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Reveal the mysteries of birth, death and so life has become one of the main purpose of bioinformatics, splice site prediction is one of the most important part, however, not been able to get this problem solved. Firstly, the third generation of genetic markers of single nucleotide polymorphisms had been used in that research to explore the influence of the SNP in splicing; Secondly, a modified hidden Markov model has been introduced; finally, experiments show that the SNP for the performance has a certain influence. In addition, location information based hidden Markov model designed also has positive effects. This method increases the effects dramatically than currently used methods.

The wide dissemination of rumor is increasingly threatening both individuals and society. In this paper, we propose a multi-domain rumor detection method based on sentiment features and attention mechanism. This method uses sentiment analysis technology to extract sentiment features from text, and uses attention mechanism to weight text features and domain features to detect rumor. Experimental results show that the proposed method has achieved good results on data sets from multiple fields and has good generalization performance.

The widespread dissemination of fake news on social media platforms can cause serious social impact, making the detection of fake news on social media platforms an urgent problem to be solved. Up to now, scholars have proposed various methods ranging from traditional manual feature extraction to deep learning algorithms for detecting fake news. However, these methods still have some limitations and two difficult problems: (1) How to learn informative news feature representations without losing information as much as possible? (2) How to effectively fuse multi-modal information to obtain high-order complementary information about news and enhance fake news detection? To overcome these two difficulties, this article proposes a multi-modal fusion fake news detection model. Firstly, the model uses BERT and VGG-19 to obtain the text and image feature representations of news content, respectively, and then further fuses multi-modal information through a multi-modal attention mechanism module to obtain high-order complementary information between different modalities, thereby obtaining informative news feature representations for fake news detection. Experimental results on two real-world public datasets demonstrate the effectiveness of our model compared to mainstream detection methods.

This paper addresses the limitations of existing rumor detection methods that heavily rely on single or local features, which restrict their ability to capture comprehensive and detailed characteristics of rumors. The main objective of this study is to enhance the efficiency of rumor detection. To achieve this, we propose a novel approach that integrates user attributes, comment structure, and propagation models, introducing the concept of ubiquitous relationships for messages in social networks. We construct a Tweet-word-user ubiquitous relationship network using a propagation model and further leverage the Graph Convolutional Neural Network (GCN) to enhance semantic features. Consequently, we present a novel rumor detection model, the Ubiquitous Relationship-based Graph Convolutional Neural Network (U-GCN), which effectively combines user, text, and comment features within a unified framework, while also enhancing semantic features from the source post for comprehensive detection. Extensive experiments are conducted on two publicly available Twitter Datasets. The results demonstrate that our proposed U-GCN model achieves an accuracy rate of above 0.9, outperforming methods that solely consider single or local features. Our findings highlight the effectiveness of leveraging ubiquitous relationships in rumor detection.

At present, a large number of rumors are mixed in with various kinds of news, such as current affairs, politics, social economy, and military activities, which seriously reduces the credibility of Internet information and hinders the positive development of various fields. In previous research on rumors, most scholars have focused their attention on the textual features, contextual semantic features, or single-emotion features of rumors but have not paid attention to the chain reaction caused by the hidden emotions in comments in social groups. Therefore, this paper comprehensively uses the emotional signals in rumor texts and comments to extract emotional features and determines the relationship between them to establish dual-emotion features. The main research achievements include the following aspects: (1) this study verifies that, in the field of affective characteristics, the combination of rumor-text emotion and comment emotion is superior to other baseline affective characteristics, and the detection performance of each component is outstanding; (2) the results prove that the combination of dual-emotion features and a semantic-feature-based detector (BiGRU and CNN) can improve the effectiveness of the detector; (3) this paper proposes reconstructing the dataset according to time series to verify the generalization ability of dual affective features; (4) the attention mechanism is used to combine domain features and semantic features to extract more fine-grained features. A large number of data experiments show that the dual-emotion features can be effectively compatible with an existing rumor detector, enhance the detector's performance, and improve the detection accuracy. [ABSTRACT FROM AUTHOR]

With the widespread adoption of social media, the rapid dissemination of rumors poses a severe threat to public perception and social stability, emerging as a major challenge confronting society. Hence, the development of efficient and accurate rumor detection models has become an urgent need. Given the challenges of rumor detection tasks, including data scarcity, feature complexity, and difficulties in cross-task knowledge transfer, this paper proposes a BERT–GCN–Transfer Learning model, an integrated rumor detection model that combines BERT (Bidirectional Encoder Representations from Transformers), Graph Convolutional Networks (GCNs), and transfer learning techniques. By harnessing BERT's robust text representation capabilities, the GCN's feature extraction prowess on graph-structured data, and the advantage of transfer learning in cross-task knowledge sharing, the model achieves effective rumor detection on social media platforms. Experimental results indicate that this model achieves accuracies of 0.878 and 0.892 on the Twitter15 and Twitter16 datasets, respectively, significantly enhancing the accuracy of rumor detection compared to baseline models. Moreover, it greatly improves the efficiency of model training. [ABSTRACT FROM AUTHOR]

With the rapid spread of Internet information and the spread of fake news, the detection of fake news becomes more and more important. Traditional detection methods often rely on a single emotional or semantic feature to identify fake news, but these methods have limitations when dealing with news in specific domains. In order to solve the problem of weak feature correlation between data from different domains, a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed. This method makes full use of the attention mechanism, grasps the correlation between different features, and effectively improves the effect of feature fusion. The algorithm first extracts the semantic features of news text through the Bi-LSTM (Bidirectional Long Short-Term Memory) layer to capture the contextual relevance of news text. Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text. It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain. Finally, the fusion features are taken as the input of the fake news detection classifier, combined with the multi-task representation of information, and the MLP and Softmax functions are used for classification. The experimental results show that on the Chinese dataset Weibo21, the F1 value of this model is 0.958, 4.9% higher than that of the sub-optimal model; on the English dataset FakeNewsNet, the F1 value of the detection result of this model is 0.845, 1.8% higher than that of the sub-optimal model, which is advanced and feasible. [ABSTRACT FROM AUTHOR]

With the rapid growth and the popularization of the Internet, network security problems become increasingly serious. This paper analyzes the impact on several malicious codes on registry access behaviour and builds a rough set of algorithms-based registry access intrusion detection systems. Related attributes are constructed considering both the time sequence attribute and one-time access attribute as our input of the training module. Attribute reduction and rules extraction using rough sets algorithms give the registry access to normal behaviour modal. Experiments show that the system differentiates normal and abnormal registry behaviour successfully.

The stock market plays an important role in national economic development. Predicting its trend, to ensure market stability, efficiency, and safety, is one of the hottest topics of current research. Some algorithms have been proposed to use in this study, however, the drawback in forecasting accuracy and applicability of the model and other issues limit its application in practice. In this paper, we designed a forgotten-genetic network model to predict the stock index. Finally, a comparison between existing algorithms to verify the validity and applicability of the proposed method in this study.