With the rapid development of artificial intelligence, it is very important to find the pattern of the data from the observed data and the functional dependency relationship between the data. By finding the existing functional dependencies, we can classify and predict them. At present, cardiovascular disease has become a major disease harmful to human health. As a disease with high mortality, the prediction problem of cardiovascular disease is becoming more and more urgent. However, some computer methods are mainly used for disease detection rather than prediction. If the computer method can be used to predict cardiovascular disease in advance and treat it as early as possible, then the consequences of the disease can be reduced to a certain extent. Diseases can be predicted by mechanical methods. Support vector machine (SVM) has strict mathematical theory support, and can deal with nonlinear classification after using kernel techniques. Therefore, support vector machine can be used to predict cardiovascular disease. On the other hand, we also use logical regression and random forest to predict cardiovascular disease. This paper mainly uses the method of machine learning to predict whether the population is sick or not. First of all, we preprocess the obtained data to improve the quality of the data, and then use svm and logical regression to predict, so as to provide reference for the prevention and treatment of cardiovascular diseases.

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Underwater gliders are prevailing in oceanic observation nowadays for their flexible deployment and low cost. However, the limited onboard energy constrains their application, hence the motion pattern optimization and energy analysis are the key to maximizing the range of the glider while maintaining the acceptable navigation preciseness of the glider. In this work, a Multi-Objective Artificial Bee Colony (MOABC) algorithm is used to solve the constrained hybrid non-convex multi-objective optimization problem about range and accuracy of gliders in combination with specific glider dynamics models. The motion parameters Pareto front that balances the navigational index referring to range and preciseness are obtained, relevant gliding profile motion results are simulated simultaneously, and the results are compared with the conventional gliding patterns to examine the quality of the solution. Comparison shows that, with the utilization of the algorithm, glider voyage performance with respect to endurance and preciseness can be effectively improved.

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Pseudogenes were originally regarded as non-functional components scattered in the genome during evolution. Recent studies have shown that pseudogenes can be transcribed into long non-coding RNA and play a key role at multiple functional levels in different physiological and pathological processes. microRNAs (miRNAs) are a type of non-coding RNA, which plays important regulatory roles in cells. Numerous studies have shown that pseudogenes and miRNAs have interactions and form a ceRNA network with mRNA to regulate biological processes and involve diseases. Exploring the associations of pseudogenes and miRNAs will facilitate the clinical diagnosis of some diseases. Here, we propose a prediction model PMGAE (Pseudogene–MiRNA association prediction based on the Graph Auto-Encoder), which incorporates feature fusion, graph auto-encoder (GAE), and eXtreme Gradient Boosting (XGBoost). First, we calculated three types of similarities including Jaccard similarity, cosine similarity, and Pearson similarity between nodes based on the biological characteristics of pseudogenes and miRNAs. Subsequently, we fused the above similarities to construct a similarity profile as the initial representation features for nodes. Then, we aggregated the similarity profiles and associations of nodes to obtain the low-dimensional representation vector of nodes through a GAE. In the last step, we fed these representation vectors into an XGBoost classifier to predict new pseudogene–miRNA associations (PMAs). The results of five-fold cross validation show that PMGAE achieves a mean AUC of 0.8634 and mean AUPR of 0.8966. Case studies further substantiated the reliability of PMGAE for mining PMAs and the study of endogenous RNA networks in relation to diseases.