Forecasting flight fares is a critical task in the rapidly expanding civil aviation industry and involves numerous factors. However, traditional airfare prediction systems are ineffective due to the complex and nonlinear relationships of multiple factors, which are not able to accurately account for the impact of different attributes such as time period. To tackle these issues, in this study, we proposed a novel approach that utilizes a deep-learning model, specifically, the Gated Recurrent Unit (GRU), by incorporating 44 decision features. The proposed model is able to capture the intricate relationships between various factors effectively and predict air ticket prices with high accuracy. In the experiments, it was found that the GRU model significantly outperforms not only classic machine learning models but also the MLP and LSTM in terms of assessment indicators of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The GRU model is thus promising concerning the fare prediction of flight tickets.

More than three years have passed since the COVID-19 outbreak. The end of the pandemic is in sight, and even some countries have declared COVID-19 as endemic. During the pandemic, COVID-19 vaccination is essential to controlling the situation in many countries. Monitoring opinions on social media is important to understand the public reception of the COVID-19 vaccine. In this study, we combine LDA with agglomerative clustering to investigate the reaction of people from various countries during different periods for approximately one year after a few countries officially approved vaccination. Agglomerative clustering is performed on the latent topics based on the Hellinger distance for measuring the distance between the distributions of latent topic. As a result, the proposed methods can show relative closeness among the topics and visualize the topic changes over time. The analysis shows that the major focuses of tweets in the four phases with an interval of three months are the effectiveness of the vaccine, vaccine booster, the Delta variant, and vaccine for children, which coincide with the timeline of the critical COVID events as announced by WHO.

This paper presents an advanced method for forecasting flight fares that combines aspect-based sentiment analysis (ABSA) with deep learning techniques, particularly the gated recurrent unit (GRU) model. This approach leverages historical airline ticket transaction data and customer reviews to better understand airline fare dynamics and the impact of customer sentiments on pricing. The aspect analysis extracts key service aspects from customer feedback and provides insightful correlations with airfare. These were further categorized into nine groups for sensitivity analysis, which offered a deeper understanding of how each group influences customers’ attitudes. This ABSA-driven forecasting method marks a departure from traditional models by utilizing sentiment data alongside airline transaction data to improve the predictive accuracy. Its effectiveness is demonstrated through metrics including a root mean square error (RMSE) of 0.0071, a mean absolute error (MAE) of 0.0137, and a coefficient of determination (R²) of 0.9899. Additionally, this model shows strong prediction performance in both short- and long-term fare predictions. It not only advances airfare forecasting methods but provides valuable insights for decision makers of airline industry to refine the pricing strategies or make improvements when it is indicated some services require further attention.

Traditionally, temperature-salinity (T-S) relationship was analysed to indicate the characteristic of water mass, and prediction models based on regression may be built to estimate the salinity in earlier researches. Temperature-salinity characteristic however might change dynamically with respect to the geographic location, season, or water layer, and is quite sensitive to the depth for the same location. It is therefore of interest whether including depth into the regression model could help to improve the prediction accuracy. In this paper, multivariate nonlinear regression is investigated to predict the salinity according to both temperature and depth. Experimental results show that depth is very effective for improving the prediction accuracy, and season-dependent model may achieve better performance than season-independent model. In addition, when the analysis was conducted for 5-year range, it is found the prediction accuracy is significantly higher than the result for all years, which indicates there might exist long-term variation on the characteristics of the water masses. Furthermore, 3D model and visualization scheme were proposed to explore the effect of depth on the temperature-salinity-depth characteristic, and a visualization system was built accordingly. This system may present the T-S curve and 3D Model according to the assigned criteria of season or multi-year range, and allows the user to view the similarity map for the given T-S-D data so as to conduct comparative study of water masses for a wide area of ocean.

Due to the dynamic of stock markets, predicting stock price trends remains a massive challenge when utilizing machine learning models, especially in Bayesian models. Indeed, the distributions of input features extracted from stock datasets are seldom normal, making Gaussian density less discriminative. This paper introduces a novel predictive model, the Bayesian Classifier with Heuristic Likelihood Estimation (BC-HLE), in which heuristic likelihood estimation instead of the Gaussian density was utilized to release the normality assumption in the conventional naïve Bayes classifier. Our model leverages the concept of the p -value, which evaluates how close the testing value is to the expected value on a distribution. This approach yields a more accurate likelihood estimate without normality assumption. When tested on 55 stock datasets from the S&P500 index, the proposed BC-HLE model outperformed conventional Gaussian classifiers and such machine learning models as support vector machines and multi-layer perceptron, regarding prediction accuracy. Additionally, its superiority was verified on the returns on investment when the stock trend prediction was applied to a simulated trading system. The experimental outcomes show that the proposed model is a reliable enhancement of Bayesian classifiers and can contribute to decision for the stock market investment.

State-of-the-art models of semantic segmentation, based on global convolutional network, ASPP, self-attention, and so on, focus on capturing context information through the fusion of multi-scale features and integrating local features with large kernels. However, these models have not been compared yet in parallel to interpret their relative efficacies. This makes it difficult to further combine or improve these models due to their complicated network structures. In this paper, a general multi-scale framework of semantic image segmentation was proposed to investigate and compare the network structures of the models in parallel. Three alternative modules were proposed to improve these methods, and the experiments show the proposed modules can give superior segmentation results and achieve outstanding performance on Pascal VOC2012 images segmentation datasets. Additionally, this framework was shown to be flexible for integrating the multi-scale features and operations from different levels. Experimental results show that the low-level operation can extract local details and the high-level operation the overall contour, so the output features from different levels complement each other to improve the performance effectively.