Music recommendation algorithms based on knowledge graph and multi-task feature learning.

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.

Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion.

Since December 2019, the novel coronavirus disease (COVID-19) caused by the syndrome coronavirus 2 (SARS-CoV-2) strain has spread widely around the world and has become a serious global public health problem. For this high-speed infectious disease, the application of X-ray to chest diagnosis plays a key role. In this study, we propose a chest X-ray image classification method based on feature fusion of a dense convolutional network (DenseNet) and a visual geometry group network (VGG16). This paper adds an attention mechanism (global attention machine block and category attention block) to the model to extract deep features. A residual network (ResNet) is used to segment effective image information to quickly achieve accurate classification. The average accuracy of our model in detecting binary classification can reach 98.0%. The average accuracy for three category classification can reach 97.3%. The experimental results show that the proposed model has good results in this work. Therefore, the use of deep learning and feature fusion technology in the classification of chest X-ray images can become an auxiliary tool for clinicians and radiologists.

ECG signal classification based on deep CNN and BiLSTM.

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[Formula: see text] 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.

Based on improved deep convolutional neural network model pneumonia image classification.

Pneumonia remains the leading infectious cause of death in children under the age of five, killing about 700,000 children each year and affecting 7% of the world's population. X-ray images of lung become the key to the diagnosis of this disease, skilled doctors in the diagnosis of a certain degree of subjectivity, if the use of computer-aided medical diagnosis to automatically detect lung abnormalities, will improve the accuracy of diagnosis. This research aims to introduce a deep learning technology based on the combination of Xception neural network and long-term short-term memory (LSTM), which can realize automatic diagnosis of patients with pneumonia in X-ray images. First, the model uses the Xception network to extract the deep features of the data, passes the extracted features to the LSTM, and then the LSTM detects the extracted features, and finally selects the most needed features. Secondly, in the training set samples, the traditional cross-entropy loss cannot more balance the mismatch between categories. Therefore, this research combines Pearson's feature selection ideas, fusion of the correlation between the two loss functions, and optimizes the problem. The experimental results show that the accuracy rate of this paper is 96%, the receiver operator characteristic curve accuracy rate is 99%, the precision rate is 98%, the recall rate is 91%, and the F1 score accuracy rate is 94%. Compared with the existing technical methods, the research has achieved expected results on the currently available datasets. And assist doctors to provide higher reliability in the classification task of childhood pneumonia.

Secondary structure prediction of protein based on multi scale convolutional attention neural networks.

To fully extract the local and long-range information of amino acid sequences and enhance the effective information, this research proposes a secondary structure prediction model of protein based on a multi-scale convolutional attentional neural network. The model uses a multi-channel multi-scale parallel architecture to extract amino acid structure features of different granularity according to the window size. The reconstructed feature maps are obtained via multiple convolutional attention blocks. Then, the reconstructed feature map is fused with the input feature map to obtain the enhanced feature map. Finally, the enhanced feature map is fed to the Softmax classifier for prediction. While the traditional cross-entropy loss cannot effectively solve the problem of non-equilibrium training samples, a modified correlated cross-entropy loss function may alleviate this problem. After numerous comparison and ablation experiments, it is verified that the improved model can indeed effectively extract amino acid sequence feature information, alleviate overfitting, and thus improve the overall prediction accuracy.

Recommendation algorithm based on attributed multiplex heterogeneous network.

In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are applied to recommendation systems. To solve the problem of inaccurate recommendations caused by processing deficiencies in large networks, this paper combines the attributed multiplex heterogeneous network with the attention mechanism that introduces the softsign and sigmoid function characteristics and derives a new framework SSN_GATNE-T (S represents the softsign function, SN represents the attention mechanism introduced by the Softsign function, and GATNE-T represents the transductive embeddings learning for attribute multiple heterogeneous networks). The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. No matter how many nodes and types are included in the model, our model can handle it well, and the improved attention mechanism can help annotations to obtain more useful information via a combination of the two. This can help to mine more potential information to improve the recommendation effect; in addition, the application of the softsign function in the fully connected layer of the model can better reduce the loss of potential user information, which can be used for accurate recommendation by the model. Using the Adam optimizer to optimize the model can not only make our model converge faster, but it is also very helpful for model tuning. The proposed framework SSN_GATNE-T was tested for two different types of datasets, Amazon and YouTube, using three evaluation indices, ROC-AUC (receiver operating characteristic-area under curve), PR-AUC (precision recall-area under curve) and F1 (F1-score), and found that SSN_GATNE-T improved on all three evaluation indices compared to the mainstream recommendation models currently in existence. This not only demonstrates that the framework can deal well with the shortcomings of obtaining accurate interaction information due to the presence of a large number of nodes and edge types of the embedding of large network models, but also demonstrates the effectiveness of addressing the shortcomings of large networks to improve recommendation performance. In addition, the model is also a good solution to the cold start problem.

Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method.

Protein secondary structure prediction is one of the most important and challenging problems in bioinformatics. Machine learning techniques have been applied to solve the problem and have gained substantial success in this research area. However there is still room for improvement toward the theoretical limit. In this paper, we present a novel method for protein secondary structure prediction based on a data partition and semi-random subspace method (PSRSM). Data partitioning is an important strategy for our method. First, the protein training dataset was partitioned into several subsets based on the length of the protein sequence. Then we trained base classifiers on the subspace data generated by the semi-random subspace method, and combined base classifiers by majority vote rule into ensemble classifiers on each subset. Multiple classifiers were trained on different subsets. These different classifiers were used to predict the secondary structures of different proteins according to the protein sequence length. Experiments are performed on 25PDB, CB513, CASP10, CASP11, CASP12, and T100 datasets, and the good performance of 86.38%, 84.53%, 85.51%, 85.89%, 85.55%, and 85.09% is achieved respectively. Experimental results showed that our method outperforms other state-of-the-art methods.

Microstructure transformation of PDMS-E grafted gelatin polymers induced by SDS and SDBS.

Inorganic-organic hybrid materials with tunable chemical and physical properties were prepared from mono epoxy terminated polydimethylsiloxane (PDMS) macromonomer and gelatin for improving their flexibility and hydrophobicity. Sodium dodecyl sulfate (SDS) and sodium dodecyl benzene sulfonate (SDBS) were used to enhance the compatibility of two polymers phases. Measurement of grafting density indicated that anionic surfactants played a crucial role in deciding the detailed microstructure of PDMS-E grafted gelatin (PGG) polymers in alkaline solution. The interaction between gelatin and SDS/SDBS was investigated by viscosity and SEM. Viscosity analysis showed a regular increase in SDS system and a steeper change in the case of SDBS. SEM micrographs displayed a series of structural transitions (spherical, spindle, irregular granular and spherical aggregates) with the increase of SDS concentration, but spindle and granular aggregates appeared alternately as varying SDBS concentrations. The results demonstrated that both the electrostatic and hydrophobic interactions between anionic surfactant and gelatin controlled the aggregate structure of gelatin-SDS/SDBS, which affected the compatibility between gelatin and PDMS. Thermal properties of PGG polymers had changed with the modification of polymer microstructure. The results above revealed that microstructure transformation of PGG polymers was determined by the compatibility of two polymers in anionic surfactant aqueous solution and the chemical nature of their monomers.

SEM-EDS technique employed in evaluating the aggregation behaviors of amphiphilic ABC-type triblock copolymers in mixed solvents with tuned polarities.

In this paper, a scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) technique has been developed for evaluating the aggregation structure of amphiphilic fluorinated ABC-type triblock copolymers MeOPEO(16)-PSt(220)-PFHEA(22) in mixed solvents with different polarities. The polarities of mixed solvents can be tuned by changing volume ratios of toluene, anhydrous ethanol, and distilled water, which leads to the changes in morphology and size of self-assembled colloidal particles of the copolymers in the system. The aggregation behaviors of the copolymers are revealed by SEM, transmission electron microscopy (TEM), and corresponding SEM-EDS techniques. The variations in concentrations of O and F elements over the thickness of copolymers particles give direct evidence for a better understanding of the arrangement of each block segment of copolymers in solution. And the technique can also help to explain the aggregation structure of micro- or nanomaterial with shell-core structure. Microsc. Res. Tech., 2011. © 2011 Wiley Periodicals, Inc.