Due to the rapid growth of network data, the authenticity and reliability of network information have become increasingly important and have presented challenges. Most of the methods for fake review detection start with textual features and behavioral features. However, they are time-consuming and easily detected by fraudulent users. Although most of the existing neural network-based methods address the problems presented by the complex semantics of reviews, they do not account for the implicit patterns among users, reviews, and products; additionally, they do not consider the usefulness of information regarding fine-grained aspects in identifying fake reviews. In this paper, we propose an attention-based multilevel interactive neural network model with aspect constraints that mines the multilevel implicit expression mode of reviews and integrates four dimensions, namely, users, review texts, products and fine-grained aspects, into review representations. We model the relationships between users and products and use these relationships as a regularization term to redefine the model’s objective function. The experimental results from three public datasets show that the model that we propose is superior to the state-of-the-art methods; thus showing the effectiveness and portability of our model.

The interaction between microbes and drugs encompasses the sourcing of pharmaceutical compounds, microbial drug degradation, the development of drug resistance genes, and the impact of microbial communities on host drug metabolism and immune modulation. These interactions significantly impact drug efficacy and the evolution of drug resistance. In this study, we propose a novel predictive model, termed GCGACNN. We first collected microbe, disease, and drug association data from multiple databases and the relevant literature to construct three association matrices and generate similarity feature matrices using Gaussian similarity functions. These association and similarity feature matrices were then input into a multi-layer Graph Neural Network for feature extraction, followed by a two-dimensional Convolutional Neural Network for feature fusion, ultimately establishing an effective predictive framework. Experimental results demonstrate that GCGACNN outperforms existing methods in predictive performance.

Recently, the character-based model that incorporates potential word information has proven effective for Chinese named entity recognition (NER). However, due to the independence of the pre-trained character model and the lexicon, it will cause the embedding space to be misaligned and cannot be combined well. Chinese pre-trained encoders usually process text as characters. It ignores the information carried by the larger granular information, so the encoder cannot easily adapt to certain character combinations. Because large-grained information is ignored and Chinese does not have clear character boundaries, this will lead to the loss of important semantic information, which is an important problem for Chinese. In this paper, we propose PTWA: pre-training with word attention for Chinese named entity recognition. PTWA uses multi-head word attention to form a word vector from multiple word vectors, and proposes a word length prediction task to better integrate the word vector into pre-training. With the powerful capabilities of the transformer, PTWA can explicitly make full use of potential word information without adding an external lexicon, and can coexist with pre-trained models that implicitly use word information (such as BERT-WWM, and ERNIE). Experiments conducted on four Chinese NER datasets show that the performance of PTWA is better than other word-word models and Chinese pre-training models.

This paper studies intuitionistic fuzzy (IF) decision making problems using an integrated approach. Firstly, an IF induced generalized ordered weighted averaging distance (IFIGOWAD) operator is introduced, that covers numerous IF aggregations distance operators. Then, two more generalized operators named IF quasi induced OWAD (Quasi-IFIOWAD) and IF induced generalized hybrid average distance (IFIGHAD) operator are presented. Furthermore, a new method using above operators is introduced to IF decision making problem and online trade systems. Finally, two real cases are present to show the effect of the method.

Predicting Herb–Disease Associations (HDA) is pivotal for modernizing Traditional Chinese Medicine (TCM); however, this is impeded by data heterogeneity and the complex, multi-component mechanisms of herbal medicines. Existing drug–disease prediction models often struggle to capture high-order structural patterns and resolve semantic inconsistencies intrinsic to herbs. To overcome these limitations, we present HData, a standardized benchmark dataset that integrates herbal medicinal properties, chemical compositions, and disease associations. We further propose GHF-ACL, a novel multi-order graph contrastive learning framework designed for HDA prediction. Specifically, GHF-ACL explicitly models low-order functional similarities via a herb–disease similarity graph while capturing high-order component interactions through a herb–chemical hypergraph. Furthermore, an adaptive gating-guided structural interaction module aligns heterogeneous graph representations into a unified latent space, and hierarchical contrastive learning enforces consistency across structural views. Extensive experiments on five datasets demonstrate that GHF-ACL achieves superior or competitive performance over six state-of-the-art models across most metrics, with significant improvements over the best-performing baseline model in AUPR (+4.8% on LRSSL, + 3.81% on Cdata), F1 score, and Recall. These results underscore the model’s superior capability in detecting true positive associations within imbalanced biomedical data. By synergizing multi-view graph modeling, semantic fusion, and contrastive regularization, this work establishes a unified framework for HDA prediction, offering valuable insights for computational TCM and data-driven drug discovery.