment analysis method based on BERT-ABiLSTM hybrid model is proposed. First, the overall framework of sentiment analysis is constructed by integrating sentiment vocabulary and deep learning methods, and the fine-grained sentiment analysis is divided into three stages: topic identification, sentiment identification and thematic sentiment identification. Then, a dynamic character-level word vector containing contextual information is generated using a bidirectional encoder representation from transformers (BERT) pre-trained language model. Then, the contextual information in the text data is fully learned by introducing the bidirectional long short-term memory (BiLSTM) model. Finally, the accurate analysis of book review sentiment is achieved by using Attention mechanism to highlight important features and improve the efficiency of resource utilization. Through an experimental comparison with existing advanced algorithms, the proposed method in this study has improved at least 4.2%, 3.9% and 3.79% in precision, recall and F1 values, respectively. The experimental results show that the proposed BERT-ABiLSTM is higher than the existing models under different metrics, indicating that the proposed model has a good application prospect in the fields of book review analysis and book recommendation.