In recent years, with the popularity of social media, users are increasingly keen to express their feelings and opinions in the form of pictures and text, which makes multimodal data with text and pictures the con tent type with the most growth. Most of the information posted by users on social media has obvious sentimental aspects, and multimodal sentiment analysis has become an important research field. Previous studies on multimodal sentiment analysis have primarily focused on extracting text and image features separately and then combining them for sentiment classification. These studies often ignore the interaction between text and images. Therefore, this paper proposes a new multimodal sentiment analysis model. The model first eliminates noise interference in textual data and extracts more important image features. Then, in the feature-fusion part based on the attention mechanism, the text and images learn the internal features from each other through symmetry. Then the fusion features are applied to sentiment classification tasks. The experimental results on two common multimodal sentiment datasets demonstrate the effectiveness of the proposed model.

With continuous developments in deep learning, image semantic segmentation technology has also undergone great advancements and been widely used in many fields with higher segmentation accuracy. This paper proposes an image semantic segmentation algorithm based on a deep neural network. Based on the Mask Scoring R-CNN, this algorithm uses a symmetrical feature pyramid network and adds a multiple-threshold architecture to improve the sample screening precision. We employ a probability model to optimize the mask branch of the model further to improve the algorithm accuracy for the segmentation of image edges. In addition, we adjust the loss function so that the experimental effect can be optimized. The experiments reveal that the algorithm improves the results.

A knowledge graph is a collection of fact triples, a semantic network composed of nodes and edges. Link prediction from knowledge graphs is used to reason about missing parts of triples. Common knowledge graph link prediction models include translation models, semantics matching models, and neural network models. However, the translation models and semantic matching models have relatively simple structures and poor expressiveness. The neural network model can easily ignore the overall structural characteristics of triples and cannot capture the links between entities and relations in low‐dimensional space. In response to the above problems, we propose a knowledge graph embedding model based on a relational memory network and convolutional neural network (RMCNN). We encode triple embedding vectors using a relational memory network and decode using a convolutional neural network. First, we will obtain entity and relation vectors by encoding the latent dependencies between entities and relations and some critical information and keeping the translation properties of triples. Then, we compose a matrix of head entity encoding embedding vector, relation encoding embedding vector, and tail entity embedding encoding vector as the input of the convolutional neural network. Finally, we use a convolutional neural network as the decoder and a dimension conversion strategy to improve the information interaction capability of entities and relations in more dimensions. Experiments show that our model achieves significant progress and outperforms existing models and methods on several metrics.

This paper introduces a new concept called q-rung picture uncertain linguistic fuzzy sets (q-RPULSs). These sets provide a reliable and comprehensive method for describing complex and uncertain decision-making information. In addition, q-RPULSs help to integrate the decision maker’s quantitative assessment ideas with qualitative assessment information. For the q-RPUL multi-attribute group decision-making problem with unknown weight information, an entropy-based fuzzy set method for q-rung picture uncertainty language is proposed. The method considers the interrelationships among attributes and builds a q-rung picture uncertain language model. In addition, the q-RPULMSM operator and its related properties are discussed in this paper. This operator enables the fusion of q-RPULSs and helps to reach consensus in decision-making scenarios. To demonstrate the validity of the methodology, we provide a real case study involving commodity selection. Based on this case study, the reasonableness and superiority of the method are evaluated, highlighting the practical advantages and applicability of q-RPULSs in decision-making processes.

This paper explores the combination of a classic mathematical function named “hyperbolic tangent” with a metaheuristic algorithm, and proposes a novel hybrid genetic algorithm called NSGA-II-BnF for multi-objective decision making. Recently, many metaheuristic evolutionary algorithms have been proposed for tackling multi-objective optimization problems (MOPs). These algorithms demonstrate excellent capabilities and offer available solutions to decision makers. However, their convergence performance may be challenged by some MOPs with elaborate Pareto fronts such as CFs, WFGs, and UFs, primarily due to the neglect of diversity. We solve this problem by proposing an algorithm with elite exploitation strategy, which contains two parts: first, we design a biased elite allocation strategy, which allocates computation resources appropriately to elites of the population by crowding distance-based roulette. Second, we propose a self-guided fast individual exploitation approach, which guides elites to generate neighbors by a symmetry exploitation operator, which is based on mathematical hyperbolic tangent function. Furthermore, we designed a mechanism to emphasize the algorithm’s applicability, which allows decision makers to adjust the exploitation intensity with their preferences. We compare our proposed NSGA-II-BnF with four other improved versions of NSGA-II (NSGA-IIconflict, rNSGA-II, RPDNSGA-II, and NSGA-II-SDR) and four competitive and widely-used algorithms (MOEA/D-DE, dMOPSO, SPEA-II, and SMPSO) on 36 test problems (DTLZ1–DTLZ7, WGF1–WFG9, UF1–UF10, and CF1–CF10), and measured using two widely used indicators—inverted generational distance (IGD) and hypervolume (HV). Experiment results demonstrate that NSGA-II-BnF exhibits superior performance to most of the algorithms on all test problems.

Knowledge graph embedding (KGE), which applies representation learning to represent entities and relationships in knowledge graphs, has attracted significant attention from researchers due to its potential applications in various domains. However, most of the existing KGE methods suffer from the limitation of using single semantic information. This limitation fails to capture the complex and structural information in knowledge graphs (KGs). In this paper, we introduce a novel method called the Structure-Aware Enhanced Multi- Partition Embedding Interaction (SAMI) model for Knowledge Graph Embedding (KGE). SAMI leverages both graph attention network and tensor decomposition to learn expressive and structural enhanced representations for KGs. Specifically, it uses the graph attention layers to aggregate nodes' features in a neighborhood as an encoder and utilizes an Enhanced Multi- Partition Embedding Interaction (EMEI) to learn independent local features as a decoder. SAMI shows impressive results on several popular datasets compared with baseline methods in terms of both Mean Reciprocal Rank (MRR) and Hits@K.