Multimodal sentiment analysis has been an active subfield in natural language processing. This makes multimodal sentiment tasks challenging due to the use of different sources for predicting a speaker’s sentiment. Previous research has focused on extracting single contextual information within a modality and trying different modality fusion stages to improve prediction accuracy. However, a factor that may lead to poor model performance is that this does not consider the variability between modalities. Furthermore, existing fusion methods tend to extract the representational information of individual modalities before fusion. This ignores the critical role of intermodal interaction information for model prediction. This paper proposes a multimodal sentiment analysis method based on cross-modal attention and gated cyclic hierarchical fusion network MGHF. MGHF is based on the idea of distribution matching, which enables modalities to obtain representational information with a synergistic effect on the overall sentiment orientation in the temporal interaction phase. After that, we designed a gated cyclic hierarchical fusion network that takes text-based acoustic representation, text-based visual representation, and text representation as inputs and eliminates redundant information through a gating mechanism to achieve effective multimodal representation interaction fusion. Our extensive experiments on two publicly available and popular multimodal datasets show that MGHF has significant advantages over previous complex and robust baselines.

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Background: LncRNAs (Long non-coding RNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides. LncRNA has been novel candidate biomarkers in cancer diagnosis and prognosis. However, it is difficult to discover the true association mechanism between lncRNAs and complex diseases. The unprecedented enrichment of multi-omics data and the rapid development of machine learning technology provide us with the opportunity to design a machine learning framework to study the relationship between lncRNAs and complex diseases. Results: In this article, we proposed a new machine learning approach, namely LGDLDA (LncRNA-Gene-Disease association networks based LncRNA-Disease Association prediction), for disease-related lncRNAs association prediction based multi-omics data, machine learning methods and neural network neighborhood information aggregation. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively. LGDLDA calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. LGDLDA obtains gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix. LGDLDA obtains disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs. Conclusions: Compared with lncRNA-disease prediction methods, IHI-BMLLR takes into account more critical information and obtains the performance improvement cancer-related lncRNA predictions. Randomly split data experiment results show that the stability of LGDLDA is better than IDHI-MIRW, NCPLDA, LncDisAP and NCPHLDA. The results on different simulation data sets show that LGDLDA can accurately and effectively predict the disease-related lncRNAs. Furthermore, we applied LGDLDA to three real cancer data including gastric cancer, colorectal cancer and breast cancer to predict potential cancer-related lncRNAs.

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Now deep learn-based object detection can be deployed on drones for criminalinvestigation or military counter-terrorism. Because the proportion of pixels of pedestrians orvehicles in the aerial picture taken by UAV is very small, the probability of detection of smallobjects in the distance is very low or there are omissions. In this paper, the HPS-YOLOv7 algorithmis proposed to improve the detection accuracy of small objects. We have proposed a modifiedhigh-efficiency layer aggregation network for feature extraction, solved the problem that theconvergence of depth models tends to worsen, and lightly processed models with a Bottleneckstructure. We have proposed C-recursively gated convolution, which fully fuses shallow objectsemantic information and enhances the model capacity. To be more helpful for detect small objects,the detection head of 20×20 was replaced by 160×160 detection head, and shallow feature fusionnetwork (SFN) was connected to make up for the information lost by small objects in the deepconvolutional network. Mosaic data enhancement and a priori anchor adaptive adjustment strategyare used in model training to improve the detection efficiency and accuracy. Experimentalevaluation was carried out on VisDrone2019 and Tinyperson data sets respectively. The results showthat mAP increases by 3.0% and 13.29% compared with yolov7 on the basis of IoU=0.5. mAP of0.5≤IoU≤0.95 increased by 1.8% and 3.97%; It is shown that the advantages of HPS-YOLOv7 insmall object detection have certain theoretical value and practical significance.

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