This paper proposes a method called autoencoder with probabilistic LightGBM (AED-LGB) for detecting credit card frauds. This deep learning-based AED-LGB algorithm first extracts low-dimensional feature data from high-dimensional bank credit card feature data using the characteristics of an autoencoder which has a symmetrical network structure, enhancing the ability of feature representation learning. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. For this situation, the smote algorithm is used to resample the data before putting the extracted feature data into LightGBM, making the amount of fraud data and non-fraud data equal. After comparing the resampled and non-resampled data, it was found that the performance of the AED-LGB algorithm was not improved after resampling, and it was concluded that the AED-LGB algorithm is more suitable for imbalanced data. Finally, the AED-LGB algorithm is comparable with other commonly used machine learning algorithms, such as KNN and LightGBM, and it has an overall improvement of 2% in terms of the ACC index compared to LightGBM and KNN. When the threshold is set to 0.2, the MCC index of AED-LGB is 4% higher than that of the second-highest LightGBM algorithm and 30% higher than that of KNN. It shows that the AED-LGB algorithm has higher performance in accuracy, true positive rate, true negative rate, and Matthew’s correlation coefficient.

Accurate and robust power load forecasting is a cornerstone for efficent energy management and the sustainable integration of renewable energy. However, the practical application of current deep learning methods is hindered by two critical challenges: the rigidity of fixed-length prediction horizons and the difficulty in capturing the complex, heterogeneous temporal patterns found in real-world load data. To address these limitations, this paper proposes the multi-scale adaptive forecasting with multi-expert collaboration (MAFMC) framework. MAFMC’s primary contribution is a novel architecture that utilizes a collaborative ensemble of specialized expert predictors, enabling it to dynamically adapt to complex and non-linear load dynamics with superior accuracy. Furthermore, it introduces an innovative iterative learning strategy that allows for highly flexible, variable-length forecasting without the need for costly and time-consuming retraining. This capability significantly enhances operational efficiency in dynamic energy environments. Extensive evaluations on three benchmark datasets demonstrate that MAFMC achieves state-of-the-art performance, consistently outperforming leading baseline methods and establishing a new standard for power load forecasting.

Many-objective optimization problems (MaOPs) are challenging optimization problems in scientific research. Research has tended to focus on algorithms rather than algorithm frameworks. In this paper, we introduce a projection-based evolutionary algorithm, MOEA/PII. Applying the idea of dimension reduction and decomposition, it divides the objective space into projection plane and free dimension(s). The balance between convergence and diversity is maintained using a Bi-Elite queue. The MOEA/PII is not only an algorithm, but also an algorithm framework. We can choose a decomposition-based or dominance-based algorithm to be the free dimension algorithm. When it is an algorithm framework, it exhibits a better performance. We compare the performance of the algorithm and the algorithm with the MOEA/PII framework. The performance is evaluated by benchmark test instances DTLZ1-7 and WFG1-9 on 3, 5, 8, 10, and 15 objectives using IGD-metric and HV-metric. In addition, we investigated its superior performance on the wireless sensor networks deployment problem using C-metric. Moreover, determining objective domain for the objects of the wireless sensor networks deployment problem reduces the time and makes the solution set more responsive to user needs.

Accurate and reliable fault diagnosis is critical for secure operation in complex smart power systems. While graph neural networks show promise for this task, existing methods often neglect the long-tailed distribution inherent in real-world grid fault data and fail to provide reliability estimates for their decisions. To address these dual challenges, we propose a novel multi-expert collaboration uncertainty-aware power fault recognition framework with cross-view graph learning. Its core innovations are two synergistic modules: (1) The infographics aggregation module tackles the long-tail problem by learning robust graph-level representations. It employs an information-driven optimization loss within a contrastive graph architecture, explicitly preserving global invariance and local structural information across diverse (including rare) fault states. This ensures balanced representation learning for both the head and tail classes. (2) The multi-expert reliable decision module addresses prediction uncertainty. It trains individual expert classifiers using the Dirichlet distribution to explicitly model the credibility (uncertainty) of each expert’s decision. Crucially, a complementary collaboration rule based on evidence theory dynamically integrates these experts. This rule generates active weights for expert participation, prioritizing more certain experts and fusing their evidence to produce a final decision with a quantifiable reliability estimate. Collaboratively, these modules enable reliable diagnosis under data imbalance: The Infographics Module provides discriminative representations for all fault types, especially tail classes, while the Multi-Expert Module leverages these representations to make decisions with explicit uncertainty quantification. This synergy significantly improves both the accuracy and the reliability of fault recognition, particularly for rare or ambiguous grid conditions. Ultimately, extensive experiment evaluations on the four datasets reveal that the proposed method outperforms the state-of-the-art methods in the fault diagnosis of smart grids, in terms of accuracy, precision, f score and recall.