Diabetes mellitus is a common chronic metabolic disease for which early diagnosis is crucial for prevention and treatment. As the amount of structured clinical information grows, machine learning has emerged as a valuable instrument to predict diabetes risk; nevertheless, several studies demonstrate the application of machine learning models to diabetes risk prediction based on original clinical features, while highlighting a general lack of systematic inspection of feature combinations and model interpretability. Various machine learning models have been built and tested in this research to predict diabetes risk using structured clinical data. Clinically motivated interaction terms were built to capture nonlinear physiological relationships, and a two-criterion selection approach using tree-based split gain and SHAP importance was used to identify meaningful interactions. An interaction-enhanced XGBoost model was then trained and compared with baseline and complete-interaction models using conventional classification metrics. The results of the experiment indicate that the noise created by indiscriminate inclusion of interaction features can lead to degraded generalization performance and that selectively retained interactions can increase sensitivity without compromising the discriminative performance. Glucose, BMI, and age were also identified as dominant predictors and verified in diabetes prediction through feature ablation analysis. Furthermore, the SHAP interpretability analysis presented clear and clinically coherent model behavior explanations. Overall, the developed framework implies that there is a sensible trade-off between predictive efficiency and interpretability, which highlights the importance of focused feature interaction modeling of reliable predictive and explainable diabetes risk assessments.

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The development of image recognition reflects the evolution of the technology paradigm. This paper systematically combs the evolution of traditional statistical models and deep learning models, analyzes the core technologies of the fusion path such as Bayesian neural network and deep integration, and discusses its application value in high-risk scenarios including medical image diagnosis, automatic driving and industrial defect detection. It also points out key challenges like computational efficiency, unified evaluation standards and model calibration. The study focuses on solving the overconfidence and lack of explicability of deterministic deep learning models, and finds that fusing AI with statistical models to realize "probability prediction" is an effective solution. Different fusion technologies have their own advantages and need scenario-based trade-offs. The research provides theoretical reference for constructing trustworthy image recognition systems and points out future directions such as efficient algorithm development and cross-research with interpretability AI.

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Time series forecasting is a core technology in critical domains such as energy dispatch and traffic management, yet its performance is hindered by challenges including long-term dependencies, multi-scale structures, and data non-stationarity. In recent years, integrating spectral analysis with deep learning has emerged as a significant trend for improving forecasting accuracy and efficiency. This paper systematically reviews progress in this field by introducing a challenge-oriented classification framework encompassing four dimensions: long-term dependency modeling, multi-scale feature extraction, lightweight design, and multi-task general-purpose modeling. Within this framework, we conduct a comparative analysis of representative methods, including Autoformer, FEDformer, and TimesNet, among others. These methods enhance modeling capacity for complex temporal patterns through mechanisms such as spectral sparsification, adaptive frequency filtering, and temporal multi-periodic transformations. We evaluate methods on eight mainstream benchmark datasets across multiple forecast horizons (96–720 steps). Results demonstrate that spectral sparsification and memory filtering mitigate error accumulation in long-term forecasting; multi-scale decomposition structures balance short-term fluctuations and long-term trends; and lightweight linear models achieve superior parameter efficiency on high-dimensional stationary data. By synthesizing technical pathways and scenario-based comparisons, this study offers practical guidance for model selection in engineering applications. Finally, we outline future research directions, including time-varying period detection and joint time-frequency representation, to enhance the robustness of forecasting models in non-stationary environments and facilitate their real-world deployment.

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The Grey Wolf Optimizer (GWO) has been extensively applied in meta-heuristic optimization. However, it inherently suffers from several limitations, including inaccurate solution outputs, slow convergence speed, and a high tendency to get trapped in local optima. To resolve these problems, this study introduces two targeted improvements to the GWO algorithm. Firstly, an elite opposition-based learning method is employed for initializing the grey wolf population. This method enhances the diversity of initial individuals, reinforces the algorithm’s global search ability, and accelerates convergence in the early iteration stage. Secondly, nonlinear parameters are incorporated into both the prey encircling and attacking processes of GWO. This modification expands the algorithm’s search scope during the early iterations. Ten benchmark test functions with distinct characteristics were used to validate the improved algorithm (IEN-GWO), which was compared with five well-recognized meta-heuristic algorithms. The experimental results demonstrate that IEN-GWO outperforms the compared algorithms in terms of solution precision, stability, and convergence rate.

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