prediction

摘要： Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal financial features, especially the stock price history. However, there are many outside-of-company features that deeply interact with the companies’ stock price performance, especially during the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features over the past 20 months. We added handcrafted external information, including COVID-related statistics and company-specific vaccine progress information. We implemented, evaluated, and compared several machine learning models, including Multilayer Perceptron Neural Networks with logistic regression and decision trees with boosting and bagging algorithms. The results suggest that the application of feature engineering and data mining techniques can effectively enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related handcrafted features help to increase the model prediction accuracy by 7.3% and AUROC by 6.5% on average. Further exploration showed that with data selection the decision tree model with gradient, boosting algorithm achieved 70% in AUROC and 66% in the accuracy.

摘要： China’s 2022 government work report sets the growth target for the coming year at 5.5 percent, a modest growth for China compared with last year’s 8.1 percent, but not a bad prediction considering the stormy weather awaiting the world in the course of this year.

摘要： BACKGROUND Factors that are associated with the short-term rehospitalization have been investigated previously in numerous studies.However,the majority of these studies have not produced any conclusive results because of their smaller sample sizes,differences in the definition of pneumonia,joint pooling of the in-hospital and post-discharge deaths and lower generalizability.AIM To estimate the effect of various risk factors on the rate of hospital readmissions in patients with pneumonia.METHODS Systematic search was conducted in PubMed Central,EMBASE,MEDLINE,Cochrane library,ScienceDirect and Google Scholar databases and search engines from inception until July 2021.We used the Newcastle Ottawa(NO)scale to assess the quality of published studies.A meta-analysis was carried out with random-effects model and reported pooled odds ratio(OR)with 95%confidence interval(CI).RESULTS In total,17 studies with over 3 million participants were included.Majority of the studies had good to satisfactory quality as per NO scale.Male gender(pooled OR=1.22;95%CI:1.16-1.27),cancer(pooled OR=1.94;95%CI:1.61-2.34),heart failure(pooled OR=1.28;95%CI:1.20-1.37),chronic respiratory disease(pooled OR=1.37;95%CI:1.19-1.58),chronic kidney disease(pooled OR=1.38;95%CI:1.23- 1.54) and diabetes mellitus (pooled OR = 1.18;95%CI: 1.08-1.28) had statistically significantassociation with the hospital readmission rate among pneumonia patients. Sensitivity analysisshowed that there was no significant variation in the magnitude or direction of outcome,indicating lack of influence of a single study on the overall pooled estimate.CONCLUSIONMale gender and specific chronic comorbid conditions were found to be significant risk factors forhospital readmission among pneumonia patients. These results may allow clinicians and policymakersto develop better intervention strategies for the patients.

摘要： The routing protocols are paramount to guarantee the Quality of Service(QoS)for Flying Ad Hoc Networks(FANETs).However,they still face several challenges owing to high mobility and dynamic topology.This paper mainly focuses on the adaptive routing protocol and proposes a Three Dimensional Q-Learning(3DQ)based routing protocol to guarantee the packet delivery ratio and improve the QoS.In 3DQ routing,we propose a Q-Learning based routing decision scheme,which contains a link-state prediction module and routing decision module.The link-state prediction module allows each Unmanned Aerial Vehicle(UAV)to predict the link-state of Neighboring UAVs(NUs),considering their Three Dimensional mobility and packet arrival.Then,UAV can produce routing decisions with the help of the routing decision module considering the link-state.We evaluate the various performance of 3DQ routing,and simulation results demonstrate that 3DQ can improve packet delivery ratio,goodput and delay of baseline protocol at most 71.36%,89.32%and 83.54%in FANETs over a variety of communication scenarios.

摘要： Dear editor,This letter presents a reciprocal alternative to model predictive control(MPC),called model controlled prediction.More specifically,in order to integrate dynamic control signals into the transportation prediction models,a new fundamental theory of machine learning based prediction models is proposed.The model can not only learn potential patterns from historical data,but also make optimal predictions based on dynamic external control signals.

摘要： Dear Editor,This letter is concerned with human parsing based on part-wise semantic prediction.Human body can be regarded as a whole structure composed of different semantic parts,and the mainstream single human parser uses semantic segmentation pipeline to solve this problem.

摘要： BACKGROUND Complete response after neoadjuvant chemotherapy(r NACT) elevates the surgical outcomes of patients with breast cancer, however, non-r NACT have a higher risk of death and recurrence.AIM To establish novel machine learning(ML)-based predictive models for predicting probability of r NACT in breast cancer patients who intends to receive NACT.METHODS A retrospective analysis of 487 breast cancer patients who underwent mastectomy or breast-conserving surgery and axillary lymph node dissection following neoadjuvant chemotherapy at the Hubei Cancer Hospital between January 1, 2013, and October 1, 2021. The study cohort was divided into internal training and testing datasets in a 70:30 ratio for further analysis. A total of twenty-four variables were included to develop predictive models for r NACT by multiple MLbased algorithms. A feature selection approach was used to identify optimal predictive factors. These models were evaluated by the receiver operating characteristic(ROC) curve for predictive performance.RESULTS Analysis identified several significant differences between the r NACT and nonr NACT groups, including total cholesterol, low-density lipoprotein, neutrophilto-lymphocyte ratio, body mass index, platelet count, albumin-to-globulin ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. The areas under the curve of the six models ranged from 0.81 to 0.96. Some ML-based models performed better than models using conventional statistical methods in both ROC curves. The support vector machine(SVM) model with twelve variables introduced was identified as the best predictive model.CONCLUSION By incorporating retreatment serum lipids and serum inflammation markers, it is feasible to develop ML-based models for the preoperative prediction of r NACT and therefore facilitate the choice of treatment, particularly the SVM, which can improve the prediction of r NACT in patients with breast cancer.

摘要： Nowadays, machine learning is growing fast to be more popular in the world, especially in the healthcare field. Heart diseases are one of the most fatal diseases, and an early prediction of such disease is a vital task for many medical professionals to save their patient’s life. The main contribution of this research is to provide a comparative analysis of different machine learning models to reach the most supporting decision for diagnosing heart disease with better accuracy as compared to existing models. Five models namely, K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boost (XGB), have been introduced for this purpose. Their performance has been tested and compared considering different metrics for precise evaluation. The comparative study has proven that the XGB is the most suitable model due to its superior prediction capability to other models with an accuracy of 91.6% and 100% on two different heart ailments datasets, respectively. Both datasets were acquired from the heart diseases repositories where dataset_1 was taken from the University of California, Irvine (UCI) and dataset_2 was from Kaggle.

摘要： In this study,we assess the prediction for May rainfall over southern China(SC)by using the NCEP CFSv2 outputs.Results show that the CFSv2 is able to depict the climatology of May rainfall and associated circulations.However,the model has a poor skill in predicting interannual variation due to its poor performance in capturing related anomalous circulations.In observation,the above-normal SC rainfall is associated with two anomalous anticyclones over the western tropical Pacific and northeastern China,respectively,with a low-pressure convergence in between.In the CFSv2,however,the anomalous circulations exhibit the patterns in response to the El Ni?o-Southern Oscillation(ENSO),demonstrating that the model overestimates the relationship between May SC rainfall and ENSO.Because of the onset of the South China Sea monsoon,the atmospheric circulation in May over SC is more complex,so the prediction for May SC rainfall is more challenging.In this study,we establish a dynamic-statistical forecast model for May SC rainfall based on the relationship between the interannual variation of rainfall and large-scale ocean-atmosphere variables in the CFSv2.The sea surface temperature anomalies(SSTAs)in the northeastern Pacific and the centraleastern equatorial Pacific,and the 500-h Pa geopotential height anomalies over western Siberia in previous April,which exert great influence on the SC rainfall in May,are chosen as predictors.Furthermore,multiple linear regression is employed between the predictors obtained from the CFSv2 and observed May SC rainfall.Both cross validation and independent test show that the hybrid model significantly improve the model’s skill in predicting the interannual variation of May SC rainfall by two months in advance.

摘要： Low visibility condition hinders both air traffic and road traffic operations. Accurate forecasting of visibility condition helps aircraft operators and travelers to make better decisions and improve their safety. It is, therefore, essential to investigate and identify the predictor variables that could influence and help predict visibility. The objective of this study is to identify the predictor variables that influence visibility. Four years of surface weather observations, from January 2011 to December 2014, were collected from the weather stations located in and around the state of North Carolina, USA for the model development. Ordinary least squares (OLS) and weighted least squares (WLS) regression models were developed for different visibility and elevation ranges. The results indicate that elevation, cloud cover, and precipitation are negatively associated with the visibility in visibility less than 15,000 m model. The elevation, cloud cover and the presence of water bodies within the vicinity play an important role in the visibility less than 2000 m model. The chances of low visibility condition are higher between six to twelve hours after the rainfall when compared to the first six hours after the rainfall. The results from this study help to understand the influence of predictor variables that should be dealt with to improve the traffic operations and safety concerning the visibility near the airports/road transportation network.

摘要： Prediction is an important problem in many fields. For the prediction of a given variable with a sequence of observations, we propose a new method based on the use of Taylor's formula. The method can besummarized as follows：Firstly, for a sequence of discrete observations which may contain noise, we usetracking-differentiators to filter the noise or smooth the observation results, and obtain the estimation of theirdifferential values. Secondly, for the prediction, we use Taylor's formula to predict the future evolution of theobservations. Theoretical estimation is provided to guarantee the precision of the relevant prediction results.And simulation results for certain kinds of observations indicate the effectiveness of the method.

摘要： In this study, the artificial neural network(ANN) and the multiple regression (MLR) model havebeen developed to predict the weight unevenness ofcotton yarn. The developed models were assessed byverifying correlation coefficient (R), mean squareerror (MSE), and mean absolute error (MAE) of testdata prediction. The results indicated that the ANNmode has better performance in comparison with thelinear regression model. The correlation coefficient ofthe ANN model is greater than that of linearregression model, whereas the MSE, MAE andMaximum error of ANN model are all lower than thatof the multiple regression model. Thus, the ANNmodel was found to be more accurate than MLRmodel and the prediction errors of ANN model waslow. Therefore, it is concluded that the ANN modelswere effectively predicting the.