China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.

In financial markets, accurate stock price movement prediction can significantly enhance investors' profits. However, the stock price is a highly complex dynamic system with considerable fluctuations, and the accuracy of direction prediction can be improved by selecting appropriate technical indicators. In this work, we propose a novel sparse support vector machines (SVMs) that combines recursive feature elimination (RFE) and ReliefF using a weight parameter. Unlike traditional RFE‐based SVMs, our approach constructs a nested feature subset structure, , using a new filter algorithm that combines backward sacrifice and ReliefF by weighting. This new filter algorithm can capture relevant features and feature interactions simultaneously and is crucial in preventing valuable features from being removed at each iteration. Moreover, the ReliefF algorithm combined with RFE can identify more discriminative feature subsets by reordering the features such that valuable ones are ranked higher than valueless ones, and removing valueless features sequentially through iterative processes. Our experimental results on nine stock datasets from the liquor and spirits concept demonstrate that our proposed method outperforms baseline sparse SVMs and SVM models in terms of accuracy and F‐test, while also producing a desirable number of features and automatically eliminating redundancy among technical indicators. We also show that on most stock datasets, the ReliefF algorithm combined with RFE can effectively identify discriminative feature subsets for cases of linear and Gaussian kernel SVMs and our proposed filter method can prevent valuable features from being removed at each iteration. In addition, our experimental findings reveal that feature subsets generated by technical indicators are more discriminative while feature subsets generated by technical indicators subsets mapped to a certain higher dimensional space are less discriminative.

Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important gene can more detrimental impact on predictions, and computation is prohibitive for exhaust search. We propose a modified memetic algorithm (MA) based on an improved splicing method to overcome the problems in the traditional genetic algorithm exploitation capability and dimension reduction in the predictor variables. The new algorithm accelerates the search in identifying the minimal best subset of genes by incorporating it into the new local search operator and hence improving the splicing method. The improvement is also due to another two novel aspects: (a) updating subsets of genes iteratively until the no more reduction in the loss function by splicing and increasing the probability of selecting the true subsets of genes; and (b) introducing add and del operators based on backward sacrifice into the splicing method to limit the size of gene subsets. Additionally, according to the experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms. Moreover, the mutation operator is replaced by it to enhance exploitation capability and initial individuals are improved by it to enhance efficiency of search. A dataset of the body weight of Hu sheep was used to evaluate the superiority of the modified MA against the genetic algorithm. According to our experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms including the most advanced adaptive best-subset selection algorithm.

This study aims to enhance the accuracy of pneumonia diagnosis from x-ray images by developing a model that integrates Vision Transformer (ViT) and Graph Convolutional Networks (GCN) for improved feature extraction and diagnostic performance. The ViT-GCN model was designed to leverage the strengths of both ViT, which captures global image information by dividing the image into fixed-size patches and processing them in sequence, and GCN, which captures node features and relationships through message passing and aggregation in graph data. A composite loss function combining multivariate cross-entropy, focal loss, and GHM loss was introduced to address dataset imbalance and improve training efficiency on small datasets. The ViT-GCN model demonstrated superior performance, achieving an accuracy of 91.43% on the COVID-19 chest x-ray database, surpassing existing models in diagnostic accuracy for pneumonia. The study highlights the effectiveness of combining ViT and GCN architectures in medical image diagnosis, particularly in addressing challenges related to small datasets. This approach can lead to more accurate and efficient pneumonia diagnoses, especially in resource-constrained settings where small datasets are common.

Parameter estimation for the skew-normal distribution is challenging, since the profile likelihood function of shape parameter has a stationary point at zero, which hampers the use of traditional methods, such as maximum likelihood method. We present a modified empirical characteristic function method to perform parameter estimation for the skew-normal distribution. The proposed approach is flexible and easy to implement. We show that the estimators converge to the true values in probability. The simulation study and data analysis suggest that the proposed method performs well, even for the case of small sample size.