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With the continuous growth of international maritime trade, black carbon (BC) emissions from ships have caused great harm to the natural environment and human health. Controlling the BC emissions from ships is of positive significance for Earth’s environmental governance. In order to accelerate the development process of ship BC emission control technologies, this paper proposes a BC emission prediction model based on stacked generalization (SG). The meta learner of the prediction model is Ridge Regression (RR), and the base learner combines four models: Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), Random Forest (RF), and Support Vector Regression (SVR). We used mutual information (MI) to measure the correlation between combustion characteristic parameters (CCPs) and BC emission concentration, and selected them as the features of the prediction model. The results show that the CCPs have a strong correlation with the BC emission concentration of the diesel engine under different working conditions, which can be used to describe the influence of the changes to the combustion process in the cylinder on the BC generation. The introduction of the stacked generalization method reconciles the inherent bias of various models. Compared with traditional models, the fusion model has achieved higher prediction accuracy on the same datasets. The research results of this paper can provide a reference for the research and development of ship black carbon emission control technologies and the formulation of relevant regulations.

Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients’ cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure.

User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users’ feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers’ opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications.

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000−4000 cm−1) and Fourier transform mid-infrared (MIR: 4000−600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

One of the most crucial applications of radar-based precipitation nowcasting systems is the short-term forecast of extreme rainfall events such as flash floods and severe thunderstorms. While deep learning nowcasting models have recently shown to provide better overall skill than traditional echo extrapolation models, they suffer from conditional bias, sometimes reporting lower skill on extreme rain rates compared to Lagrangian persistence, due to excessive prediction smoothing. This work presents a novel method to improve deep learning prediction skills in particular for extreme rainfall regimes. The solution is based on model stacking, where a convolutional neural network is trained to combine an ensemble of deep learning models with orographic features, doubling the prediction skills with respect to the ensemble members and their average on extreme rain rates, and outperforming them on all rain regimes. The proposed architecture was applied on the recently released TAASRAD19 radar dataset: the initial ensemble was built by training four models with the same TrajGRU architecture over different rainfall thresholds on the first six years of the dataset, while the following three years of data were used for the stacked model. The stacked model can reach the same skill of Lagrangian persistence on extreme rain rates while retaining superior performance on lower rain regimes.

An accurate and efficient Large-for-Gestational-Age (LGA) classification system is developed to classify a fetus as LGA or non-LGA, which has the potential to assist paediatricians and experts in establishing a state-of-the-art LGA prognosis process. The performance of the proposed scheme is validated by using LGA dataset collected from the National Pre-Pregnancy and Examination Program of China (2010−2013). A master feature vector is created to establish primarily data pre-processing, which includes a features’ discretization process and the entertainment of missing values and data imbalance issues. A principal feature vector is formed using GridSearch-based Recursive Feature Elimination with Cross-Validation (RFECV) + Information Gain (IG) feature selection scheme followed by stacking to select, rank, and extract significant features from the LGA dataset. Based on the proposed scheme, different features subset are identified and provided to four different machine learning (ML) classifiers. The proposed GridSearch-based RFECV+IG feature selection scheme with stacking using SVM (linear kernel) best suits the said classification process followed by SVM (RBF kernel) and LR classifiers. The Decision Tree (DT) classifier is not suggested because of its low performance. The highest prediction precision, recall, accuracy, Area Under the Curve (AUC), specificity, and F1 scores of 0.92, 0.87, 0.92, 0.95, 0.95, and 0.89 are achieved with SVM (linear kernel) classifier using top ten principal features subset, which is, in fact higher than the baselines methods. Moreover, almost every classification scheme best performed with ten principal feature subsets. Therefore, the proposed scheme has the potential to establish an efficient LGA prognosis process using gestational parameters, which can assist paediatricians and experts to improve the health of a newborn using computer aided-diagnostic system.

Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients' cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure.