The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. Multiple evaluation metrics, including the specificity, sensitivity, negative predictive value, positive predictive value, accuracy, Matthews correlation coefficient, and the Youden index, were used to assess the model’s performance. The stacked ensemble model integrated the strengths of the nine base models and consistently outperformed individual base models in classifying GTV (area under the curve [AUC] = 0.928), brainstem (AUC = 0.932), and normal brain tissue (AUC = 0.942). Among the base models, the support vector machine model demonstrated the best performance in the three classifications (AUC = 0.922, 0.909, and 0.928). The higher performance of the stacked ensemble model highlighted the low performance of other models, including the decision tree (AUC = 0.709, 0.706, 0.804) and k-nearest neighbors (AUC = 0.721, 0.663, 0.729) models in certain contexts, such as when faced with high-dimensional feature spaces. While machine learning shows significant promise in medical image analysis, relying solely on a single model may lead to suboptimal results. By combining the strengths of various algorithms, the stacking ensemble model offers a better solution for the classification of brain metastases based on radiomic features.

Accurate 3D whole heart segmentation provides more details of the morphological and pathological information that could help doctors with more effective patient-specific treatments. 3D CNN network has been recognized as an important role in accurate volumetric segmentation. Typically, 3D CNN network has a large number of parameters as well as the floating point operations (FLOPs), which leads to heavy and complex computation. In this paper, we introduce an efficient 3D network (Ghost-Light-3DNet) for heart segmentation. Our solution is characterized by two key components: First, inspired by GhostNet in 2D, we extend the Ghost module to 3D which can generate more feature maps from cheap operations. Second, a sequential separable conv with residual module is applied as a light plug-and-play component to further reduce network parameters and FLOPs. For evaluation, the proposed method is validated on the MM-WHS heart segmentation Challenge 2017 datasets. Compared to state-of-the-art solution using 3D UNet-like architecture, our Ghost-Light-3DNet achieves comparable segmentation accuracy with the 2. 18x fewer parameters and 4. 48x less FLOPs, respectively.

Assessing the public opinion on food safety events constitutes an important job of government regulators. To optimize the government’s management of food safety affairs, a promising way is to use artificial intelligence to improve the efficiency of food safety public opinion assessment. In this paper, we model the assessment of public opinion influence as a text classification task. The whole model adopts the ensemble learning framework, and it integrates naive Bayes, support vector machine, extreme gradient boosting, convolutional neural network, long- and short-term memory network, FastText, and BERT classification methods into the framework to form an ensemble learner. The ensemble learner is able to classify textual public opinion into high, medium, and low influence levels by learning from the samples assessed by human experts. To overcome the problem of unbalanced samples, we propose a sample generation method consisting of synonym replacement and semantic filtering to increase the number of high-influence samples. Real public opinion data collected from the Food Safety Department of the Chinese government are used for experiment. Extensive comparison of the proposed method with baseline methods proves the effectiveness of the ensemble learner and the sample generation steps.

Epidemics spread through shipping networks and have dual characteristics as both biological sources of infection and triggers of cascading failures. However, existing resilience models fail to capture this dual and coupled dynamics. To minimize the cascading impacts of epidemics on global shipping networks, this paper proposes an innovative resilience assessment framework that considers the interaction between epidemic transmission and the shipping network cascading failure. First, a weighted shipping network topology is constructed based on route flow characteristics to quantify route frequency, stopping time, and the number of infected people, and the epidemic transmission across ports is modeled with an improved SEIR model, which contains a heterogeneous infectivity function and a dynamic transmission matrix, revealing a dual transmission mechanism inside and outside the ports. Second, a two-stage cascading failure model is developed: a direct failure triggered by infected people exceeding the threshold and an indirect failure triggered by the dynamic redistribution of loads. The load redistribution strategy is optimized to reconcile the residual port capacity and the risk of infection. Finally, a multidimensional resilience assessment framework covering structural destruction resistance, network efficiency, path redundancy, and a cascading failure propagation rate is constructed. Example validation shows that the improved load redistribution strategy reduces the maximum connected subgraph decay rate by 68.2%, reduces the cascading failure rate by 88%, and improves the peak network efficiency by 128.2%. In case of multi-source epidemics, the state of the network collapse can be shortened by 12 days if the following recovery strategy is adopted: initially repair high connectivity hubs (e.g., Port of Shanghai), and then repair high centrality nodes (e.g., Antwerp Port) to achieve a balance between recovery efficiency and network functionality. The research results reduce the risk of systemic disruptions in maritime networks and provide decision-making tools for dynamic port scheduling during pandemics.

Accurate prediction of tuna distribution is essential for sustainable fisheries management. This study develops a two-stage hybrid model combining Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Random Forest (RF) to predict tuna distribution around drifting fish aggregating devices (DFAD) in the Western and Central Pacific Ocean (WCPO). Echo-sounder buoy data from DFAD were aggregated into 2° × 2° grid cells and matched with oceanographic variables from the Copernicus Marine Service. Random Forest-based variable importance analysis identified primary productivity (27%), chlorophyll-a (22%), and dissolved oxygen (18%) as the three dominant environmental drivers. The CNN-RNN component extracts spatiotemporal features from multi-layer ocean data, while the RF classifier performs binary classification of tuna aggregation zones (high-yield vs. low-yield). All five models (Decision Tree, RF, CNN, Transformer, and CNN-RNN-RF) were evaluated on 557 samples using 5-fold stratified cross-validation, with each fold further split 80:20 for training and validation. The proposed CNN-RNN-RF model achieved the highest performance with an AUC of 0.830, accuracy of 82.6%, and F1-scores of 86.3% (high-yield) and 76.2% (low-yield), outperforming the best baseline model (RF: AUC 0.761, accuracy 75.4%). Predicted high-yield zones showed strong consistency with fishing log records, demonstrating the potential of integrating echo-sounder data with hybrid deep learning for data-driven tuna fisheries management.