Zongwen Fan

The liver plays a crucial role in maintaining essential physiological functions; however, excessive alcohol consumption significantly increases the risk of liver disorders. Accurate and early prediction of such conditions is vital for timely intervention and effective clinical management. Nevertheless, liver disorder prediction is typically challenged by class imbalance, which makes the conventional fixed classification threshold (0.5) suboptimal for binary classification. To address these issues, this paper proposes an AI-driven two-stage feature fusion-based ensemble model for intelligent biomedical data processing and liver disorder prediction. In the first stage, multiple tree-based ensemble models are employed to evaluate feature importance, and a feature selection strategy is designed to select an optimal subset of discriminative features. In the second stage, prediction probabilities generated by these base learners are integrated with the selected feature subset to construct an enhanced feature space through feature-level information fusion. This probability-aware fusion strategy captures richer predictive information and alleviates the limitations of fixed-threshold binary classification. In addition, a meta-ensemble model is employed to aggregate heterogeneous predictive patterns from multiple learners for the final prediction based on an optimized threshold. Extensive experiments based on two benchmark liver disorder datasets demonstrate that the proposed model consistently outperforms the compared models in terms of predictive performance. Statistical analysis also confirms that the proposed model significantly outperforms the compared methods. These results indicate that the proposed model could serve as a useful AI-aided biomedical healthcare data processing tool for liver disorder prediction.

The liver performs numerous essential metabolic and regulatory functions; however, chronic alcohol consumption remains a major cause of hepatic disease. Consequently, the early and accurate prediction of alcohol-related liver disorders is of considerable clinical importance. A key challenge in this task is the class imbalance present in standard liver disorder datasets, which renders the default classification threshold of 0.5 suboptimal for minority-class prediction performance. To address this issue, we propose a swarm intelligence-enhanced framework that integrates the Butterfly Optimization Algorithm (BOA) with CatBoost. Unlike conventional approaches that either tune hyper-parameters alone or adjust classification thresholds as a post-processing step, the proposed method simultaneously optimizes both CatBoost hyper-parameters and the classification threshold. By incorporating threshold optimization into the BOA objective function, the model adaptively balances sensitivity and specificity without altering the original class distribution. Experiments conducted on two benchmark liver disorder datasets demonstrate that the proposed BOA-driven dual optimization consistently improves performance over baseline classifiers across multiple evaluation metrics. These results demonstrate that joint hyper-parameter and threshold optimization provides an effective and computationally efficient approach for imbalanced liver disorder prediction.

Human-object interaction detection methods usually adopt a multi-task framework, including a feature learning backbone and two sub-tasks: instance detection and interaction classification. These two sub-tasks shared the same image representation. However, different sub-task requires different image information. For instance detection, detector focused on the feature of local region while interaction predictor required the feature on bigger receptive field. To solve this problem, a new HOI detection framework is designed to select the appropriate representation for each sub-task. Specially, the local feature is learned to predict the center points of instances. For the interaction detection, Transformer is chosen to extract the context information to improve the accuracy. In the matching stage of human-object pair, the offsets from human and object to the interaction point are predicted to obtain more accurate interaction pair. Finally, the experimental results show that compared with the existing algorithms, the proposed method achieves the better performance.

Educational Data Mining (EDM) has achieved substantial gains in predictive performance, yet many existing approaches remain centered on single-objective optimization, most often accuracy. This does not adequately reflect the multi-dimensional nature of real-world educational decision-making, which requires balancing interpretability, fairness, robustness, efficiency, and timeliness. This perspective advocates a shift toward multi-objective, interpretable, and trustworthy EDM frameworks. We highlight the role of multi-objective optimization in modeling trade-offs through Pareto-optimal solutions and address the challenge of actionable decision-making through bargaining-based mechanisms, such as Nash bargaining, to select balanced and transparent outcomes. In addition, we discuss the value of fuzzy logic and adaptive methods for handling uncertainty and supporting interpretable reasoning in dynamic learning environments. Finally, we emphasize the importance of governance, accountability, and rigorous evaluation, and argue that emerging technologies should be assessed not only by performance gains but also by their practical and educational relevance. Overall, this perspective outlines a human-centered research agenda for the development of trustworthy, interpretable, and context-aware EDM systems.

The rapid digitalization of education has led to an explosive growth of multi-source and multi-modal learning data, providing new opportunities for advancing Educational Data Mining (EDM). By learning diverse data such as learning behaviors, assessment records, and interaction logs, EDM enables deeper insights into student learning processes and supports the development of personalized and intelligent education. However, several critical challenges remain, including the heterogeneity and fragmentation of multi-source data, the difficulty of extracting meaningful knowledge through effective data fusion, and the limited interpretability of high-performance predictive models. To address these challenges, this paper emphasizes the importance of integrating multi-source data fusion with explainable knowledge modeling. Multi-modal data fusion provides the basis for constructing comprehensive and coherent learner representations. The explainable approaches are essential for ensuring transparency, trust, and practical usability in educational settings. In particular, fuzzy knowledge representation, characterized by interpretable rules and membership functions, is important for bridging data-driven analytics and pedagogical reasoning. Fuzzy systems are the ideal candidates for explainable knowledge modeling as they combine human‑like linguistic reasoning with mathematically precise computation by allowing symbolic rules and numerical data in a transparent and interpretable way. By analyzing the key challenges and emerging directions in multi-source educational data mining, this editorial highlights the necessity of combining data integration, personalized learning, and interpretability. Such an approach enables more precise learning path design, targeted intervention, and trustworthy decision support, ultimately advancing the development of intelligent, explainable, and learner-centered educational systems.