Multi-Label Learning Based on Transfer Learning and Label Correlation

In recent years,multi-label learning has received a lot of attention.However,most of the existing methods only consider global label correlation or local label correlation.In fact,on the one hand,both global and local label correlations can appear in real-world situation at same time.On the other hand,we should not be limited to pairwise labels while ignoring the high-order label correlation.In this paper,we propose a novel and effective method called GLLCBN for multi-label learning.Firstly,we obtain the global label correlation by exploiting label semantic similarity.Then,we analyze the pairwise labels in the label space of the data set to acquire the local correlation.Next,we build the original version of the label dependency model by global and local label correlations.After that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent model.Finally,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label learning.The experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.

Personalized News Recommendation Based on the Text and Image Integration

The personalized news recommendation has been very popular in the news recommendation field.In most research,the picture information in the news is ignored,but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’reading interests than the one in the textual form.Therefore,in this paper,a model that combines images and texts in the news is proposed.In this model,the new tags are extracted from the images and texts in the news,and based on these new tags,an adaptive tag(AT)algorithm is proposed.The AT algorithm selects the tags the user is interested in based on the user feedback.In particular,the AT algorithm can predict tags that a user may be interested in with the help of the tag correlation graph without any user feedback.The proposed AT algorithm is verified by experiments.The experimental results verified the AT algorithm regarding three evaluation indexes F1-score(F1),area under curve(AUC)and mean reciprocal rank(MRR).The recommended effect of the proposed algorithm is found to be better than those of the various baseline algorithms on real-world datasets.

An Evidence Combination Method based on DBSCAN Clustering

Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple sources.However,the combination rules can be paradoxical when the evidence seriously conflict with each other.In the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density clustering.In the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict evidence.Then,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density clustering.Finally,we conduct the information fusion for the revised evidence sets by D-S combination rules.Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.

Perioperative psychological issues and nursing care among patients undergoing minimally invasive surgeries

Minimally invasive surgeries,including laparoscopic,endoscopic,and robotic surgeries,have gained great popularity and have gradually replaced conventional open surgeries.Commonly,patients may have perioperative psychological issues such as anxiety,depression,sleep disturbance,and delirium.A comprehensive literature review was conducted to identify how these psychological issues occur in minimally invasive surgeries and how nurses can take better care of patients to alleviate these issues.Only papers focusing on psychological issues during the perioperative period were included in the re-view,and preexisting issues before the setting of surgical treatment plan were not discussed.Compared to conventional surgeries,the incidence of postoperative anxiety,preoperative depression,and sleep disturbance is lower in minimally invasive surgeries,the incidence of postoperative depression may be higher with limited evidence,and the incidence of preoperative anxiety and delirium is inconclusive.Systematic perioperative nursing programs not only alleviate psychological issues,but also reduce postsurgical complications and accelerate recovery.However,special nursing programs to handle delirium are lacking.

Keyphrase Generation Based on Self-Attention Mechanism

Keyphrase greatly provides summarized and valuable information.This information can help us not only understand text semantics,but also organize and retrieve text content effectively.The task of automatically generating it has received considerable attention in recent decades.From the previous studies,we can see many workable solutions for obtaining keyphrases.One method is to divide the content to be summarized into multiple blocks of text,then we rank and select the most important content.The disadvantage of this method is that it cannot identify keyphrase that does not include in the text,let alone get the real semantic meaning hidden in the text.Another approach uses recurrent neural networks to generate keyphrases from the semantic aspects of the text,but the inherently sequential nature precludes parallelization within training examples,and distances have limitations on context dependencies.Previous works have demonstrated the benefits of the self-attention mechanism,which can learn global text dependency features and can be parallelized.Inspired by the above observation,we propose a keyphrase generation model,which is based entirely on the self-attention mechanism.It is an encoder-decoder model that can make up the above disadvantage effectively.In addition,we also consider the semantic similarity between keyphrases,and add semantic similarity processing module into the model.This proposed model,which is demonstrated by empirical analysis on five datasets,can achieve competitive performance compared to baseline methods.