Background: Currently, cardiovascular disease has become a major disease endangering human health, and the number of such patients is growing. Electrocardiogram (ECG) is an important basis for {medical doctors to diagnose the cardiovascular disease, which can truly reflect the health of the heart. In this context, the contradiction between the lack of medical resources and the surge in the number of patients has become increasingly prominent. The use of computer-aided diagnosis of cardiovascular disease has become particularly important, so the study of ECG automatic classification method has a strong practical significance.Methods: This article proposes a new method for automatic identification and classification of ECG.We have developed a dense heart rhythm network that combines a 24-layer Deep Convolutional Neural Network (DCNN) and Bidirectional Long Short-Term Memory (BiLSTM) to deeply mine the hierarchical and time-sensitive features of ECG data. Three different sizes of convolution kernels (32, 64 and 128) are used to mine the detailed features of the ECG signal, and the original ECG is filtered using a combination of wavelet transform and median filtering to eliminate the influence of noise on the signal. A new loss function is proposed to control the fluctuation of loss during the training process, and convergence mapping of the tan function in the range of 0–1 is employed to better reflect the model training loss and correct the optimization direction in time.Results: We applied the dataset provided by the 2017 PhysioNet/CINC challenge for verification. The experiment adopted ten-fold cross validation,and obtained an accuracy rate of 89.3 $$\%$$% and an F1 score of 0.891.Conclusions: This article proposes its own method in the aspects of ECG data preprocessing, feature extraction and loss function design. Compared with the existing methods, this method improves the accuracy of automatic ECG classification and is helpful for clinical diagnosis and self-monitoring of atrial fibrillation.

Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies this method of the ECG data set provided by the 2017 PhysioNet/CINC challenge. After cross validation, this method can obtain 87.1% accuracy and the F1 score is 86.46%. Compared with the existing classification method, our proposed algorithm has higher accuracy and generalization ability for ECG signal data classification.

During music recommendation scenarios, sparsity and cold start problems are inevitable. Auxiliary information has been utilized in music recommendation algorithms to provide users with more accurate music recommendation results. This study proposes an end-to-end framework, MMSS_MKR, that uses a knowledge graph as a source of auxiliary information to serve the information obtained from it to the recommendation module. The framework exploits Cross & Compression Units to bridge the knowledge graph embedding task with recommendation task modules. We can obtain more realistic triple information and exclude false triple information as much as possible, because our model obtains triple information through the music knowledge graph, and the information obtained through the recommendation module is used to determine the truth of the triple information; thus, the knowledge graph embedding task is used to perform the recommendation task. In the recommendation module, multiple predictions are adopted to predict the recommendation accuracy. In the knowledge graph embedding module, multiple calculations are used to calculate the score. Finally, the loss function of the model is improved to help us to obtain more useful information for music recommendations. The MMSS_MKR model achieved significant improvements in music recommendations compared with many existing recommendation models.

The integration of multi-modality images significantly enhances the clarity of critical details for object detection. Valuable semantic data from object detection enriches the fusion process of these images. However, the potential reciprocal relationship that could enhance their mutual performance remains largely unexplored and underutilized, despite some semantic-driven fusion methodologies catering to specific application needs. To address these limitations, this study proposes a mutually reinforcing, dual-task-driven fusion architecture. Specifically, our design integrates a feature-adaptive interlinking module into both image fusion and object detection components, effectively managing the inherent feature discrepancies. The core idea is to channel distinct features from both tasks into a unified feature space after feature transformation. We then design a feature-adaptive selection module to generate features rich in target semantic information and compatible with the fusion network. Finally, effective combination and mutual enhancement of the two tasks are achieved through an alternating training process. A diverse range of swift evaluations is performed across various datasets to corroborate the potential efficiency of our framework, actualizing visible advancements in both fusion effectiveness and detection accuracy.

In the field of image fusion, the fusion of infrared and visible images emerges as a crucial work, retaining the inherent characteristics of each source image for the creation of high-quality fusion images. However, existing fusion algorithms primarily prioritize visual performance and statistical metrics, often neglecting the requisites of high-level vision tasks. To bridge this gap, our paper proposes SIGFusion, an end-to-end method for infrared and visible image fusion. SIGFusion not only considers the visual performance and statistical metrics of the fused image but also focuses on meeting the demands of subsequent high-level vision tasks, thereby enhancing performance in fusion and high-level vision tasks. Concretely, we define significant semantic information as salient targets presenting in both infrared and visible images. By utilizing targets, our fusion network is guided to enhance these salient targets. In contrast to the isolated employ of either infrared or visible images, our target-based method comprehensively utilizes the semantic information within the image. Additionally, the utilization of target-based self-attention loss enhances the extraction of vital information from the source image. It is worth noting that masks are only necessary during the training stage and are automatically generated through detection algorithms. We conduct experiments in both image fusion and high-level vision tasks. Experimental results demonstrate the effectiveness of SIGFusion in both infrared and visible image fusion tasks, as well as in subsequent high-level vision tasks. The source code is available at https://github.com/poison-pig/SIGFusion.